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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 Application of Data Science for Data Scientists - AIML TM Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 8h 32m | Size: 4.06 GB Mastering Real-World Data Science Applications and Techniques for Advanced Problem Solving What you'll learn Students will learn the fundamentals of Data Science and its applications across various industries. Students will explore key algorithms and perform exploratory data analysis (EDA). Students will learn about the roles, skills, and responsibilities of a Data Scientist. Students will dive into advanced techniques and practical applications used by Data Scientists. Students will learn the stages of the Data Science process, from problem definition to data collection. Students will explore model building, evaluation, deployment, and post-deployment strategies. Students will apply Data Science concepts to solve a real-world case study from start to finish. Students will learn how to ensure data quality and make their models interpretable. Students will explore the ethical considerations and responsibilities involved in Data Science. Students will examine the ethical dilemmas surrounding data collection, privacy, and bias. Students will understand how to manage and execute a Data Science project from planning to reporting. Students will learn techniques for selecting and engineering relevant features to improve model performance. Students will explore how to implement and scale Data Science solutions in real-world applications. Students will master data wrangling and manipulation techniques to efficiently handle large datasets. Requirements Anyone can learn this class it is very simple. Description 1. Introduction to Data ScienceOverview of what Data Science isImportance and applications in various industriesKey components: Data, Algorithms, and InterpretationTools and software commonly used in Data Science (e.g., Python, R)2. Data Science Session Part 2Deeper dive into fundamental conceptsKey algorithms and how they workExploratory Data Analysis (EDA) techniquesPractical exercises: Building first simple models3. Data Science Vs Traditional AnalysisDifferences between traditional statistical analysis and modern Data ScienceAdvantages of using Data Science approachesPractical examples comparing both approaches4. Data Scientist Part 1Role of a Data Scientist: Core skills and responsibilitiesKey techniques a Data Scientist uses (e.g., machine learning, data mining)Introduction to model building and validation5. Data Scientist Part 2Advanced techniques for Data ScientistsWorking with Big Data and cloud computingBuilding predictive models with real-world datasets6. Data Science Process OverviewSteps of the Data Science process: Problem definition, data collection, preprocessingBest practices in the initial phases of a Data Science projectExamples from industry: Setting up successful projects7. Data Science Process Overview Part 2Model building, evaluation, and interpretationDeployment of Data Science models into productionPost-deployment monitoring and iteration8. Data Science in Practice - Case StudyHands-on case study demonstrating the Data Science processProblem-solving with real-world dataStep-by-step guidance from data collection to model interpretation9. Data Science in Practice - Case Study: Data Quality & Model InterpretabilityImportance of data quality and handling missing dataTechniques for ensuring model interpretability (e.g., LIME, SHAP)How to address biases in your model10. Introduction to Data Science EthicsImportance of ethics in Data ScienceHistorical examples of unethical Data Science practicesGuidelines and frameworks for ethical decision-making in Data Science11. Ethical Challenges in Data Collection and CurationChallenges in ensuring ethical data collection (privacy concerns, data ownership)Impact of biased or incomplete dataHow to approach ethical dilemmas in practice12. Data Science Project LifecycleOverview of a complete Data Science project lifecycleManaging each phase: Planning, execution, and reportingTeam collaboration and version control best practices13. Feature Engineering and SelectionTechniques for selecting the most relevant featuresDimensionality reduction techniques (e.g., PCA)Practical examples of feature selection and its impact on model performance14. Application - Working with Data ScienceHow to implement Data Science solutions in real-world applicationsCase studies of successful applications (e.g., fraud detection, recommendation systems)Discussion on the scalability and robustness of models15. Application - Working with Data Science: Data ManipulationTechniques for data wrangling and manipulationWorking with large datasets efficientlyUsing libraries like Pandas, NumPy, and Dask for data manipulationThis framework covers key aspects and ensures a deep understanding of Data Science principles with practical applications. Who this course is for Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert. 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Free Download Python for Engineers, Scientists and Analysts Published 9/2024 Created by Harry Munro MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 11 Lectures ( 1h 36m ) | Size: 789 MB Introduction to Data Analysis, Statistics, Modelling and Visualisation What you'll learn: Learn how to install and run Python on your computer Write and execute basic Python scripts. Import data from CSV files and other formats. Utilise libraries such as Pandas for data manipulation and exploration. Apply statistical methods to analyse data (e.g., mean, median, standard deviation). Calculate and interpret descriptive statistics using Python libraries. Apply advanced statistical measures such as percentiles and interquartile range to gain deeper insights into data variability. Create new data using mathematical and statistical models. Simulate real-world scenarios using models in Python. Generate plots and visualisations using libraries like Matplotlib and Seaborn. Create informative charts, graphs, and plots to summarise data. Write functions for repetitive tasks in data analysis. Understand the concept