Znajdź zawartość

Free Download Working with Data in Amazon Redshift Released 10/2024 By Saravanan Dhandapani MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English | Duration: 32m | Size: 100 MB Effective querying and backing up data is important for a successful cloud data warehouse solution. This course will teach you how to analyze your query for optimal performance and quickly restore data from your backups The consistent availability of your cloud data warehouse is important to derive maximum value for your organization. In this course, Working with Data in Amazon Redshift, you'll learn to ensure continuous availability of your data warehouse. First, you'll explore how to analyze and tune your queries for optimal performance. Next, you'll discover how to back up and restore your data using snapshots. Finally, you'll learn how to perform database migrations by minimizing downtime. When you're finished with this course, you'll have the skills and knowledge to work with your data needed to ensure a highly available data warehouse solution. Homepage https://app.pluralsight.com/library/courses/amazon-redshift-working-with-data/table-of-contents Screenshot Rapidgator https://rg.to/file/16861dbcea5900fc89419920dfd51243/sxfcg.Working.with.Data.in.Amazon.Redshift.rar.html Fikper Free Download https://fikper.com/sxZkmCu7cA/sxfcg.Working.with.Data.in.Amazon.Redshift.rar.html No Password - Links are Interchangeable

Free Download Web Scraping APIs for Data Science 2021 - PostgreSQL+Excel Last updated 2/2023 Created by Dr. Alexander Schlee MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 24 Lectures ( 4h 25m ) | Size: 2.22 GB From Beginner to Advanced | 4 Hands-On Projects What you'll learn web scraping data extraction data mining create your own dataset output data in Excel output your dataframe in PostgreSQL run SQL commands on your dataframe Requirements basic understanding of Python programming Description In this course the students will get to know how to scrape data from the API of a website (if available). We start with the fundamentals and the beginner level project. After that, two different projects will be covered, followed by the advanced project. After scraping data of wach project, the results will be stored inside an Excel file. Within the advanced level project we will create two dofferent datasets with 5000 results each. The goal is to merge both dataframes (total: 10000 results), save it in Excel and output the data in the PostgreSQL database and run SQL commands on our own data.The requirement for this course is basic knowledge of Python Programming. Since we will not cover very difficult Python topics you do not have to be a professional. The most important characteristic is that you are curious about Web Scraping and Data Mining. You should be ready to invest time in gaining the knowledge which is taught in this course.After this course you will have the knowledge and the experience to scrape your own data and create your own dataset. With the help of the course resources you will always have documents you can refer to. If you have a question or if a concept just does not make sense to you, you can ask your questions anytime inside the Q&A - Forum. Either the instructor or other students will answer your question. Thanks to the community you will never have the feeling to learn alone by yourself.Disclaimer : I teach web scraping as a tutor for educational purposes. That's it. The first rule of scraping the web is: do not harm a certain website. The second rule of web crawling is: do NOT harm a certain website. Who this course is for Data Enthusiasts who want to create their own datasets Homepage https://www.udemy.com/course/web-scraping-apis-for-data-science-2021/ Screenshot TakeFile https://takefile.link/7mgui09x6890/wermw.Data.Science.2021..PostgreSQLExcel.rar.html Rapidgator https://rg.to/file/cbadcce3f81aa59180993d77933531a3/wermw.Data.Science.2021..PostgreSQLExcel.rar.html Fikper Free Download https://fikper.com/0Qm7rzvGER/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part2.rar.html https://fikper.com/7lA8et4IFq/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part1.rar.html https://fikper.com/Ei8ue4zEvK/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part3.rar.html https://fikper.com/UTDaWfqO84/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part3.rar.html https://fikper.com/ZohTjua3N7/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part1.rar.html https://fikper.com/oDLDbgjv97/wermw.Web.Scraping.APIs.for.Data.Science.2021..PostgreSQLExcel.part2.rar.html https://fikper.com/ps3WIqEP9Y/wermw.Data.Science.2021..PostgreSQLExcel.rar.html No Password - Links are Interchangeable

Free Download Unlock CCT Data Center (010-151 DCTECH) Essential Training Published 10/2024 Created by Muhammad Muheeb MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 20 Lectures ( 2h 19m ) | Size: 1.58 GB CCT Data Center Technician Mastery: Build, Manage, and Secure Data Centers, From Fundamentals to Advanced Operations. What you'll learn Understanding the purpose, scope, and career benefits of earning the CCT Data Center certification. The core concepts behind data centers, their role in IT infrastructure, and the key components involved. A deep dive into how data center networks are structured, including typical topologies and networking layers. An understanding of essential networking protocols like TCP/IP and Ethernet, and their functions within a data center. Introduction to VLANs, IP addressing strategies, and subnetting, crucial for efficient data center network management. How data centers ensure reliability through redundancy and high availability mechanisms. Insight into key hardware components, including servers, storage systems (SAN, NAS), and networking devices. Importance of managing power supply and cooling systems to maintain optimal data center performance. Best practices for managing the operations of a data center to ensure efficiency and uptime. Key tools and techniques for monitoring data centers and troubleshooting common issues. Essential security practices, including both physical and network security measures. How virtualization technology and cloud computing are integrated into modern data centers to optimize resources. The role of automation tools and orchestration systems in streamlining data center operations. Knowledge of different types of cabling, such as fiber optics and copper, that support data center connectivity. Fundamentals of designing scalable, efficient, and future-proof data centers. and much more Requirements Willingness or Interest to learn about CCT Data Center and Preparation for the 010-151 DCTECH Exam for Success. Description IMPORTANT NOTICE BEFORE YOU ENROLL:This course is not a replacement for the official materials you need for the certification exams. It is not endorsed by the certification vendor. You will not receive official study materials or an exam voucher as part of this course.This CCT Data Center course is designed to provide a comprehensive understanding of data center infrastructure, networking, and operations. Whether you're a networking professional, IT specialist, or aspiring data center technician, this course will equip you with the knowledge and skills necessary to excel in the data center field and prepare for the CCT Data Center Certification.CCT Data Center (Cisco Certified Technician for Data Center) is a certification offered by Cisco Systems designed for individuals who support and maintain Cisco data center infrastructure. This certification focuses on the skills required to diagnose, restore, repair, and replace critical Cisco networking and data center equipment. It is typically aimed at field technicians or engineers responsible for maintaining the physical aspects of data centers.The course begins with an introduction to the CCT Data Center Certification, exploring its purpose, career benefits, and the growing demand for data center expertise in modern IT environments. You'll learn about the fundamentals of data centers, their critical role in housing, processing, and managing large amounts of data, and how they support businesses and organizations in the digital age.Next, the course covers the key components of data centers, from the physical hardware like servers, networking devices, and storage systems to the logical elements that make up data center networks. You'll dive deep into data center network architecture, understanding common topologies, networking layers, and protocols such as TCP/IP and Ethernet, essential for efficient data center operation.You'll also learn about VLANs, subnetting, and IP addressing, essential for managing data center networks, as well as the importance of redundancy and high availability in ensuring network reliability. Additionally, the course offers a detailed examination of the critical hardware components used in data centers, including servers, storage systems like SAN (Storage Area Network) and NAS (Network Attached Storage), and the importance of proper cooling and power management to maintain optimal performance.Data center operations management is a core part of the curriculum, covering best practices for maintaining efficiency and uptime in data center environments. You'll explore monitoring tools and troubleshooting techniques to identify and resolve common issues and delve into security best practices, with a focus on both physical and network security measures.The course also addresses the growing importance of virtualization and cloud computing in modern data centers. You'll gain a clear understanding of how virtualization technology optimizes resources through virtual machines (VMs) and hypervisors, and how data centers integrate with cloud services. Automation and orchestration tools that streamline data center operations will also be covered, ensuring that you stay on the cutting edge of data center management.In addition, the course touches on data center cabling, explaining the different types of cabling systems, such as fiber optics and copper cables, that are critical to high-speed data transmission. Finally, you will learn about data center design principles, ensuring that you understand how to plan and build efficient, scalable, and future-proof data center infrastructures.By the end of this course, you will have a solid foundation in all aspects of data center technology, from hardware and networking to virtualization, operations, and design, giving you the confidence to pursue CCT Data Center certification and advance your career in the rapidly evolving world of data centers.Thank you Who this course is for Entry-Level Technicians: Individuals starting their career in IT and data centers who want to gain foundational knowledge and hands-on skills for maintaining Cisco-based data center equipment. Network Support Engineers: Professionals responsible for supporting and troubleshooting data center environments, particularly those working with Cisco hardware. Field Engineers and Technicians: Those involved in the physical installation, replacement, and maintenance of data center equipment, including servers, switches, and cabling. IT Professionals Seeking Certification: Individuals looking to earn the CCT Data Center certification as a stepping stone toward more advanced Cisco certifications or data center-related roles. System Administrators: Those managing day-to-day data center operations who want to deepen their knowledge of networking, hardware, and virtualization within a data center context. Career Switchers into Data Centers: People transitioning into the data center or IT infrastructure field, seeking a solid understanding of data center technologies and practices. This course is designed to provide practical, hands-on knowledge for those at the beginning stages of their data center careers, as well as professionals looking to enhance their skills and pursue certification. Homepage https://www.udemy.com/course/unlock-cct-data-center-010-151-dctech-essential-training/ Screenshot Rapidgator https://rg.to/file/4fcff160cade4bcad5019b3abe47a882/ukbha.Unlock.CCT.Data.Center.010151.DCTECH.Essential.Training.part2.rar.html https://rg.to/file/b1852bee7c22188aa512759891e22772/ukbha.Unlock.CCT.Data.Center.010151.DCTECH.Essential.Training.part1.rar.html Fikper Free Download https://fikper.com/dPZaJ465i8/ukbha.Unlock.CCT.Data.Center.010151.DCTECH.Essential.Training.part2.rar.html https://fikper.com/g4ouVfLNhS/ukbha.Unlock.CCT.Data.Center.010151.DCTECH.Essential.Training.part1.rar.html No Password - Links are Interchangeable

Free Download Udemy - Pyspark For Data Scientists Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.23 GB | Duration: 4h 43m PySpark for Data Scientists What you'll learn Foundations of PySpark: Gain a solid understanding of fundamental PySpark concepts and principles. Data Manipulation Techniques: Explore key data manipulation techniques such as dataframes, RDDs, and SQL queries in PySpark. Distributed Data Processing: Learn techniques for distributed data processing and optimisation. Data Preparation: Understand and implement strategies for data cleaning and transformation. Requirements Basic Understanding of Python Programming: This includes familiarity with libraries such as NumPy and Pandas. Knowledge of Data Science Fundamentals: Understanding of data manipulation, exploratory data analysis, and basic machine learning concepts. Familiarity with Big Data Concepts: Basic knowledge of big data concepts and distributed computing is beneficial but not required. Description Welcome to the "PySpark for Data Scientists" course! This comprehensive program is designed to equip you with essential knowledge and skills to harness PySpark for big data analytics. Whether you are new to data science or looking to enhance your expertise, this course covers everything required to build, optimize, and analyze large-scale datasets effectively.Throughout the course, you will explore a wide range of PySpark concepts and practical applications, focusing on distributed data processing and large-scale data analysis. You'll begin with the fundamental principles of PySpark and its ecosystem, covering crucial topics such as data manipulation techniques, including DataFrames and RDDs, as well as SQL queries for data transformation. Practical applications of distributed computing will help optimize your data processing workflows. In addition to foundational concepts, the course delves into advanced topics, including data preparation strategies for cleaning and transforming datasets and utilizing PySpark's capabilities for real-time data processing.By the end of this course, you will be proficient in implementing PySpark techniques to tackle complex data challenges. You will be able to extract meaningful insights from large datasets and apply your skills to real-world scenarios across various data-driven fields. Get ready to unlock limitless opportunities in big data analytics! Overview Section 1: Introduction to Big Data Lecture 1 BIG DATA HISTORY PART 1 Lecture 2 BIG DATA HISTORY PART 2 Section 2: Introduction tp RDD and Spark Lecture 3 RDD Introduction Lecture 4 Spark Ecosystem Lecture 5 Spark Lazy Evulation Lecture 6 Spark RDD Setup On Google Colab Lecture 7 Spark context & Spark Session Lecture 8 Spark RDD Transformation - Part 1 Lecture 9 Spark RDD Transformation - Part 2 Lecture 10 Spark RDD Transformation - Part 3 Lecture 11 RDD Action Section 3: Data Frame & Sparke shell Lecture 12 DataFrame - Part 1 Lecture 13 DataFrame - Part 2 Lecture 14 Spark-shell, spark-submit & running spark in local Section 4: Quiz Aspiring Data Scientists,Data Engineers and Analysts,Business Analysts,Students looking to enter the field of big data,Professionals seeking to enhance their data processing skills Homepage https://www.udemy.com/course/pyspark-for-data-scientists/ Rapidgator https://rg.to/file/792ce6d1d4faab677c9652b0c6f5c6b7/hmnyt.Pyspark.For.Data.Scientists.part3.rar.html https://rg.to/file/947c85ae4e85f3f40a6ad2798188d40c/hmnyt.Pyspark.For.Data.Scientists.part2.rar.html https://rg.to/file/dd3de4b5a868b0e344b308f2fecf8b6b/hmnyt.Pyspark.For.Data.Scientists.part1.rar.html Fikper Free Download https://fikper.com/HacITMzbBu/hmnyt.Pyspark.For.Data.Scientists.part3.rar.html https://fikper.com/Qi8h5gtDGp/hmnyt.Pyspark.For.Data.Scientists.part2.rar.html https://fikper.com/xNQXnZdpBi/hmnyt.Pyspark.For.Data.Scientists.part1.rar.html No Password - Links are Interchangeable