of modular programming and apply it to real-world problems. Solve engineering, scientific, and analytical problems using Python. Integrate data analysis and visualisation techniques to extract insights from data. Present data analysis results through clear, professional visualisations. Use Python to automate processes and produce immediate, actionable insights. Requirements: Basic Computer Literacy: Learners should be comfortable using a computer, navigating files, and installing software. Curiosity and a Willingness to Learn: No prior programming experience is required! This course is designed to take beginners through the foundations of Python and data analysis step by step. (Optional) Basic Understanding of Mathematics and Statistics: While not required, familiarity with basic statistical concepts (e.g., mean, median, and standard deviation) will help learners get the most out of the course, particularly when we explore data analysis and modelling. No Prior Python Experience Required! This course is ideal for beginners, and we'll cover everything you need to know from scratch. If you're new to programming, don't worry-this course is designed to make Python accessible and enjoyable. Description: Unlock the power of Python to address real-world challenges in engineering, science, and data analysis with efficiency and precision. This course is designed to provide you with the essential skills needed to apply Python in practical, industry-relevant contexts-whether you're a professional seeking to automate workflows, a student aiming to enhance your data analysis capabilities, or simply interested in using Python to streamline complex tasks.Developed for learners of all levels, this course provides clear, structured guidance on the core concepts of Python. Through hands-on exercises and real-world examples, you will learn how to import, clean, and manipulate data, perform key statistical analyses, build mathematical models, and create professional visualisations. The lessons are concise and focused, ensuring you can acquire the knowledge and skills you need efficiently.While covering fundamental Python programming and data analysis techniques, this course is intentionally designed to avoid more advanced topics such as machine learning, object-oriented programming, and complex Boolean operations-allowing you to focus on practical skills that can be applied immediately.What You'll Learn:How to install Python and set up your programming environment efficientlyImporting, cleaning, and manipulating datasets using libraries such as PandasPerforming advanced statistical analysis, including outlier detection, percentiles, and interquartile rangesVisualising data using professional charts and graphs with Matplotlib and SeabornBuilding mathematical models for data simulation and predictionWriting efficient, reusable Python code to solve real-world problemsApplying Python to fields such as engineering, finance, manufacturing, and scientific researchWhy Take This Course?Practical and Immediate Value: The course is designed to deliver skills that can be applied immediately in your professional or academic work.Hands-on Learning: Engage with interactive coding exercises, downloadable code, and real-world projects that enable you to apply what you learn.Accessible for All Levels: Whether you are new to Python or seeking to refine your skills, this course is structured to accommodate learners at all levels.High-Quality Content: Enjoy well-organised lessons, quizzes, and downloadable resources that facilitate effective learning.What This Course Does NOT Cover: This course provides a strong foundation in Python for data analysis, modelling, and visualisation, but it does not cover the following advanced topics:Iterables and GeneratorsAdvanced Boolean OperationsObject-Oriented Programming (OOP)Machine Learning or Deep LearningAdvanced Big Data Libraries such as Dask or PySparkConcurrency and ParallelismAdvanced simulation techniques such as discrete-event simulation or agent-based modellingThis course is designed to equip you with immediate, actionable skills that can be applied directly to real-world challenges. 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Advanced SQL for Data Scientists Size: 172 MB | Duration: 1h 24m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch Genre: eLearning | Level: Advanced | Language: English There is an increasing need for data scientists and analysts to understand relational data stores. Organizations have long used SQL databases to store transactional data as well as business intelligence related data. If you need to work with SQL databases, this course is designed to help you learn how to perform common data science tasks, including finding, exploration, and extraction within relational databases. You can also learn how to prepare data for use in analytics tools such as SAS, R, and Python. The course begins with a brief overview of SQL. Then the five major topics a data scientist should understand when working with relational databases: basic statistics in SQL, data preparation in SQL, advanced filtering and data aggregation, window functions, and preparing data for use with analytics tools. Topics include: * Data manipulation * ANSI standards * SQL and variations * Statistical functions in SQL * String, numeric, and regular expression functions in SQL * Advanced filtering techniques * Advanced aggregation techniques * Windowing functions for working with ordered data sets * Best practices for preparing data for analytics tools Download From NitroFlare http://nitroflare.com/view/AF9C9BDF795C92E/xidau123_LyndaAdvancedSQLforDataScientists.rar Download From Rapidgator http://rapidgator.net/file/97a56ece96a311f567fb47e487efcd41/xidau123_LyndaAdvancedSQLforDataScientists.rar.html Download From UploadGig https://uploadgig.com/file/download/c6eA7fc16Ad74Bf6/xidau123_LyndaAdvancedSQLforDataScientists.rar