Free Download Udemy - Data Science Using R (2024) Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 17.31 GB | Duration: 19h 22m "Data Science Using R: Comprehensive Training in Data Analysis, Visualization, and Machine Learning Techniques . What you'll learn Proficient R Programming: Develop a solid foundation in R programming for data manipulation, analysis, and visualization. Statistical Analysis Skills: Apply statistical methods and machine learning algorithms to derive meaningful insights from datasets. Data Visualization Mastery: Create compelling visualizations using ggplot2 to effectively communicate data findings and trends. Practical Application: Complete real-world projects that enhance problem-solving abilities and demonstrate proficiency in data science concepts. Requirements To enroll in the Data Science Using R course, parti[beeep]nts should have a basic understanding of programming concepts, as familiarity with any programming language will facilitate the learning process. A foundational knowledge of statistics is also beneficial, as it will help students grasp essential data analysis techniques more effectively. Additionally, proficiency in general computer literacy and software applications is required to navigate R and its associated tools. Most importantly, a strong eagerness to learn and a curiosity about data science are crucial for success in this course. These prerequisites will ensure that all students are well-prepared to dive into the exciting world of data science. Description This course, "Data Science with R," is designed for aspiring data scientists and analysts seeking to harness the power of R for data manipulation, analysis, and visualization. Parti[beeep]nts will begin by gaining a solid foundation in R programming, covering key concepts such as data types, structures, and essential functions.As the course progresses, students will delve into data wrangling techniques using packages like dplyr and tidyr, enabling them to clean and prepare datasets for analysis. The curriculum emphasizes statistical analysis, including hypothesis testing, regression models, and machine learning algorithms, empowering parti[beeep]nts to draw meaningful insights from their data.Visualization is a key focus, with instruction on using ggplot2 to create informative and engaging graphics that communicate results effectively. Real-world case studies and hands-on projects will provide practical experience, allowing students to apply their skills to actual data challenges.By the end of the course, parti[beeep]nts will have developed a comprehensive toolkit for data science, including proficiency in R, an understanding of statistical methodologies, and the ability to present their findings clearly. This course is perfect for those looking to kickstart a career in data science or enhance their analytical capabilities in any field and sorroundings.IIBM Institute of Business Management. Overview Section 1: R Introduction Lecture 1 R Introduction Section 2: R Implementation, R Data Structures, R Interfaces, R Interfaces Lecture 2 R Implementation, R Data Structures, R Interfaces, R Interfaces Section 3: Data Visualization Using R Software Lecture 3 Introduction to Visualisation - Line Plots and Bar Charts - Pie Chart and Histog Section 4: Predictive Customer Analytics using R - Linear Regression using R Software Lecture 4 Predictive Customer Analytics using R - Linear Regression using R Software-Part1 Lecture 5 Predictive Customer Analytics using R - Linear Regression using R Software-Part2 Section 5: Bank Loan Modelling using R Lecture 6 Logistic Function - Single Predictor Model. Section 6: Sales Promotion Effectiveness -Dimension Reduction using R Software. Lecture 7 Sales Promotion Effectiveness -Dimension Reduction using R Software - Part 1 Lecture 8 Sales Promotion Effectiveness -Dimension Reduction using R Software - Part 2 Section 7: Customer and Market Segmentation - Cluster Analysis using R Lecture 9 Customer and Market Segmentation - Cluster Analysis using R Software-Part 1 Lecture 10 Customer and Market Segmentation - Cluster Analysis using R Software-Part 2 Section 8: Retail Analytics: Market Basket Analysis (MBA) - Association Rule using R. Lecture 11 Association Rule Introduction - Apriori Algorithm - Multiple Association Rules Section 9: Customer Loyalty Analytics- Naïve Bayes Classification using R Software. Lecture 12 Naïve Bayes Introduction - Probabilistic Basics and Probabilistic Classification Section 10: K - Nearest Neighbour (KNN) Using R Software Lecture 13 K - Nearest Neighbour Introduction - K - Nearest Neighbour Algorithm. Section 11: Decision Trees using R Software Lecture 14 What is a Decision Tree? - How to create Decision Tree Section 12: Random Forest using R Software Lecture 15 Ensample of Decision Tree. Section 13: Support Vector Machine - SVM Lecture 16 Linear SVM using Hyperplane - Non-Linear Hyperplane using Kernal Trick. Section 14: Real Time Project - Customer Loyalty Analytics and its Application. Lecture 17 RFM Segmentation and Analysis - Propensity Modelling and its application. Lecture 18 Real Time Project - Customer Loyalty Analytics and its Application Section 15: Real Time Project - Finance Analytics and its Application using R Lecture 19 Credit Risk Analytics using Logistic Regression. Section 16: Course Complete Revision Lecture 20 Course Complete This course is for aspiring data scientists, analysts, and researchers seeking to enhance their R programming skills, gain insights from data, and apply analytical techniques in real-world scenarios. Screenshot Homepage https://www.udemy.com/course/data-science-using-r-b/ Rapidgator https://rg.to/file/19c80ea942b25abefe3bb5ee7646c076/ciamq.Data.Science.Using.R.2024.part02.rar.html https://rg.to/file/4a89fa03c97158c87416fc6adbc7461f/ciamq.Data.Science.Using.R.2024.part05.rar.html https://rg.to/file/4c334648ee7f9e16eec22ae05c9c26bc/ciamq.Data.Science.Using.R.2024.part07.rar.html https://rg.to/file/5a158dc9d69e2209f64af9cab3c173d6/ciamq.Data.Science.Using.R.2024.part06.rar.html https://rg.to/file/5c9dd47ab17fdd7d53e421ec67a17e1b/ciamq.Data.Science.Using.R.2024.part10.rar.html https://rg.to/file/5ce6afaa7987d19f961c4f87d48abbc4/ciamq.Data.Science.Using.R.2024.part01.rar.html https://rg.to/file/5e56d058cf9592f3e2ba969bb1329b0f/ciamq.Data.Science.Using.R.2024.part04.rar.html https://rg.to/file/72b186dd6c18350dc7462c5406a9ff45/ciamq.Data.Science.Using.R.2024.part12.rar.html https://rg.to/file/7d31289b169cec76667b61de5dcc58e4/ciamq.Data.Science.Using.R.2024.part18.rar.html https://rg.to/file/8a5028fb9d07906a36d8048d79e59ade/ciamq.Data.Science.Using.R.2024.part11.rar.html https://rg.to/file/9da200f852d17a8ffdacb31ddc0693cb/ciamq.Data.Science.Using.R.2024.part08.rar.html https://rg.to/file/a6663035c54100fe5e06416c347aa658/ciamq.Data.Science.Using.R.2024.part14.rar.html https://rg.to/file/ab0507fd686085c2a04a10837df8af9e/ciamq.Data.Science.Using.R.2024.part15.rar.html https://rg.to/file/badebc6ed42b975fa8af67a21d8af586/ciamq.Data.Science.Using.R.2024.part16.rar.html https://rg.to/file/cee9a215c056437c5c5661030334bd3c/ciamq.Data.Science.Using.R.2024.part09.rar.html https://rg.to/file/d053986c792a15dcafa7ae07a9a69b1e/ciamq.Data.Science.Using.R.2024.part17.rar.html https://rg.to/file/d053cd957db11b17beae9b7bf99850a9/ciamq.Data.Science.Using.R.2024.part03.rar.html https://rg.to/file/d75cc7e43c49e31d9437e587e863b344/ciamq.Data.Science.Using.R.2024.part13.rar.html Fikper Free Download https://fikper.com/2cgKc9dMG4/ciamq.Data.Science.Using.R.2024.part18.rar.html https://fikper.com/6VhrQxynnC/ciamq.Data.Science.Using.R.2024.part03.rar.html https://fikper.com/EGFuHSNt2C/ciamq.Data.Science.Using.R.2024.part17.rar.html https://fikper.com/EsbJsIy7a1/ciamq.Data.Science.Using.R.2024.part14.rar.html https://fikper.com/FsYjrjXOiN/ciamq.Data.Science.Using.R.2024.part01.rar.html https://fikper.com/Mc146efif3/ciamq.Data.Science.Using.R.2024.part11.rar.html https://fikper.com/OJlO96M0ev/ciamq.Data.Science.Using.R.2024.part02.rar.html https://fikper.com/Qf624W17X4/ciamq.Data.Science.Using.R.2024.part08.rar.html https://fikper.com/SoWGEZrxSS/ciamq.Data.Science.Using.R.2024.part10.rar.html https://fikper.com/VvlFJeNZY9/ciamq.Data.Science.Using.R.2024.part16.rar.html https://fikper.com/WBJwAkLF35/ciamq.Data.Science.Using.R.2024.part06.rar.html https://fikper.com/hXHUUsoihd/ciamq.Data.Science.Using.R.2024.part15.rar.html https://fikper.com/iFt541FxyM/ciamq.Data.Science.Using.R.2024.part07.rar.html https://fikper.com/iO8cfnHgdG/ciamq.Data.Science.Using.R.2024.part13.rar.html https://fikper.com/pIbj0oU8Jm/ciamq.Data.Science.Using.R.2024.part05.rar.html https://fikper.com/uVo9h9Ud5j/ciamq.Data.Science.Using.R.2024.part04.rar.html https://fikper.com/wXhFe2flLk/ciamq.Data.Science.Using.R.2024.part12.rar.html https://fikper.com/x1Qd6QeILJ/ciamq.Data.Science.Using.R.2024.part09.rar.html No Password - Links are Interchangeable

Free Download Udemy - Data Mesh Demystified Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 546.15 MB | Duration: 1h 52m Mastering Data Mesh: Building Scalable, Decentralized Data Architectures for Modern Organizations What you'll learn Understand Data Mesh Principles: Explain key principles like Domain Ownership, Data as a Product, and Federated Governance in Data Mesh. Compare Traditional Data Architectures vs. Data Mesh: Identify how Data Mesh solves challenges of traditional architectures, such as scalability and data silos. Design and Implement a Self-Serve Data Platform: Build a self-serve platform for teams to access and manage data autonomously within your organization. Data Governance and Quality Practices Implement governance and data quality management in a decentralized Data Mesh environment to ensure security and complianc Requirements There are no strict prerequisites for taking this course, making it accessible for both beginners and experienced professionals. However, having the following skills or knowledge will be beneficial: Basic Understanding of Data Concepts: Familiarity with basic data concepts like databases, data storage, and data management will help grasp the more advanced concepts of Data Mesh. Experience with Data Architecture (Optional): Some experience with data architecture, data warehouses, or data pipelines would make it easier to understand the comparisons between traditional systems and Data Mesh. Comfort with Technology and Cloud Platforms (Optional): Having a general understanding of cloud platforms like AWS, Azure, or GCP is helpful, especially when discussing the tools and technologies used in Data Mesh. Eagerness to Learn: Most importantly, a willingness to learn new concepts and explore modern data architectures is all you really need! Description In today's fast-paced world, organizations are generating more data than ever before, and traditional data architectures often struggle to keep up. Data Mesh is a revolutionary approach to data management that decentralizes ownership, promotes scalability, and enables cross-team collaboration. This course will guide you through the foundational concepts, practical tools, and real-world strategies to implement Data Mesh within your organization.Throughout the course, you will learn:The challenges of traditional data architectures and how Data Mesh solves themThe core principles of Data Mesh, including Domain Ownership and Data as a ProductHow to design and build a Self-Serve Data Platform that empowers teams to access and manage data autonomouslyHow to implement data governance and ensure high data quality in a decentralized systemPractical steps to deploy Data Mesh within your organization, including tracking success and continuous improvementThis course combines theoretical knowledge with practical examples and step-by-step guides, making it perfect for data architects, engineers, IT leaders, and anyone looking to modernize their data infrastructure. By the end of the course, you will have the tools and knowledge to design scalable, decentralized data architectures using Data Mesh principles.Whether you are new to Data Mesh or an experienced professional, this course will provide you with actionable insights to transform the way your organization handles data.Join me to unlock the potential of Data Mesh and take your data strategy to the next level! Overview Section 1: Introduction Lecture 1 Welcome Section 2: Introduction to Data Mesh Lecture 2 What is Data Mesh? Lecture 3 Traditional Data Architecture Challenges Lecture 4 How Data Mesh Solves These Challenges Lecture 5 Benefits of Adopting Data Mesh Section 3: Core Principles of Data Mesh Lecture 6 Introduction to the Four Key Principles Lecture 7 Domain Ownership Lecture 8 Data as a Product Lecture 9 Self-Serve Data Platform Lecture 10 Federated Computational Governance Section 4: Data Mesh Architecture and Components Lecture 11 Overview of Data Mesh Architecture Lecture 12 Domain Teams and Their Roles Lecture 13 Data Products Lecture 14 Self-Serve Data Platform in Detail Lecture 15 Federated Governance in Practice Section 5: Tools & Technologies for Data Mesh Implementation Lecture 16 Introduction to Data Mesh Tools Lecture 17 Data Storage Solutions in Data Mesh Lecture 18 Data Pipelines and ETL Tools Lecture 19 APIs and Service Mesh for Data Exchange Lecture 20 Data Visualization and Reporting Section 6: Data Governance and Data Quality in Data Mesh Lecture 21 Introduction to Data Governance in Data Mesh Lecture 22 Implementing Data Governance Policies Lecture 23 Data Quality Management in a Decentralized System Lecture 24 Monitoring and Continuous Improvement of Data Quality Section 7: Implementing Data Mesh in Your Organization Lecture 25 Step-by-Step Implementation Guide Lecture 26 Defining Data Products and Governance Structures Lecture 27 Building the Self-Serve Data Platform Lecture 28 Tracking Success and Continuous Improvement Section 8: Conclusion Lecture 29 Thank You! Data Architects and Engineers: Professionals responsible for designing and building data systems will find this course valuable as it explores modern Data Mesh architectures, providing insights into scaling and decentralizing data management.,Business Intelligence (BI) Professionals: Those working in BI roles who want to understand how Data Mesh can improve data access and collaboration across departments will benefit from learning how to implement decentralized data governance and self-serve platforms.,Data Governance and Quality Specialists: Professionals focused on data governance and quality will gain a deeper understanding of how Data Mesh addresses challenges around data compliance, quality, and security in a decentralized environmen,IT Leaders and Decision Makers: IT managers and leaders seeking to modernize their organization's data architecture and improve collaboration across teams will find this course helpful for evaluating Data Mesh as a solution.,Learners New to Data Mesh Concepts: If you're curious about new approaches to data architecture, particularly Data Mesh, and want to learn how to apply it in real-world settings, this course provides an accessible introduction. Screenshot Homepage https://www.udemy.com/course/data-mesh-demystified/ Rapidgator https://rg.to/file/ebb92d37dbb7c9778ee3f17c31d28650/tjqtl.Data.Mesh.Demystified.rar.html Fikper Free Download https://fikper.com/82ljrKxiJ9/tjqtl.Data.Mesh.Demystified.rar.html No Password - Links are Interchangeable

Free Download Udemy - Data Management In Clinical Trials Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 434.22 MB | Duration: 2h 9m Mastering Data Management: Explore EDC Systems, Regulatory Compliance, Data Quality, and Emerging Technologies in Clinic What you'll learn Understand the fundamental principles and best practices of clinical data management in clinical trials. Use key tools and technologies such as electronic data capture (EDC) systems, while adhering to regulatory standards. Design clinical trial databases, ensure data quality, and apply security measures to protect patient privacy and confidentiality. Implement data standards such as CDISC (SDTM and ADaM) in clinical trials, facilitating data integration and analysis. Apply techniques for data validation, cleaning, and analysis, and generate statistical reports in compliance with regulations. Prepare and submit data to regulatory authorities, ensuring adherence to international requirements Explore emerging technologies like artificial intelligence, machine learning, and blockchain to enhance efficiency and security in clinical data management. Requirements Not required Description This 10-module course provides a thorough and comprehensive exploration of data management in clinical trials, covering both foundational principles and cutting-edge developments in the field. Parti[beeep]nts will gain in-depth knowledge about how data is organized, structured, and managed across all phases of a clinical trial, ensuring that the data maintains its quality, integrity, and security throughout the entire process. The course delves into essential topics such as data collection methodologies, the utilization of electronic data capture (EDC) systems, and the adherence to global standards like CDISC (Clinical Data Interchange Standards Consortium). It also addresses critical privacy regulations, including GDPR and HIPAA, ensuring that parti[beeep]nts understand the legal and ethical aspects of handling clinical data.Further, the course highlights the processes involved in preparing clinical trial data for submission to regulatory bodies, such as the FDA and EMA, focusing on the importance of meeting specific technical and formatting requirements. In addition to these core components, the course examines recent innovations in the field, such as the integration of artificial intelligence, blockchain technologies, and real-world data (RWD) in clinical trials. These modules provide parti[beeep]nts with a forward-thinking perspective, equipping them with the tools and knowledge to navigate the evolving landscape of clinical data management. Overview Section 1: Introduction to Data Management in Clinical Trials Lecture 1 Introduction to Data Management in Clinical Trials Section 2: Data Collection Methods and Tools Lecture 2 Data Collection Methods and Tools Section 3: Data Standards in Clinical Trials Lecture 3 Data Standards in Clinical Trials Section 4: Data Quality Management Lecture 4 Data Quality Management Section 5: Data Privacy and Security in Clinical Trials Lecture 5 Data Privacy and Security in Clinical Trials Section 6: Database Design and Build Lecture 6 Database Design and Build Section 7: Data Integration and Interoperability Lecture 7 Data Integration and Interoperability Section 8: Statistical Analysis and Reporting Lecture 8 Statistical Analysis and Reporting Section 9: Data Submission to Regulatory Authorities Lecture 9 Data Submission to Regulatory Authorities Section 10: Future Trends in Data Management for Clinical Trials Lecture 10 Future Trends in Data Management for Clinical Trials Section 11: Exam Professionals in the pharmaceutical and biotechnology industries looking to enhance their knowledge of data management in clinical trials.,Clinical research coordinators, monitors, biostatisticians, and data managers involved in clinical trials.,Personnel from contract research organizations (CROs) and clinical trial sponsors.,Students and recent graduates in fields such as health sciences, biomedicine, statistics, and technology.,Anyone interested in understanding how data is managed within the context of clinical trials, including regulatory and technological aspects. Homepage https://www.udemy.com/course/data-management-in-clinical-trials/ Rapidgator https://rg.to/file/8ce09c72d04f10f5fd041eb8431b0d4a/nbjly.Data.Management.In.Clinical.Trials.rar.html Fikper Free Download https://fikper.com/Be0CRD4jUU/nbjly.Data.Management.In.Clinical.Trials.rar.html No Password - Links are Interchangeable

Free Download The Ultimate Python Data Visualization Course- Step By Step Published 10/2024 Created by Click Learning MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 28 Lectures ( 4h 34m ) | Size: 1.61 GB Master Data Visualization with Python: A Complete Step-by-Step Guide to Unlocking the Power of Your Data What you'll learn Introduction to Python for Data Visualization Installing Required Libraries (Matplotlib, Seaborn, Plotly, etc.) Basic Plotting: Line Plots, Scatter Plots, and Bar Charts Customizing Plots: Titles, Labels, and Legends Creating Subplots for Multiple Charts Adding Annotations and Text to Plots Saving and Exporting Charts for Different Formats Customizing Aesthetics with Seaborn Themes and Styles Creating Pair Plots, Heatmaps, and Violin Plots Visualizing Relationships with Seaborn (Categorical, Linear, and Non-linear) Creating Interactive Line, Bar, and Scatter Plots Building Interactive Dashboards with Plotly Dash Visualizing Time Series Data Optimizing Performance for Large Data Visualizations Principles of Effective Data Storytelling Using Color Effectively in Data Visualizations Requirements No Prior Experience Required Description Unlock the power of your data with 'The Ultimate Python Data Visualization Course- Step By Step.' This comprehensive course is designed to take you from a beginner to an expert in Python data visualization. You'll learn how to create stunning and informative visuals that communicate your data's story effectively.Starting with the basics, you'll delve into Python's powerful libraries like Matplotlib, Seaborn, and Plotly. Each section of the course builds on the previous one, ensuring a solid understanding of core concepts before moving on to more advanced techniques. You'll work on real-world projects and practical examples that bring theory to life and equip you with skills you can apply immediately.This Course Include:Introduction to Data VisualizationIntroduction to Python for Data VisualizationThe Importance of Data Visualization and TypessInstalling Required Libraries (Matplotlib, Seaborn, Plotly, etc.)Getting Started with MatplotlibBasic Plotting: Line Plots, Scatter Plots, and Bar ChartsCustomizing Plots: Titles, Labels, and LegendsWorking with Colors, Markers, and Line StylesCreating Subplots for Multiple ChartsAdvanced Matplotlib TechniquesCustomizing Plot Axes and TicksAdding Annotations and Text to PlotsCreating Histograms and Density PlotsWorking with 3D Plots in MatplotlibSaving and Exporting Charts for Different FormatsData Visualization with SeabornCreating Pair Plots, Heatmaps, and Violin PlotsCustomizing Aesthetics with Seaborn Themes and StylesVisualizing Relationships with Seaborn (Categorical, Linear, and Non-linear)Interactive Visualizations with PlotlyCreating Interactive Line, Bar, and Scatter PlotsVisualizing Geospatial Data with PlotlyBuilding Interactive Dashboards with Plotly DashVisualizing Data with Pandas and Other LibrariesUsing Pandas for Quick Data VisualizationVisualizing Time Series DataData Visualization with Altair and BokehCreating Interactive Visualizations with AltairVisualizing Large DatasetsWorking with Big Data: Challenges and StrategiesVisualizing Data with Dask and VaexOptimizing Performance for Large Data VisualizationsVisual Storytelling and Design PrinciplesPrinciples of Effective Data StorytellingUsing Color Effectively in Data VisualizationsTypography and Layout for Enhanced ClarityDesigned for data analysts, business professionals, and aspiring data scientists, this course provides the tools to make data-driven decisions with confidence. Unlock your data's potential with this comprehensive, step-by-step guide and become a visualization expert.Enroll now in this transformative journey and start making your data speak volumes! Who this course is for Anyone interested in Python programming, Python scripting, machine learning, data science and data visualization. Those who are interested to learn data science or data visualization application. Homepage https://www.udemy.com/course/the-ultimate-python-data-visualization-course-step-by-step/ Screenshot Rapidgator https://rg.to/file/783f9c110dcde69cf132342f3a221ea7/ewjek.The.Ultimate.Python.Data.Visualization.Course.Step.By.Step.part1.rar.html https://rg.to/file/edaa199bbd8532f73322222a990ffd74/ewjek.The.Ultimate.Python.Data.Visualization.Course.Step.By.Step.part2.rar.html Fikper Free Download https://fikper.com/8CpWwNFahM/ewjek.The.Ultimate.Python.Data.Visualization.Course.Step.By.Step.part1.rar.html https://fikper.com/QWUmkdlzrZ/ewjek.The.Ultimate.Python.Data.Visualization.Course.Step.By.Step.part2.rar.html No Password - Links are Interchangeable

Free Download The Complete Data Storytelling Masterclass + Certificate Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 710.80 MB | Duration: 3h 39m Internationally Accredited Certification | Learn Data Storytelling, Data Visualisation and Tell Stories with Data What you'll learn Learn the basics of data storytelling and why it's important today. Understand the key parts of a data story and how to use them. Explore tools like Excel, Tableau, and Power BI for creating data visuals. Build compelling narratives with structure and flow in your data stories. Turn raw data into engaging stories, step by step. Requirements None. Just a smartphone / tab / computer / laptop with speakers/ headphone. Description Welcome to this IRAP Accredited Certification Data Storytelling.Unlock the power of data visualisation and data visualization in our comprehensive storytelling with data course designed to transform your data insights into compelling narratives. Learn from data-driven storytelling examples and understand how to apply these techniques through practical, real-world applications. This course covers the essential skills of storytelling and storytelling data, teaching you how to tell a story with data and tell stories with data effectively. You'll explore an example of data storytelling and multiple examples of data storytelling to gain a deeper understanding of strategic insights. Dive into the teachings of Mike X Cohen, and discover the principles behind storytelling with data a data visualization guide for business professionals, inspired by the works of Cole Nussbaumer Knaflic. Our course covers all aspects, including storytelling with data books, storytelling with data book, and even provides access to resources like storytelling with data pdf and storytelling with data pdf github. This course offers an immersive experience through a hands-on storytelling with data workshop and introduces you to strategic data storytelling. With insights from popular storytelling with data courses, you'll be equipped to master data narratives and elevate your data storytelling skills. Join us to transform your data into powerful, strategic stories!In this course, you will learn:Introduction to Data StorytellingImportance of Data Storytelling in the Modern WorldKey Elements of a Data Story in Data StorytellingUnderstanding Data in Data Storytelling: Types and SourcesPrinciples of Effective Communication in Data StorytellingThe Role of Visualization in Data StorytellingCrafting a Compelling Narrative in Data Storytelling: Structure and FlowIntroduction to Data Preparation and Analysis in Data StorytellingCollecting and Cleaning Data in Data StorytellingExploring and Understanding Data in Data StorytellingIdentifying Key Insights and Patterns in Data StorytellingTools and Techniques for Data Analysis in Data StorytellingPrinciples of Data Visualization in Data StorytellingChoosing the Right Chart or Graph in Data StorytellingAdvanced Visualization Techniques in Data StorytellingTools for Data Visualization in Data Storytelling (Excel, Tableau, Power BI, etc.)Developing a Narrative Arc in Data StorytellingIntegrating Data and Storytelling in Data StorytellingCreating Engaging Introductions and Conclusions in Data StorytellingTechniques for Persuasive Storytelling in Data StorytellingBuilding a Data Story from Scratch in Data Storytelling: Step-by-StepTransforming Raw Data into Compelling Stories in Data Storytelling: Step-by-StepUsing Templates and Frameworks for Efficiency in Data Storytelling: Step-by-StepRefining and Revising Your Data Story in Data Storytelling: Step-by-StepAddressing Common Challenges in Data StorytellingTroubleshooting Data Visualization Issues in Data StorytellingStrategies for Improving Your Data Storytelling SkillsInteractive Data Stories and Dashboards in Data StorytellingThe Future of Data Storytelling: Trends and TechnologiesIncorporating AI and Machine Learning in Data Stories Overview Section 1: Introduction to the Course Lecture 1 Introduction to the Course Lecture 2 Free Download Course Manual Section 2: Introduction to Data Storytelling Lecture 3 Introduction to Data Storytelling Lecture 4 Importance of Data Storytelling in the Modern World Lecture 5 Key Elements of a Data Story in Data Storytelling Section 3: Understanding Data Lecture 6 Understanding Data in Data Storytelling: Types and Sources Section 4: Principles of Communication and Visualization Lecture 7 Principles of Effective Communication in Data Storytelling Lecture 8 The Role of Visualization in Data Storytelling Section 5: Crafting the Narrative Lecture 9 Crafting a Compelling Narrative in Data Storytelling: Structure and Flow Section 6: Data Preparation and Analysis Lecture 10 Introduction to Data Preparation and Analysis in Data Storytelling Lecture 11 Collecting and Cleaning Data in Data Storytelling Lecture 12 Exploring and Understanding Data in Data Storytelling Lecture 13 Identifying Key Insights and Patterns in Data Storytelling Lecture 14 Tools and Techniques for Data Analysis in Data Storytelling Section 7: Data Visualization Techniques Lecture 15 Principles of Data Visualization in Data Storytelling Lecture 16 Choosing the Right Chart or Graph in Data Storytelling Lecture 17 Advanced Visualization Techniques in Data Storytelling Lecture 18 Tools for Data Visualization in Data Storytelling (Excel, Tableau, Power BI, etc Section 8: Enhancing the Narrative Lecture 19 Developing a Narrative Arc in Data Storytelling Lecture 20 Integrating Data and Storytelling in Data Storytelling Lecture 21 Creating Engaging Introductions and Conclusions in Data Storytelling Lecture 22 Techniques for Persuasive Storytelling in Data Storytelling Section 9: Building and Refining Data Stories Lecture 23 Building a Data Story from Scratch in Data Storytelling: Step-by-Step Lecture 24 Transforming Raw Data into Compelling Stories in Data Storytelling: Step-by-Step Lecture 25 Using Templates and Frameworks for Efficiency in Data Storytelling: Step-by-Step Lecture 26 Refining and Revising Your Data Story in Data Storytelling: Step-by-Step Section 10: Challenges and Troubleshooting Lecture 27 Addressing Common Challenges in Data Storytelling Lecture 28 Troubleshooting Data Visualization Issues in Data Storytelling Section 11: Skill Enhancement Lecture 29 Strategies for Improving Your Data Storytelling Skills Section 12: Future of Data Storytelling Lecture 30 Interactive Data Stories and Dashboards in Data Storytelling Lecture 31 The Future of Data Storytelling: Trends and Technologies Lecture 32 Incorporating AI and Machine Learning in Data Stories Section 13: Getting Certified Lecture 33 Download Customized Certificate This course is for anyone who wants to learn data storytelling from scratch.,Perfect for beginners with no prior experience in data analysis or storytelling.,Designed for professionals who want to improve their communication with data.,Great for business owners looking to make data-driven decisions through storytelling.,Perfect for learners who want to develop new skills in data analysis and visualization. Screenshot Homepage https://www.udemy.com/course/the-complete-data-storytelling-masterclass-certificate/ Rapidgator https://rg.to/file/505451fdc7b979f0b7f4736fb80ac123/ajaiz.The.Complete.Data.Storytelling.Masterclass..Certificate.rar.html Fikper Free Download https://fikper.com/6lqHpKhHyh/ajaiz.The.Complete.Data.Storytelling.Masterclass..Certificate.rar.html No Password - Links are Interchangeable

Free Download The Advanced SQL Server Masterclass For Data Analysis Last updated 8/2024 Created by Travis Cuzick MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 82 Lectures ( 8h 7m ) | Size: 2 GB Take your SQL skills - and your career - to the next level What you'll learn Installing SQL Server and SQL Server Management Studio Window Functions Correlated Subqueries Advanced filtering with EXISTS and NOT EXISTS Flattening data with PIVOT Generating data series with Recursive CTEs Leveraging CTEs and temporary tables to break complex processes into manageable steps Defining and manipulating tables with DDL and DML commands Designing lookup tables to simplify redundant analysis SQL optimization techniques, including indexes Procedural programming techniques like variables and IF statements Defining your own SQL functions Creating stored procedures for flexible, repeatable analysis Supercharge your SQL knowledge with procedural programming techniques like variables and IF statements Writing code that writes code, with Dynamic SQL Requirements Access to a Windows 10 operating system (SQL Server only runs on Windows); there are known compatibility issues between Windows 11 and SQL Server 2019, so I STRONGLY recommend Windows 10 for this course. If you only have access to a Windows 11 machine, you can install SQL Server 2022 instead. Microsoft SQL Server 2019 and SQL Server Management Studio; these are both available for free, and I'll walk you through installation and setup. A working knowledge of SQL fundamentals - including SELECT statements, applying criteria, table joins, and aggregate queries - is necessary for success in this course. Specific knowledge of SQL Server is helpful, but not required. Description Do you already know the basics of SQL, but sometimes get frustrated when you encounter situations where the basics just aren't enough?Are you a junior analyst who wants to level up to advanced SQL so you can take the next step in your career?Or maybe you're a data scientist who knows enough SQL to get by, but want to take your skills further so you can spend less time wrangling data and more time building models.If any of these sound like your situation, then you're in the right place. This course on advanced SQL for data analysis has everything that isn't covered in introductory SQL courses.The curriculum goes DEEP, spanning all the advanced techniques you'll ever need to wrangle and analyze data in the fastest and most efficient way possible.And these concepts aren't presented in isolation; everything is taught in the context of real-world analytics scenarios, meaning you'll be ready to apply these techniques on the job from day one.Here's a look at just some of the things you'll get out of this course:Make the leap to Senior Analyst by mastering advanced data wrangling techniques with SQLBecome the resident SQL expert on your teamPerform nuanced analysis of large datasets with Window FunctionsUse subqueries, CTEs and temporary tables to handle complex, multi-stage queries and data transformationsWrite efficient, optimized SQLLeverage indexes to speed up your SQL queriesSupercharge your SQL knowledge with procedural programming techniques like variables and IF statementsProgram database objects like user defined functions and stored procedures that will make life easier for you AND your teammatesMaster useful tips and tricks not found in most database courses, like Dynamic SQLGain an intuition for what technique to apply and whenTrain your brain with tons of hands-on exercises that reflect real-world business scenariosWhat makes this course differentThere are three things that really set this course apart.First is its scope. We'll dig deep into the advanced toolbox that SQL has to offer, uncovering techniques to solve problems that leave even senior analysts scratching their heads.Second is a relentless focus on that practical, real-world applications. Techniques are taught not as abstract concepts, but rather as solutions to common data wrangling and analytics problems. And whenever possible, these techniques are presented, not in isolation, but in combination with other things that have already been covered. This "spiral" approach reinforces concepts you've learned so they stay with you long after taking the course.Finally, and most importantly, the course provides a treasure trove of coding exercises that give you ample opportunity for hands-on practice. And these exercises are distributed throughout the course - not clustered together after hours of videos. This means you'll have an opportunity to practice every concept you learn, right after you learn it.So if you want to go from SQL apprentice to SQL master, enroll today. I look forward to seeing you in the course. Who this course is for Data Analysts or BI Professionals wanting to "make the leap" to Senior Analyst/Developer Data Scientists who aspire to stand out from their peers by going beyond the basics in SQL Job seekers who want to turbocharge their resumes with advanced SQL skills Students seeking a comprehensive but practical pathway to SQL mastery Anyone who wants to take their SQL data analysis skills to the next level Homepage https://www.udemy.com/course/advanced-sql-server-masterclass-for-data-analysis/ Screenshot Rapidgator https://rg.to/file/180175786d980780774ceaa9c8d4acbb/kmapa.The.Advanced.SQL.Server.Masterclass.For.Data.Analysis.part2.rar.html https://rg.to/file/6f7645fe4eb5ca85d1daa998c7765dca/kmapa.The.Advanced.SQL.Server.Masterclass.For.Data.Analysis.part3.rar.html https://rg.to/file/7516dfbd8bdcc3630b168a12c3398ce9/kmapa.The.Advanced.SQL.Server.Masterclass.For.Data.Analysis.part1.rar.html Fikper Free Download https://fikper.com/BVGSXi4rKv/kmapa.The.Advanced.SQL.Server.Masterclass.For.Data.Analysis.part1.rar.html https://fikper.com/pUNQuJ143H/kmapa.The.Advanced.SQL.Server.Masterclass.For.Data.Analysis.part2.rar.html https://fikper.com/wLUDng9eWq/kmapa.The.Advanced.SQL.Server.Masterclass.For.Data.Analysis.part3.rar.html No Password - Links are Interchangeable

Free Download Take Control of Your Big Data with HUE in Cloudera CDH Duration: 2h 54m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 395 MB Genre: eLearning | Language: English Working with Big Data is no small task. Jumpstart your Hadoop skills by loading, visualizing, analyzing, and searching your data using Cloudera HUE, the Hadoop User Experience. Take control of your Big Data! Hadoop is a very complex ecosystem with a potentially pretty steep learning curve to get started from scratch. To make adoption easier, several distributions have been created to integrate all key projects and give a turn-key approach, one of the most popular and complete being Cloudera CDH. In this course, Take Control of Your Big Data with HUE in Cloudera CDH, you'll learn how to leverage Hadoop using a relatable data source. First, you'll explore how to work with the major components of a cluster. Next, you'll discover how to load data into your cluster and how to analyze it with query editors. Finally, you'll go one level beyond with interactive dashboards using HUE. By the end of this course, you'll be able to load, process, and analyze your big data using HUE in Cloudera CDH. Homepage https://www.pluralsight.com/courses/big-data-hue-cloudera-cdh Rapidgator https://rg.to/file/92b12b57b3579aaa5ff51f171d725df7/icnlf.Take.Control.of.Your.Big.Data.with.HUE.in.Cloudera.CDH.rar.html Fikper Free Download https://fikper.com/553G1ZHycG/icnlf.Take.Control.of.Your.Big.Data.with.HUE.in.Cloudera.CDH.rar.html No Password - Links are Interchangeable

Free Download Snowflake End-to-End Cloud Data Warehousing & Analytics Published 10/2024 Created by Uplatz Training MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 39 Lectures ( 30h 44m ) | Size: 15.3 GB Build Cloud Data Platform with Snowflake. Accelerate your career in data engineering, data science, and cloud computing. What you'll learn Understand Data Warehousing Fundamentals - Gain foundational knowledge of data warehousing, including concepts and principles, and how they relate to Snowflake. Master Data Modeling Techniques - Learn best practices for designing efficient data models to optimize performance and storage in Snowflake. Explore Snowflake Architecture - Understand Snowflake's unique architecture and how it supports cloud-native data warehousing and analytics. Create and Manage Data Warehouses in Snowflake - Develop practical skills in creating and managing data warehouses using Snowflake's interface and tools. Efficiently Load and Unload Data - Learn to load and unload data using various methods, including from external storage solutions (AWS, Azure, GCP). Effectively Manage Complex Data Formats - Handle complex formats like JSON and Parquet using Snowflake. Implement Data Transformations - Gain expertise in performing transformations during the data loading process to clean and structure data efficiently. Learn Snowflake's performance optimization features - caching, clustering and resource monitoring to ensure cost-effective and high-performance data operations. Leverage Time Travel, Fail Safe, and Zero-Copy Clone - Explore Snowflake's advanced features like Time Travel, Fail Safe, and Zero-Copy Clone for data recovery. Manage Secure Data Sharing - Learn how to securely share data within and outside of Snowflake environments, including with non-Snowflake users. Implement Best Practices for Snowflake Administration - Master account administration, access management, and apply best practices for efficient Snowflake usage Requirements Enthusiasm and determination to make your mark on the world! Description A warm welcome to the Snowflake: End-to-End Cloud Data Warehousing & Analytics course by Uplatz.Snowflake is a cloud-based data warehousing platform designed to handle massive volumes of structured and semi-structured data. It's built from the ground up to leverage cloud infrastructure, offering scalability, performance, and ease of use. Snowflake is not tied to any specific cloud provider; it runs on AWS, Microsoft Azure, and Google Cloud Platform (GCP), providing flexibility for businesses to use their preferred cloud platform.Snowflake's architecture, scalability, and advanced features make it a powerful platform for modern data warehousing, analytics, and data engineering. Its flexibility to handle massive datasets, structured and semi-structured data, and multi-cloud capabilities has positioned it as a preferred choice for businesses looking to leverage cloud-native data platforms.How Snowflake WorksSnowflake operates using a unique architecture that separates storage and compute, allowing for independent scaling of resources. Key methodology in its working involves:Data Storage: Snowflake stores data in a compressed, columnar format on cloud storage. Data is logically organized into databases, schemas, and tables, but physically, Snowflake manages how data is stored and optimized on the backend.Compute Layer (Virtual Warehouses): Compute resources, called virtual warehouses, are independent clusters of resources that process queries and workloads. Virtual warehouses can be scaled up or down based on performance needs and can run multiple, parallel queries without interfering with each other.Cloud Services Layer: This layer manages metadata, optimization, security, and query parsing. It handles authentication, query planning, and transaction management, allowing Snowflake to offer features like automated scaling, data sharing, and access controls.The separation of storage and compute makes Snowflake highly flexible. You can store large volumes of data without worrying about compute costs when the data is not being queried. Conversely, you can scale compute resources for demanding queries without impacting the storage cost.Core Features of SnowflakeSeparation of Storage and Compute: Snowflake allows independent scaling of compute resources (virtual warehouses) and storage. This flexibility helps optimize costs and performance based on workload requirements.Multi-Cloud Availability: Snowflake runs on all major cloud platforms (AWS, Azure, GCP), offering cross-cloud functionality and flexibility in choosing cloud providers.Instant Elasticity: Snowflake can instantly scale compute resources up or down based on workload demands. You can run multiple queries simultaneously without performance degradation.Data Sharing: Snowflake offers secure data sharing across organizations or between Snowflake accounts without moving or copying data. This feature allows real-time data collaboration.Support for Structured and Semi-Structured Data: Snowflake natively supports a wide range of data formats, including JSON, Parquet, Avro, and XML, making it easier to load and query semi-structured data alongside structured data.Zero-Copy Cloning: This feature allows you to create a copy of databases, tables, and schemas instantly without duplicating the data. It enables quick testing or development without additional storage costs.Time Travel and Fail-Safe: Time Travel allows users to access historical data versions for up to 90 days, facilitating recovery from accidental data changes or deletions. Fail-Safe provides an additional data recovery mechanism for a defined period.Automatic Scaling and Concurrency: Snowflake automatically manages concurrency, allowing multiple users to query data simultaneously without affecting performance, and automatically scales up or down depending on demand.Security and Compliance: Snowflake includes robust security features such as end-to-end encryption, role-based access controls, and multi-factor authentication (MFA). It complies with industry standards like GDPR, HIPAA, and SOC 2.Snowpipe: Snowpipe is Snowflake's continuous data ingestion tool that automates loading data from external sources (such as AWS S3, Azure Blob, GCP Storage) into Snowflake in near real-time.Snowflake - Course CurriculumIntroduction to Data Warehouse - part 1Introduction to Data Warehouse - part 2Data Modelling - part 1Data Modelling - part 2Introduction to Snowflake and ArchitectureCreate Datawarehouse in SnowflakeLoad Data in a TableSnowflake Pricing and Resource MonitorLoading Data from External StorageTransformations while LoadingCopy Options and File Formats - part 1Copy Options and File Formats - part 2Loading of JSONLoading of ParquetData UnloadingPerformance Optimizations in SnowflakeCaching and ClusteringLoading Data from AWS External StorageSnowpipe in AWSLoading Data from Azure CloudSnowpipe in AzureLoading and Uploading Data from GCPTime Travel - part 1Time Travel - part 2Fail Safe and Types of TablesZero Copy CloneData Sharing - part 1Data Sharing - part 2Data Sharing with non-Snowflake Users - part 1Data Sharing with non-Snowflake Users - part 2Secure vs Normal ViewData SamplingScheduling TasksMaterialized View - part 1Materialized View - part 2Dynamic Data MaskingAccess Management and Account Administration - part 1Access Management and Account Administration - part 2Best Practices in Snowflake Who this course is for Data Engineers - Looking to build data pipelines and handle data transformations within Snowflake. Data Analysts - Aiming to perform data querying, reporting, and analysis on cloud-based data platforms. Beginners and newbies aspiring for a career in Cloud Data Warehousing and Snowflake. Data Architects - Designing scalable cloud data architectures using Snowflake's features. Solution Architects - Architecting comprehensive solutions that integrate Snowflake for efficient data management and analytics. Cloud Engineers - Implementing and managing cloud infrastructure for data operations on Snowflake. Business Intelligence Professionals - Using Snowflake for analytics, reporting, and generating business insights. ETL Developers - Handling Extract, Transform, Load (ETL) processes using Snowflake for efficient data movement and transformation. Data Scientists - Working with large datasets, performing advanced analytics, and leveraging Snowflake for machine learning workflows. Cloud Data Engineers - Focused on developing data solutions in the cloud using Snowflake's infrastructure. Database Administrators (DBAs) - Transitioning to cloud data platforms like Snowflake for managing and optimizing data storage and access. Developers - Needing to integrate Snowflake with other applications and services. IT Professionals - Transitioning to cloud-based data solutions and aiming to broaden their cloud data expertise. Homepage https://www.udemy.com/course/snowflake-cloud-data-platform/ Screenshot Rapidgator https://rg.to/file/04c274e4e4660d28ce83d8f54de386b1/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part14.rar.html https://rg.to/file/092952882e4ffcafd7c2319af9de8d00/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part16.rar.html https://rg.to/file/1c35dd6b4352a03eb3bcbd41beb3cb3e/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part05.rar.html https://rg.to/file/29814b308aa16a6b0378a4ad84fe864b/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part03.rar.html https://rg.to/file/3355d9cefd17b274dcf2b84b7988afa3/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part09.rar.html https://rg.to/file/3eaf7ed34755595d4c19a775236ba2d2/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part12.rar.html https://rg.to/file/486ab0cd620746b73474fc7258bde642/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part08.rar.html https://rg.to/file/60c0be9f49d0cd801741301c34cb60a3/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part06.rar.html https://rg.to/file/6676893434335eb15f23f55ec8fd3922/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part13.rar.html https://rg.to/file/8efb9e6b60073878bd8516af447dc6eb/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part15.rar.html https://rg.to/file/9d21079483842b42a19b57cb8c3cf6ba/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part01.rar.html https://rg.to/file/9f5fe987239eeb2f8c2dcab734cb6217/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part07.rar.html https://rg.to/file/b1b9074a032b3ea0aa4f0c583ae0716a/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part11.rar.html https://rg.to/file/c60557ce8399efe1f4a3132ae13db8c9/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part04.rar.html https://rg.to/file/cdf997f0b09f9a23b16d91be7e9fb0c8/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part02.rar.html https://rg.to/file/ddcfe0a6bfa614d7e0ba8ec50ea06d50/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part10.rar.html Fikper Free Download https://fikper.com/7UTuKilP29/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part11.rar.html https://fikper.com/8BOoWkq91t/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part12.rar.html https://fikper.com/AfKXLushMm/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part10.rar.html https://fikper.com/EUPjYZsEze/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part06.rar.html https://fikper.com/GGEiXzuXqI/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part08.rar.html https://fikper.com/SCM9WnGoZp/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part09.rar.html https://fikper.com/YsNxoJVxqw/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part04.rar.html https://fikper.com/fuMZKbdqLi/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part02.rar.html https://fikper.com/jwn3CGCJw2/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part07.rar.html https://fikper.com/k3uIPISlCH/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part15.rar.html https://fikper.com/nMVyIwQpI9/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part03.rar.html https://fikper.com/uBwWK9UBgR/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part14.rar.html https://fikper.com/uZtfhyb9cr/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part05.rar.html https://fikper.com/vL2KyNeRjk/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part13.rar.html https://fikper.com/yMo1nksnsZ/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part01.rar.html https://fikper.com/zkBFTHXZJJ/bubyb.Snowflake.EndtoEnd.Cloud.Data.Warehousing..Analytics.part16.rar.html No Password - Links are Interchangeable

Free Download Security Engineering Access Control and Data Protection Released 10/2024 By Chris Jackson MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 31m | Size: 220 MB Welcome to the next course in the Security Engineering learning path Access Control and Data Protection. This course will teach you to protect and control access to sensitive data. This course solves the challenge of securing sensitive data by teaching you how to implement access controls, protect information, and analyze data flows effectively. In this course, Security Engineering: Access Control and Data Protection, you'll learn to protect and control access to sensitive data. First, you'll explore implementing access controls. Next, you'll discover methods to protect data. Finally, you'll learn how to analyze databases and data flows. When you're finished with this course, you'll have the skills and knowledge needed to protect and access organizational data. Homepage https://www.pluralsight.com/courses/sec-engineering-access-ctrl-data-protection Screenshot Rapidgator https://rg.to/file/97a7ff37afd9ea0c7fd25d97e38ce29d/koivg.Security.Engineering.Access.Control.and.Data.Protection.rar.html Fikper Free Download https://fikper.com/rd9aYyHDZD/koivg.Security.Engineering.Access.Control.and.Data.Protection.rar.html No Password - Links are Interchangeable

Free Download SSIS Design Patterns for Data Warehousing Last updated 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 50m | Size: 721 MB Learn about the most popular design patterns used in data warehousing. This course will show how to solve common SSIS problems with designs tested and used by others in the industry. Over time, certain designs have emerged in SSIS as the best way to solve particular types of problems. These have become best practices, and can be used in your environment as well. In this course, you will learn about the most common patterns used in data warehousing, which are also applicable to non-data warehouse situations. Homepage https://www.pluralsight.com/courses/ssis-design-patterns-data-warehousing Screenshot Rapidgator https://rg.to/file/91f37d8ef74dad5b4f921f793eca5599/egpxg.SSIS.Design.Patterns.for.Data.Warehousing.rar.html Fikper Free Download https://fikper.com/AD3GcUc3Wr/egpxg.SSIS.Design.Patterns.for.Data.Warehousing.rar.html No Password - Links are Interchangeable

Free Download SQL Practice Inserting Data with INSERT Statements Released 10/2024 With Walter Shields MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 23m | Size: 43 MB Sharpen your skills in inserting data into SQL tables with the INSERT INTO statement in this interactive course featuring hands-on coding challenges in CoderPad. Course details Level up your SQL skills with this comprehensive course focused on inserting data into SQL tables. Through interactive Code Challenges powered by CoderPad, you'll get hands-on coding practice with real-time feedback, ensuring that you understand each concept as you go. Join instructor Walter Shields as he guides you through mastering the INSERT INTO statement, handling diverse data types, and verifying data insertion with SELECT statements. Tailored for intermediate to advanced learners, this course will equip you with the expertise to manage data insertion tasks with precision and confidence. Ideal for data professionals, database administrators, and software developers, this course prepares you to efficiently insert data into SQL tables across any scenario. Homepage https://www.linkedin.com/learning/sql-practice-inserting-data-with-insert-statements Screenshot Rapidgator https://rg.to/file/0cd026efb95537553c25f2fd5ddea591/duijw.SQL.Practice.Inserting.Data.with.INSERT.Statements.rar.html Fikper Free Download https://fikper.com/EWdUypzJ4J/duijw.SQL.Practice.Inserting.Data.with.INSERT.Statements.rar.html No Password - Links are Interchangeable

Free Download Rust Hands-On Seismic Data Fetch & Analysis Published 10/2024 Created by Pegah Flashgary MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 6 Lectures ( 51m ) | Size: 263 MB Observable Async Data Processing with Rust What you'll learn Learn how to create Rust applications using Rust's unique features like structs, traits, and macros. Leverage Rust's async capabilities to build responsive applications, ensuring smooth multitasking without blocking or delays. Transform raw data into meaningful insights using Rust libraries such as ndarray, linfa, and Polars for advanced data analysis. Monitor and optimize your application in real time using OpenTelemetry and Jaeger, gaining full visibility into performance, traces, logs, and metrics. Requirements No prior Rust knowledge is needed, but you should be familiar with basic programming concepts. Description If you're looking to build something fast, reliable, and packed with real-time data power, this course is for you. Using Rust, we'll create an app that fetches and analyzes live earthquake data straight from sources like the USGS. We'll dive into Rust's async programming to make sure everything stays snappy while it's analyzing data and multitasking.You'll see how to build out a clean `EarthquakeEvent` structure using Rust's structs and traits, and set up functionality to handle real-time data smoothly. Along the way, we'll explore how to monitor your app's performance using OpenTelemetry and Jaeger, giving you real-time insights and an easy way to keep things running smoothly.What's Inside:Structs and Traits: Rust's approach to organizing and structuring real-time data in a way that's easy to work with.Async Programming: We'll keep the app responsive by handling multiple tasks at once, so you're ready for real-time data processing.Data Analysis: Get hands-on with Rust libraries to pull insights and trends from earthquake event data.Observability: With tools like OpenTelemetry and Jaeger, you'll be able to monitor every part of your app, keeping performance in check.This course is super practical because you'll build a real app that handles real-world data needs. Who this course is for This course is for programmers with experience in other languages who want to switch to Rust. You'll learn Rust by building a real-world application step by step. Homepage https://www.udemy.com/course/rust-hands-on-seismic-data-fetch-analysis/ Screenshot Rapidgator https://rg.to/file/ebb3298931bbb76ff35e09974ace2eb7/uuwnd.Rust.HandsOn.Seismic.Data.Fetch..Analysis.rar.html Fikper Free Download https://fikper.com/wZFjDnCP4U/uuwnd.Rust.HandsOn.Seismic.Data.Fetch..Analysis.rar.html No Password - Links are Interchangeable

Free Download Quantitative Analysis of Data Using SPSS V29 Published 10/2024 Created by Alain Tannous MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 21 Lectures ( 4h 19m ) | Size: 1.93 GB Quantitative Analysis, SPSS Software, Tests and Conclusions What you'll learn Explain the six SPSS windows and their specific uses. Calculate the mean, sum, standard deviation, variance, and more. Examine relationships using Scatter Plot and Scatter Plot Matrices. Test for Normality and Homoscedasticity. Explain numerical measures such as Central Location, Dispersion, Skewness, Kurtosis and Linearity. Identify and differentiate between the different forms of outcomes. Differentiate between the statistical types I and II errors. Construct a multiple regression model, identify its elements and list its assumptions. Learn to use the Ordinary Least Squares (OLS) regression method to estimate the coefficients. Define the Standard Error of Estimates (SEE), explain its role in regression model and understand its visual representation. Relate SEE to R-squared and the overall performance of the regression model. Define ANOVA, explain its assumptions and significance in regression analysis, set its hypotheses, conduct ANOVA analysis using SPSS and explain its outcomes. Formulate the null and alternative hypotheses. Conduct a statistical T-test to assess and interpret the significance of the coefficients. Define autocorrelation, identify its impact on regression models and test for autocorrelation using Durbin-Watson Test. Explain the importance of forecasting time series data and perform a forecast seasonal time series data using SPSS V29. Define MANOVA, explain its assumptions and significance in regression analysis, set its hypotheses, conduct MANOVA test using SPSS and explain its outcomes. Generate different tests related to MANOVA such as: Box's M Test, Hotelling's T Squared, Pillai's Trace Test and Wilk's Lambda Test. Formulate and test the hypotheses of all MANOVA tests using SPSS. Compose and test the hypotheses for Levene's Test of homogeneity of variances. Define Discriminant analysis and identify its variables, assumptions and hypotheses. Test for Multivariate Normality, Equality of Covariances and Vectors of Means. Perform a stepwise discriminant analysis and analyze its outcomes using the cross-validation method. Define logistic regression, identify its variables and assumptions, compose its hypotheses, perform a stepwise logistic regression test and analyze its outputs. Define the Prin[beeep]l Component Analysis (PCA), identify its key variables and assumptions, and conduct a PCA analysis following a detailed procedure on SPSS. Define Factor Analysis (FA), identify its assumptions, variables and hypotheses, and conduct all FA tests and analyze their outcomes. Define Cluster Analysis, identify its assumptions, variables and hypotheses, and conduct all FA tests and analyze their outcomes. Apply all tests using practical exercises on SPSS Requirements No prerequisites are required. You will learn all steps in this course. Description This course offers a detailed exploration of quantitative data analysis using SPSS V29, a powerful software tool widely used in research fields such as social sciences, business, healthcare, and education. Designed for both beginners and intermediate users, the course covers essential statistical techniques and guides learners through the process of managing, analyzing, and interpreting quantitative data. Over 4 hours and 20 minutes of recorded lectures, accompanied by PDF notes for each session, will introduce you to core concepts in data analysis, such as:Data entry, management, and cleaning techniques using SPSS V29Descriptive statistics, including measures of central tendency and variabilityInferential statistics such as t-tests, ANOVA, regression analysis, and chi-square testsMultivariate analysis techniques, including factor analysis, discriminant analysis, and cluster analysisReporting and interpreting statistical outputs effectivelyUsing visual tools like graphs and charts for data presentationThe course also includes SPSS data files for hands-on practice, ensuring that students gain practical experience working with real datasets.Additionally, a quiz at the end of each lesson allows students to assess their understanding and apply the skills learned. By the end of this course, parti[beeep]nts will be able to effectively use SPSS V29 to perform complex statistical analyses, create meaningful data visualizations, and report results professionally and clearly, equipping them with the tools needed for academic research or professional projects. Who this course is for Graduate and Postgraduate students who are engaged in writing their thesis or dissertation which is based on a quantitative analysis of data. Beginner researchers who require a tool to analyze their findings. Homepage https://www.udemy.com/course/quantitative-analysis-of-data-using-spss-v29/ Screenshot Rapidgator https://rg.to/file/4ee0c5dd42c50de8b7cb0531cad05f37/aferd.Quantitative.Analysis.of.Data.Using.SPSS.V29.part2.rar.html https://rg.to/file/c42573ee55f2e4566e978ead782fdd44/aferd.Quantitative.Analysis.of.Data.Using.SPSS.V29.part1.rar.html Fikper Free Download https://fikper.com/9IFYkeRpEH/aferd.Quantitative.Analysis.of.Data.Using.SPSS.V29.part1.rar.html https://fikper.com/O1NEa86Gdz/aferd.Quantitative.Analysis.of.Data.Using.SPSS.V29.part2.rar.html No Password - Links are Interchangeable

Free Download Python For Data Science Your Career Accelerator Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 3.83 GB | Duration: 10h 37m Master Python and Unlock Data Analysis, Visualization, and Machine Learning Skills What you'll learn Learn the basics of Python, including data types, variables, loops, conditionals, and string manipulation. Gain hands-on experience with essential libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization. Learn to clean, transform, and preprocess datasets for analysis, preparing them for real-world data science tasks. Understand the concepts of object-oriented programming (OOP) and apply them to structure Python code efficiently. By the end of the course, students will have completed a data science capstone project, where they will collect, analyze, and present insights from a real-world Requirements No programming experience or knowledge of Data Science required. Just come with a passion to learn. Description Are you ready to embark on an exciting journey into the world of data science? "Python for Data Science: Your Career Accelerator" is meticulously designed to transform beginners into proficient data science professionals, equipping you with the essential skills and knowledge needed to thrive in today's rapidly evolving, data-driven landscape.This comprehensive Python for Data Science course covers:Comprehensive Python Course: Master Python programming from the basics to advanced data science applications, including essential libraries like Pandas and NumPy.Data Analysis: Learn essential techniques to manipulate, clean, and analyze real-world datasets, ensuring your data is ready for actionable insights.Data Visualization: Create impactful visualizations using libraries like Matplotlib and Seaborn to present data in a meaningful way and drive decision-making.Machine Learning: Explore core machine learning concepts and algorithms, from linear regression to classification models, and apply them to solve real-world problems.Hands-on Projects: Work on real-world projects to build practical skills and a strong portfolio for your data science career, preparing you to excel in the field.Career-Focused: Gain the skills to excel in roles like Data Analyst, Data Scientist, or Machine Learning Engineer with the confidence to tackle industry challenges.With a focus on practical, project-based learning, this course equips you with both theoretical knowledge and hands-on experience, ensuring you're ready to succeed in the fast-growing field of data science. Overview Section 1: Python Essentials: From Basics to Collaboration Lecture 1 Welcome Note & Intro to python Lecture 2 Introduction to Google Colab Notebook Lecture 3 Introduction to GitHub Lecture 4 Print & Comment Section 2: Python Basics: Fundamental Concepts and Operations Lecture 5 Variables & Assignment Operators Lecture 6 Understanding Data Types Lecture 7 Understanding Expressions Lecture 8 Arithmetic & Assignment Operators Lecture 9 Relational/Comparison Operators Lecture 10 Logical Operators Lecture 11 Identity & Membership Operators, Type Lecture 12 User Input Section 3: Mastering Conditional Branching in Python Lecture 13 Conditional Statements with Logical Operators Lecture 14 If-elif-else Statements Lecture 15 Switch Case Section 4: Mastering Loops in Python Lecture 16 For Loop Lecture 17 While Loops Lecture 18 Do-While Loop Lecture 19 Break and Continue Statements Section 5: Exploring Functions in Python Lecture 20 Introduction to Functions & Pass Statements in Python Lecture 21 Working with Function Arguments Lecture 22 Functions with Return Types Lecture 23 Understanding Local and Global Variables Lecture 24 Lambda Functions in Python Section 6: Mastering Strings in Python Lecture 25 Creating Strings Lecture 26 Understanding Strings as Arrays Lecture 27 Looping Through Strings Lecture 28 String Manipulation Lecture 29 Essential String Operations Lecture 30 Exploring Useful String Methods Section 7: Mastering Lists in Python Lecture 31 Introduction to Lists Lecture 32 Iterating Through List Items Lecture 33 Exploring List Properties Lecture 34 Mastering List Manipulation Lecture 35 Exploring List Methods in Python Section 8: Mastering Tuples in Python Lecture 36 Introduction to Tuples Lecture 37 Advanced Tuple Operations Lecture 38 Mastering Tuple Operations Lecture 39 Exploring Tuple Methods and Operations Section 9: Mastering Dictionaries in Python Lecture 40 Introduction to Dictionaries Lecture 41 Dictionary Operations Lecture 42 Looping through Dictionaries Lecture 43 Essential Dictionary Methods Section 10: Exploring Sets in Python Lecture 44 Understanding Sets Lecture 45 Exploring Set Operations and Looping Lecture 46 Set Operations Lecture 47 Exploring Set Methods Section 11: Machine Learning with K-Nearest Neighbors Lecture 48 KNN Theory Explained Lecture 49 KNN Regression from Scratch using Python Lecture 50 KNN Classification from Scratch using Python Section 12: Machine Learning with Support Vector Machine Lecture 51 SVM Theory Explained Lecture 52 SVM Regression using Python Lecture 53 SVM Classification using Python Section 13: Machine Learning with K-Means Clustering Lecture 54 Detailed Overview of K-Means Clustering Lecture 55 K-Means Clustering using Python Beginners,Career Switchers,Students,Data Enthusiasts Homepage https://www.udemy.com/course/python-for-data-science-your-career-accelerator/ Rapidgator https://rg.to/file/0b96396af360738b70f969c9f56645d7/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part3.rar.html https://rg.to/file/560bf988f4484f5d21009ea8a33aa391/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part2.rar.html https://rg.to/file/732b91242f1e89a3d5d627f9231d28c2/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part1.rar.html https://rg.to/file/aa873b7af3d02ebdac6ed3f2b635364d/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part4.rar.html Fikper Free Download https://fikper.com/A5VoQywqSj/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part1.rar.html https://fikper.com/ngsEG1P93X/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part2.rar.html https://fikper.com/ww0lblGIhg/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part4.rar.html https://fikper.com/yJgJBFN38T/rkpjb.Python.For.Data.Science.Your.Career.Accelerator.part3.rar.html No Password - Links are Interchangeable

Free Download Python Data Processing and Visualization Published 10/2024 Created by Studio 01 App MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 42 Lectures ( 1h 10m ) | Size: 471 MB Master Data Manipulation and Visualization with Python What you'll learn: Understand how to manipulate data using Python's core libraries, such as Pandas and NumPy. Create compelling visualizations to communicate insights effectively using Matplotlib and Plotly. Apply data processing techniques to clean and transform data into a usable format. Gain the ability to identify data patterns and present them clearly through visual storytelling. Requirements: Basic knowledge of Python programming is recommended but not required. A willingness to learn and experiment with new tools and techniques. Description: This comprehensive course is designed to take you on an in-depth journey through the world of data processing and visualization using Python.Starting from the basics, we will introduce you to the essential concepts of data manipulation, focusing on powerful libraries like Pandas, NumPy, and others.Throughout the course, we will provide hands-on exercises and real-world examples, ensuring that you gain practical skills to work with different types of data, including structured and unstructured formats.Our focus will then shift to the art of data visualization, where you will explore how to present your data in visually engaging formats using popular libraries such as Matplotlib, Seaborn, and Plotly.The course will cover various chart types, including line charts, bar charts, histograms, scatter plots, heatmaps, and more, helping you understand when and how to use each type effectively.Whether you are a beginner looking to develop core data processing skills or an experienced professional seeking to expand your visualization toolkit, this course provides everything you need to succeed in modern data analysis.By the end of this course, you will be fully equipped to clean, process, analyze, and visualize data, providing valuable insights and making informed decisions based on your analyses. Who this course is for: Aspiring data analysts, data scientists, and professionals interested in enhancing their data processing skills. Anyone looking to improve their ability to analyze and visualize data for better decision-making. Homepage https://www.udemy.com/course/python-data-processing-visualization/ Rapidgator https://rg.to/file/604a618fe4c8df591dd19b6b1574ee13/nlrwz.Python.Data.Processing.and.Visualization.rar.html Fikper Free Download https://fikper.com/2ddzPt6TDx/nlrwz.Python.Data.Processing.and.Visualization.rar.html No Password - Links are Interchangeable

Free Download Professional Certificate in Data Science 2024 Last updated 1/2024 Created by Academy of Computing & Artificial Intelligence MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 170 Lectures ( 26h 36m ) | Size: 12 GB Learn All the Skills to Become a Data Scientist[ Machine Learning,Deep Learning, CNN, DCGAN, Python, Java, Algorithms] What you'll learn Python Programming Basics For Data Science Machine Learning -[A -Z] Comprehensive Training with Step by step guidance Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest) Unsupervised Learning - Clustering, K-Means clustering Evaluating the Machine Learning Algorithms : Precision, Recall, F-Measure, Confusion Matrices, Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it. Algorithm Analysis For Data Scientists KERAS Tutorial - Developing an Artificial Neural Network in Python -Step by Step Deep Learning -Handwritten Digits Recognition[Step by Step][Complete Project ] Deep Convolutional Generative Adversarial Networks (DCGAN) Java Programming For Data Scientists Kaggle - Covid 19- Classification (Chest X-ray.) - Covid-19 & Pneumonia Developing a CNN From Scratch for CIFAR-10 Photo Classification Requirements Computer & Internet Connection Description At the end of the Course you will have all the skills to become a Data Science Professional. (The most comprehensive Data Science course )1) Python Programming Basics For Data Science - Python programming plays an important role in the field of Data Science2) Introduction to Machine Learning -[A -Z] Comprehensive Training with Step by step guidance3) Setting up the Environment for Machine Learning - Step by step guidance4) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines, Random Forest)5) Unsupervised Learning6) Evaluating the Machine Learning Algorithms7) Data Pre-processing8) Algorithm Analysis For Data Scientists9) Deep Convolutional Generative Adversarial Networks (DCGAN)10) Java Programming For Data ScientistsCourse Learning OutcomesTo provide awareness of the two most integral branches (Supervised & Unsupervised learning) coming under Machine LearningDescribe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.To build appropriate neural models from using state-of-the-art python framework.To build neural models from scratch, following step-by-step instructions. To build end - to - end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available. To critically review and select the most appropriate machine learning solutionsTo use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.Beginners guide for python programming is also inclusive. Introduction to Machine Learning - Indicative Module ContentIntroduction to Machine Learning:- What is Machine Learning ?, Motivations for Machine Learning, Why Machine Learning? Job Opportunities for Machine Learning Setting up the Environment for Machine Learning:-Downloading & setting-up Anaconda, Introduction to Google CollabsSupervised Learning Techniques:-Regression techniques, Bayer's theorem, Naïve Bayer's, Support Vector Machines (SVM), Decision Trees and Random Forest.Unsupervised Learning Techniques:- Clustering, K-Means clusteringArtificial Neural networks[Theory and practical sessions - hands-on sessions]Evaluation and Testing mechanisms :- Precision, Recall, F-Measure, Confusion Matrices, Data Protection & Ethical PrinciplesSetting up the Environment for Python Machine LearningUnderstanding Data With Statistics & Data Pre-processing (Reading data from file, Checking dimensions of Data, Statistical Summary of Data, Correlation between attributes)Data Pre-processing - Scaling with a demonstration in python, Normalization , Binarization , Standardization in Python,feature Selection Techniques : Univariate SelectionData Visualization with Python -charting will be discussed here with step by step guidance, Data preparation and Bar Chart,Histogram , Pie Chart, etc..Artificial Neural Networks with Python, KERASKERAS Tutorial - Developing an Artificial Neural Network in Python -Step by StepDeep Learning -Handwritten Digits Recognition[Step by Step][Complete Project ]Naive Bayes Classifier with Python[Lecture & Demo]Linear regressionLogistic regressionIntroduction to clustering[K - Means Clustering ]K - Means ClusteringThe course will have step by step guidance for machine learning & Data Science with Python.You can enhance your core programming skills to reach the advanced level. By the end of these videos, you will get the understanding of following areas the Python Programming Basics For Data Science - Indicative Module ContentPython ProgrammingSetting up the environmentPython For Absolute Beginners : Setting up the Environment : AnacondaPython For Absolute Beginners : Variables , Lists, Tuples , DictionaryBoolean operationsConditions , Loops(Sequence , Selection, Repetition/Iteration)FunctionsFile Handling in PythonAlgorithm Analysis For Data Scientists This section will provide a very basic knowledge about Algorithm Analysis. (Big O, Big Omega, Big Theta)Java Programming for Data Scientists Deep Convolutional Generative Adversarial Networks (DCGAN)Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) are one of the most interesting and trending ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator , learns to create images that look real, while a discriminator learns to tell real images apart from fakes.At the end of this section you will understand the basics of Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) .This will have step by step guidance Import TensorFlow and other librariesLoad and prepare the datasetCreate the models (Generator & Discriminator)Define the loss and optimizers (Generator loss , Discriminator loss)Define the training loopTrain the modelAnalyze the output Does the course get updated?We continually update the course as well.What if you have questions?we offer full support, answering any questions you have.Who this course is for:Beginners with no previous python programming experience looking to obtain the skills to get their first programming job.Anyone looking to to build the minimum Python programming skills necessary as a pre-requisites for moving into machine learning, data science, and artificial intelligence.Who want to improve their career options by learning the Python Data Engineering skills. Who this course is for Anyone who wish to start the career in Data Science Homepage https://www.udemy.com/course/professional-certificate-in-data-science/ Screenshot Rapidgator https://rg.to/file/18d196d14a3f59b827d0cb89be5f5a8b/ghggk.Professional.Certificate.in.Data.Science.2024.part06.rar.html https://rg.to/file/2967d6bae298c6236baed2c08e7025d9/ghggk.Professional.Certificate.in.Data.Science.2024.part01.rar.html https://rg.to/file/351f999e0c80597de93cdca88c95f589/ghggk.Professional.Certificate.in.Data.Science.2024.part11.rar.html https://rg.to/file/3a0cb41992365aec181f3e3bf2bdeb86/ghggk.Professional.Certificate.in.Data.Science.2024.part04.rar.html https://rg.to/file/47ef317bc29c19fada2672e7b5286a2d/ghggk.Professional.Certificate.in.Data.Science.2024.part03.rar.html https://rg.to/file/4980a26b793349c312fa011745f90541/ghggk.Professional.Certificate.in.Data.Science.2024.part10.rar.html https://rg.to/file/6f18b5cbc7cc3903a31d891319657266/ghggk.Professional.Certificate.in.Data.Science.2024.part02.rar.html https://rg.to/file/a53baaa699ec9553125a9aab97a1752c/ghggk.Professional.Certificate.in.Data.Science.2024.part09.rar.html https://rg.to/file/a77b4ef6c02e2d02523b620c266d873f/ghggk.Professional.Certificate.in.Data.Science.2024.part08.rar.html https://rg.to/file/a9d94c3d7c9eb1d6e49ca3c0c4efcec8/ghggk.Professional.Certificate.in.Data.Science.2024.part05.rar.html https://rg.to/file/ac2ab425a249d75b2085b7543c55e4ff/ghggk.Professional.Certificate.in.Data.Science.2024.part07.rar.html https://rg.to/file/d5c8b5e332695099e437890b8ad817f4/ghggk.Professional.Certificate.in.Data.Science.2024.part13.rar.html https://rg.to/file/f8617ead8b6153316f33da40e15dd631/ghggk.Professional.Certificate.in.Data.Science.2024.part12.rar.html Fikper Free Download https://fikper.com/51NF5S2RCe/ghggk.Professional.Certificate.in.Data.Science.2024.part12.rar.html https://fikper.com/8zYdU3fxIW/ghggk.Professional.Certificate.in.Data.Science.2024.part04.rar.html https://fikper.com/I2FJDcvhkg/ghggk.Professional.Certificate.in.Data.Science.2024.part05.rar.html https://fikper.com/NuVfhiuCNC/ghggk.Professional.Certificate.in.Data.Science.2024.part03.rar.html https://fikper.com/PzLmPNPTGy/ghggk.Professional.Certificate.in.Data.Science.2024.part06.rar.html https://fikper.com/RJqhFAqWnv/ghggk.Professional.Certificate.in.Data.Science.2024.part13.rar.html https://fikper.com/YluJbV3mOD/ghggk.Professional.Certificate.in.Data.Science.2024.part02.rar.html https://fikper.com/c9IxZvdgc3/ghggk.Professional.Certificate.in.Data.Science.2024.part10.rar.html https://fikper.com/gz4zzLLRjm/ghggk.Professional.Certificate.in.Data.Science.2024.part07.rar.html https://fikper.com/nszn4HqvQB/ghggk.Professional.Certificate.in.Data.Science.2024.part11.rar.html https://fikper.com/pkuQ5KriTY/ghggk.Professional.Certificate.in.Data.Science.2024.part08.rar.html https://fikper.com/rhtgDJKnA6/ghggk.Professional.Certificate.in.Data.Science.2024.part09.rar.html https://fikper.com/ysQ41IX4K7/ghggk.Professional.Certificate.in.Data.Science.2024.part01.rar.html No Password - Links are Interchangeable

Free Download Modern HR Generative AI, AI and Data Analysis in HR Published 10/2024 Created by GenMan Solutions MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 18 Lectures ( 1h 55m ) | Size: 658 MB Learn how Generative AI, predictive AI models and HR Data Analytics are transforming today's Human Resource Management What you'll learn Discover the key applications of Generative AI in HR and how it transforms recruitment, employee engagement, and learning processes. Explore how AI models can predict workforce trends and forecast HR outcomes to drive strategic decisions. Master the use of Generative AI to streamline the recruitment and selection process, from candidate sourcing to interview automation. Apply AI-driven text analysis techniques to enhance employee feedback systems and sentiment analysis. Implement data-driven HR strategies that leverage analytics to improve decision-making and workforce planning. Create predictive AI models that can assist in workforce forecasting and optimize HR resource allocation. Learn how HR metrics dashboards help track performance, retention, and engagement, turning raw data into actionable insights. Balance traditional HR intuition with data analytics to form well-rounded, strategic decisions in talent management and development. Requirements No prior experience with AI or data analytics is needed-just a basic understanding of HR functions, access to a computer, and a willingness to learn. Description Are you an HR professional looking to stay ahead in the rapidly changing world of human resources? Do you want to integrate cutting-edge technology into your HR strategy and revolutionize your approach to recruitment, employee engagement, and decision-making? As the HR landscape evolves, the ability to leverage AI and data analytics is not just a luxury but a necessity for the modern HR practitioner.In Modern HR: Generative AI, AI and Data Analysis in HR, you'll discover how transformative technologies like Generative AI, predictive AI models, and HR data analytics are reshaping HR operations. This course equips you with practical tools to harness these innovations, enabling you to lead HR initiatives with precision, efficiency, and insight.In this course, you will:Develop a foundational understanding of how Generative AI enhances recruitment, employee engagement, and learning and development processes.Master traditional AI techniques to build predictive models, forecast workforce trends, and conduct text analysis for HR tasks.Apply data analytics to make strategic HR decisions and balance data-driven insights with human intuition.Discover how to implement data-driven HR strategies that turn raw insights into meaningful actions.Identify the key metrics that will help you optimize workforce management and contribute to business goals.Why is this course so important? As organizations increasingly rely on technology to gain a competitive edge, HR professionals must evolve from administrative roles to strategic partners in the business. AI and analytics enable smarter, faster decision-making, improving everything from recruitment to employee retention and talent development.Throughout the course, you will engage in hands-on activities, including building AI models, analyzing HR data sets, and developing actionable insights that can be applied directly to your workplace. By the end, you'll not only understand these advanced technologies but also know how to leverage them to drive tangible results in your HR function.This course is different because it bridges the gap between core HR functions and advanced tech integration, designed specifically for HR professionals. Whether you're new to AI and data analytics or looking to deepen your expertise, this course provides a practical, step-by-step guide to mastering the future of HR.Ready to transform your HR career and become a leader in the digital era? Enroll now and start revolutionizing the way you manage your workforce! Who this course is for HR Managers who want to integrate AI and data analytics into their HR operations for more strategic decision-making. HR Analysts eager to master data analytics tools for actionable workforce insights and forecasting. HR Executives seeking to transform their department into a strategic, data-driven business partner. Learning & Development Professionals looking to optimize employee training programs with AI-powered personalization. Talent Acquisition Specialists who aim to streamline recruitment processes using Generative AI. Homepage https://www.udemy.com/course/modern-hr-generative-ai/ Screenshot Rapidgator https://rg.to/file/bfcd76a2df3c074fcd29d7bac6d5605c/ykucb.Modern.HR.Generative.AI.AI.and.Data.Analysis.in.HR.rar.html Fikper Free Download https://fikper.com/npSjMZXZU0/ykucb.Modern.HR.Generative.AI.AI.and.Data.Analysis.in.HR.rar.html No Password - Links are Interchangeable

Free Download Math 0-1 Matrix Calculus in Data Science & Machine Learning Last updated 10/2024 Created by Lazy Programmer Inc. MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 40 Lectures ( 6h 55m ) | Size: 2.75 GB A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers What you'll learn Derive matrix and vector derivatives for linear and quadratic forms Solve common optimization problems (least squares, Gaussian, financial portfolio) Understand and implement Gradient Descent and Newton's method Learn to use the Matrix Cookbook Requirements Competence with Calculus and Linear Algebra Optional: Familiarity with Python, Numpy, and Matplotlib to implement optimization techniques Description Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you'll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices.Course Objectives:Understand the basics of matrix calculus, linear and quadratic forms, and their derivatives.Learn how to utilize the famous Matrix Cookbook for a wide range of matrix calculus operations.Gain proficiency in optimization techniques like gradient descent and Newton's method in one and multiple dimensions.Apply the concepts learned to real-world problems in machine learning and data science, with hands-on exercises and Python code examples.Why Matrix Calculus? Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal.Section 1: Linear and Quadratic Forms In the first part of the course, we'll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.The Matrix Cookbook is a valuable resource that compiles a wide range of matrix derivative formulas in one place. You'll learn how to use this reference effectively, saving you time and ensuring the accuracy of your derivations.Section 2: Optimization Techniques Optimization lies at the heart of many machine learning and data science tasks. In this section, we will explore two crucial optimization methods: gradient descent and Newton's method. You'll learn how to optimize not only in one dimension but also in high-dimensional spaces, which is essential for training complex models. We'll provide Python code examples to help you grasp the practical implementation of these techniques.Course Structure:Each lecture will include a theoretical introduction to the topic.We will work through relevant mathematical derivations and provide intuitive explanations.Hands-on exercises will allow you to apply what you've learned to real-world problems.Python code examples will help you implement and experiment with the concepts.There will be opportunities for questions and discussions to deepen your understanding.Prerequisites:Basic knowledge of linear algebra, calculus, and Python programming is recommended.A strong desire to learn and explore the fascinating world of matrix calculus.Conclusion: Matrix calculus is an indispensable tool in the fields of machine learning and data science. It empowers you to understand, create, and optimize algorithms that drive innovation and decision-making in today's data-driven world. This course will equip you with the knowledge and skills to navigate the intricate world of matrix calculus, setting you on a path to become a proficient data scientist or machine learning engineer. So, let's dive in, embrace the world of matrices, and unlock the secrets of data science and machine learning together! Who this course is for Students and professionals interested in the math behind AI, data science and machine learning Homepage https://www.udemy.com/course/matrix-calculus-machine-learning/ Screenshot Rapidgator https://rg.to/file/5920dbeb204f141b0926febfc4ab86c9/orurp.Math.01.Matrix.Calculus.in.Data.Science..Machine.Learning.part3.rar.html https://rg.to/file/819f2e19373cc592ac7cee41839080ff/orurp.Math.01.Matrix.Calculus.in.Data.Science..Machine.Learning.part2.rar.html https://rg.to/file/e92d22a0f463fc6607336ad328b0d34c/orurp.Math.01.Matrix.Calculus.in.Data.Science..Machine.Learning.part1.rar.html Fikper Free Download https://fikper.com/E1fmLnmMwS/orurp.Math.01.Matrix.Calculus.in.Data.Science..Machine.Learning.part2.rar.html https://fikper.com/KaMiHn4Ygw/orurp.Math.01.Matrix.Calculus.in.Data.Science..Machine.Learning.part3.rar.html https://fikper.com/mwTIgXgrgQ/orurp.Math.01.Matrix.Calculus.in.Data.Science..Machine.Learning.part1.rar.html No Password - Links are Interchangeable

Free Download Master Data Structures and Algorithms in Java Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 1h 2m | Size: 540 MB Learn the Data Structures and Algorithms in Java and Be able to ace the interviews! What you'll learn Become more confident and prepared for your next coding interview Learn, implement and use different Algorithms in Java Programming Language Learn, implement, and use different Data Structures in Java Programming Language Learn Arrays and linked lists in Java Requirements Need to have basic of coding knowledge Description Data Structures and Algorithms (DSA) are essential components of computer science, offering systematic approaches to organizing data and solving computational problems efficiently. In Java, a popular object-oriented language, DSA becomes particularly powerful due to its extensive library support and consistent performance. Data structures-such as arrays, linked lists, stacks, queues, trees, hash tables, and graphs-form the foundation of how data is organized, accessed, and modified.Algorithms are the procedures that operate on these data structures. Searching algorithms like binary search optimize data retrieval by narrowing down search spaces in sorted data, reducing time complexity to O(log n). Sorting algorithms such as merge sort, quicksort, and insertion sort play a critical role in data organization, each offering unique trade-offs in time and space complexity. Merge sort and quicksort are divide-and-conquer algorithms that partition data to sort it efficiently, often with logarithmic or linearithmic complexity. Arrays and linked lists represent linear data structures; arrays offer quick access but fixed size, while linked lists enable dynamic sizing but require sequential access. Mastering data structures and algorithms in Java equips programmers with skills to design scalable applications, making optimal use of resources and ensuring responsive software. Proficiency in DSA also prepares developers for more complex programming challenges, such as implementing custom data structures or optimizing algorithms for high performance, both critical in advanced computing and industry applications. Who this course is for Anyone preparing for programming interviews Software Developers Homepage https://www.udemy.com/course/master-data-structures-and-algorithms-in-java/ Screenshot Rapidgator https://rg.to/file/bba5575176c83ec4aff7a9558551b90e/jnyby.Master.Data.Structures.and.Algorithms.in.Java.rar.html Fikper Free Download https://fikper.com/YlTQuCE8CN/jnyby.Master.Data.Structures.and.Algorithms.in.Java.rar.html No Password - Links are Interchangeable

Free Download Master Data Structures + Algorithms For Developers Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 204.96 MB | Duration: 0h 57m Learn the Data Structures and Algorithms for the interviews What you'll learn Become more confident and prepared for your next coding interview Learn, implement and use different Algorithms Learn, implement, and use different Data Structures Learn everything you need to ace difficult coding interviews Requirements Need to have basic of coding knowledge Description Data Structures and Algorithms (DSA) are fundamental concepts in computer science that enable efficient data handling and problem-solving. Data structures are specific ways of organizing, managing, and storing data to facilitate access and modifications. Algorithms, on the other hand, are well-defined steps or procedures to solve a particular problem. Together, DSA provides the foundation for building efficient software solutions, optimizing performance, and enhancing scalability in computer programs.Algorithms are equally vital, with the primary goal of solving computational problems effectively. Sorting algorithms, such as quicksort, mergesort, and heapsort, organize data systematically, enabling faster searches and optimized storage. Searching algorithms like binary search allow faster look-up times in sorted data by halving the search space with each step, contrasting with linear search's sequential approach. Algorithms are often evaluated by their time and space complexity using Big O notation, which provides a metric for algorithm efficiency. This helps in selecting the optimal algorithm based on resource constraints, ensuring that applications run efficiently even as data scales.Mastering DSA empowers developers to write efficient code, reduce computational bottlenecks, and build scalable applications. As modern computing deals with increasingly vast datasets and complex systems, DSA remains essential for creating programs that are not only functional but also performant. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Types of Data Structures Lecture 3 Floating Point Operations Lecture 4 Get Format Instructions Lecture 5 Model Compression Lecture 6 Model Training Lecture 7 Wrapper Creation Anyone preparing for programming interviews,Software Developers Screenshot Homepage https://www.udemy.com/course/master-data-structures-algorithms-for-developers/ Rapidgator https://rg.to/file/080b92fff78c56f699874fafab61722f/zlbjv.Master.Data.Structures..Algorithms.For.Developers.rar.html Fikper Free Download https://fikper.com/9Ax425xTaI/zlbjv.Master.Data.Structures..Algorithms.For.Developers.rar.html No Password - Links are Interchangeable

Free Download Master Advanced Data Science -Data Scientist AIML Experts TM Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 31h 29m | Size: 15.6 GB Real-World Case Studies and Practical Applications in Data Science What you'll learn Data Science Sessions Part 1 & 2: Understand the foundational methodologies and approaches in data science. Data Science vs Traditional Analysis: Compare modern data science techniques to traditional statistical methods. Data Scientist Journey Parts 1 & 2: Explore the skills, roles, and responsibilities of a data scientist. Data Science Process Overview Parts 1 & 2: Gain insights into the end-to-end data science process. Introduction to Python for Data Science: Learn Python programming for data science tasks and analysis. Python Libraries for Data Science: Master key Python libraries like Numpy, Pandas, and Matplotlib. Introduction to R for Data Science: Get acquainted with R programming for statistical analysis. Data Structures and Functions in Python & R: Handle and manipulate data efficiently using Python and R. Introduction to Data Collection Methods: Understand various data collection techniques, including experimental methods. Data Preprocessing (Parts 1 & 2): Clean and transform raw data to prepare it for analysis. Exploratory Data Analysis (EDA): Detect outliers and anomalies to understand your data better. Data Visualization Techniques: Choose the right visualization methods to represent data insights. Tableau and Data Visualization: Utilize Tableau for advanced data visualization. Inferential Statistics for Hypothesis Testing: Apply inferential statistics to test hypotheses and determine confidence intervals. Introduction to Machine Learning: Learn the fundamentals of machine learning and its applications. Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction): Discover patterns and clusters in unlabeled datasets. Supervised Learning (Regression, Classification, Decision Trees): Build and evaluate predictive models using labeled data. Evaluation Metrics for Regression & Classification: Use various metrics to assess machine learning model performance. Model Evaluation and Validation Techniques: Improve model robustness through bias-variance tradeoffs and validation techniques. Ethical Challenges in Data Science: Address ethical concerns in data collection and model deployment. Requirements Anyone can learn this class it is very simple. Description This comprehensive Data Science Mastery Program is designed to equip learners with essential skills and knowledge across the entire data science lifecycle. The course covers key concepts, tools, and techniques in data science, from basic data collection and processing to advanced machine learning models. Here's what learners will explore:Core Data Science Fundamentals:Data Science Sessions Part 1 & 2 - Foundation of data science methodologies and approaches.Data Science vs Traditional Analysis - Comparing modern data science techniques to traditional statistical methods.Data Scientist Journey Parts 1 & 2 - Roles, skills, and responsibilities of a data scientist.Data Science Process Overview Parts 1 & 2 - An introduction to the step-by-step process in data science projects.Programming Essentials:Introduction to Python for Data Science - Python programming fundamentals tailored for data science tasks.Python Libraries for Data Science - In-depth exploration of key Python libraries like Numpy, Pandas, Matplotlib, and Seaborn.Introduction to R for Data Science - Learning the R programming language basics for statistical analysis.Data Structures and Functions in Python & R - Efficient data handling and manipulation techniques in both Python and R.Data Collection & Preprocessing:Introduction to Data Collection Methods - Understanding various data collection techniques, including experimental studies.Data Preprocessing - Cleaning, transforming, and preparing data for analysis (Parts 1 & 2).Exploratory Data Analysis (EDA) - Detecting outliers, anomalies, and understanding the underlying structure of data.Data Wrangling - Merging, transforming, and cleaning datasets for analysis.Handling Missing Data and Outliers - Techniques to manage incomplete or incorrect data.Visualization & Analysis:Data Visualization Techniques - Best practices for choosing the right visualization method to represent data.Tableau and Data Visualization - Leveraging advanced data visualization software.Inferential Statistics for Hypothesis Testing & Confidence Intervals - Key statistical concepts to test hypotheses.Machine Learning Mastery:Introduction to Machine Learning - Core concepts, types of learning, and their applications.Unsupervised Learning (Clustering, DBSCAN, Dimensionality Reduction) - Discovering patterns in unlabeled data.Supervised Learning (Regression, Classification, Decision Trees) - Building predictive models from labeled data.Evaluation Metrics for Regression & Classification - Techniques to evaluate model performance (e.g., accuracy, precision, recall).Model Evaluation and Validation Techniques - Methods for improving model robustness, including bias-variance tradeoffs.Advanced Topics in Data Science:Dimensionality Reduction (t-SNE) - Reducing complexity in high-dimensional datasets.Feature Engineering and Selection - Selecting the best features for machine learning models.SQL for Data Science - Writing SQL queries for data extraction and advanced querying techniques.Ethical Challenges in Data Science - Understanding the ethical implications in data collection, curation, and model deployment.Hands-on Applications & Case Studies:Data Science in Practice Case Study (Parts 1 & 2) - Real-world data science projects, combining theory with practical implementation.End-to-End Python & R for Data Science - Practical coding exercises to master Python and R in real data analysis scenarios.Working with Data Science Applications - Applying data science techniques in real-world situations.By the end of this program, learners will be equipped to handle end-to-end data science projects, including data collection, cleaning, visualization, statistical analysis, and building robust machine learning models. With hands-on projects, case studies, and a capstone, this course will provide a solid foundation in data science and machine learning, preparing learners for roles as data scientists and AI/ML professionals. Who this course is for Anyone who wants to learn future skills and become Data Scientist, Sr. Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert. Homepage https://www.udemy.com/course/master-advanced-data-science-data-scientist-aiml-experts-tm/ Screenshot Rapidgator https://rg.to/file/09ba300aed59b10d432469af6a27669c/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part12.rar.html https://rg.to/file/0f6b2c994925646af6ae960605a743e1/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part04.rar.html https://rg.to/file/2702cca51860a5730a21e58c6992d3bc/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part16.rar.html https://rg.to/file/362079217c1ad3423f87758eb89a8aa7/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part01.rar.html https://rg.to/file/3aaad8bfcedb2c14ce7df97da31e6024/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part02.rar.html https://rg.to/file/4536c73dfb7da2601e5d0daee2cfd7db/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part17.rar.html https://rg.to/file/4d4dfb560db5a326c4d29f4b53013f1b/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part11.rar.html https://rg.to/file/521341cdf023dc9112d57fc63819b2c1/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part09.rar.html https://rg.to/file/837fcdd1fb7f04495565610627cc0411/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part05.rar.html https://rg.to/file/a3bbf5e08117b839a1d819686a6e97a9/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part15.rar.html https://rg.to/file/a4627887d184e9783af813ef891707d5/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part10.rar.html https://rg.to/file/af874c8934fb5e5d86bd764820f53ffd/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part07.rar.html https://rg.to/file/bada27055a9ea6687b79295b0b769dbb/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part06.rar.html https://rg.to/file/d0ce2aa70e8e140b6b0cdee71c1a22db/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part13.rar.html https://rg.to/file/e10894c08de8a876ed1a4324884efbde/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part08.rar.html https://rg.to/file/f1fe89eb1ba80f9751bb7453dc0d37d8/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part03.rar.html https://rg.to/file/fc984b61c42a0018e16b1fff1ff8de9f/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part14.rar.html Fikper Free Download https://fikper.com/0DCpbiINv4/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part02.rar.html https://fikper.com/0FxWepLF8l/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part09.rar.html https://fikper.com/6LY9h2DafR/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part08.rar.html https://fikper.com/7mttz0MAtH/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part05.rar.html https://fikper.com/ABNpEDFiXZ/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part11.rar.html https://fikper.com/BUnbRf2eZ5/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part17.rar.html https://fikper.com/R52G0FJi4u/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part04.rar.html https://fikper.com/ScpWRz576m/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part10.rar.html https://fikper.com/UyKQoPucAV/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part07.rar.html https://fikper.com/cE7IOjHKtQ/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part12.rar.html https://fikper.com/eEil5XxB1P/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part03.rar.html https://fikper.com/gCDBzYvJnB/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part15.rar.html https://fikper.com/n0ZaqGSr9O/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part06.rar.html https://fikper.com/pHqptX929B/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part13.rar.html https://fikper.com/wsdWfDZujP/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part16.rar.html https://fikper.com/yQkQqzXKad/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part01.rar.html https://fikper.com/yulzha8TH0/mquhf.Master.Advanced.Data.Science.Data.Scientist.AIML.Experts.TM.part14.rar.html No Password - Links are Interchangeable