Lynda - Advanced SQL for Data Scientists Size: 172 MB | Duration: 1h 24m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch Genre: eLearning | Level: Advanced | Language: English There is an increasing need for data scientists and analysts to understand relational data stores. There is an increasing need for data scientists and analysts to understand relational data stores. Organizations have long used SQL databases to store transactional data as well as business intelligence related data. If you need to work with SQL databases, this course is designed to help you learn how to perform common data science tasks, including finding, exploration, and extraction within relational databases. You can also learn how to prepare data for use in analytics tools such as SAS, R, and Python. The course begins with a brief overview of SQL. Then the five major topics a data scientist should understand when working with relational databases: basic statistics in SQL, data preparation in SQL, advanced filtering and data aggregation, window functions, and preparing data for use with analytics tools. * Data manipulation * ANSI standards * SQL and variations * Statistical functions in SQL * String, numeric, and regular expression functions in SQL * Advanced filtering techniques * Advanced aggregation techniques * Windowing functions for working with ordered data sets * Best practices for preparing data for analytics tools Download link: http://rapidgator.net/file/784c389fc3da9a614464ad8fc09769e1/ila9u.Lynda..Advanced.SQL.for.Data.Scientists.rar.html http://nitroflare.com/view/37872B3D1A50826/ila9u.Lynda..Advanced.SQL.for.Data.Scientists.rar https://uploadgig.com/file/download/5aDf265846698943/ila9u.Lynda..Advanced.SQL.for.Data.Scientists.rar http://uploaded.net/file/wx7ztzhq/ila9u.Lynda..Advanced.SQL.for.Data.Scientists.rar Links are Interchangeable - No Password - Single Extraction

Lynda - Tableau 10 for Data Scientists Size: 349 MB | Duration: 2h 4m | Video: AVC (.mp4) 1280x720 15&30fps | Audio: AAC 48KHz 2ch Genre: eLearning | Level: Intermediate | Language: English Tableau is designed for data science! Move beyond the basics and delve deeper into the power of this data visualization software. Tableau is designed for data science! Move beyond the basics and delve deeper into the power of this data visualization software. Learn how to deal with messy or badly formatted data, use Tableau to answer key data analytics questions, and visualize your results with maps and dashboards. Tableau-certified "Zen Master" Matt Francis will show you how to use parameters to enhance visualizations, create cross-source filters, use data extracts to optimize slow connections, and much more. The training starts with one of the most important features in Tableau: the difference between the green and blue pills (discrete and continuous data) and how this affects every single action Tableau performs. Then find out how to add new maps and create more effective dashboards that maximize screen real estate. Discover how actions can link together sheets and provide greater levels of interactivity and performance, and how formatting can make an ordinary dashboard demand attention. Plus, get some bonus tips on performing date and time calculations in Tableau. This course deep-dives into the practical, applicable, and essential skills that anyone doing data visualization and analytics in a professional setting needs to have. * Green vs. blue pills * Using filters, colors, and dates * Connecting to data * Extracting data * Cleaning and prepping data * Pivoting data * Merging and joining data * Highlighting data * Using the Analytics pane * Creating new maps * Creating calculations based on parameters * Designing dashboards DOWNLOAD http://rapidgator.net/file/94b99301b93c4bc0df3e283298dccca2/gf67t.Lynda..Tableau.10.for.Data.Scientists.rar.html https://bytewhale.com/0z08bdtlimw3/gf67t.Lynda..Tableau.10.for.Data.Scientists.rar http://uploaded.net/file/rb17pmd2/gf67t.Lynda..Tableau.10.for.Data.Scientists.rar https://www.bigfile.to/file/kzxUcYhjgZd7/gf67t.Lynda..Tableau.10.for.Data.Scientists.rar http://nitroflare.com/view/965F08930FAB8B8/gf67t.Lynda..Tableau.10.for.Data.Scientists.rar http://uploadgig.com/file/download/e83aA63f5Df7557f/gf67t.Lynda..Tableau.10.for.Data.Scientists.rar

O'reilly - Hadoop Fundamentals for Data Scientists English | 6 hrs 5 mins | .MP4, AVC, 3449 kbps, 1920x1080 | AAC, 128 kbps, 2 Ch | 7.95 gb Genre: eLearning Hadoop's Architecture, Distributed Computing Framework, and Analytical Ecosystem Get a practical introduction to Hadoop, the framework that made big data and large-scale analytics possible by coMbining distributed computing techniques with distributed storage. In this video tutorial, hosts Benjamin Bengfort and Jenny Kim discuss the core concepts behind distributed computing and big data, and then show you how to work with a Hadoop cluster and program analytical jobs. You'll also learn how to use higher-level tools such as Hive and Spark. Hadoop is a cluster computing technology that has many moving parts, including distributed systems administration, data engineering and warehousing methodologies, software engineering for distributed computing, and large-scale analytics. With this video, you'll learn how to operationalize analytics over large datasets and rapidly deploy analytical jobs with a variety of toolsets. Once you've completed this video, you'll understand how different parts of Hadoop coMbine to form an entire data pipeline managed by teams of data engineers, data programmers, data researchers, and data business people. Understand the Hadoop architecture and set up a pseudo-distributed development environment Learn how to develop distributed computations with MapReduce and the Hadoop Distributed File System (HDFS) Work with Hadoop via the command-line interface Use the Hadoop Streaming utility to execute MapReduce jobs in Python Explore data warehousing, higher-order data flows, and other projects in the Hadoop ecosystem Learn how to use Hive to query and analyze relational data using Hadoop Use summarization, filtering, and aggregation to move Big Data towards last mile computation Understand how analytical workflows including iterative machine learning, feature analysis, and data modeling work in a Big Data context Benjamin Bengfort is a data scientist and programmer in Washington DC who prefers technology to politics but sees the value of data in every domain. Alongside his work teaching, writing, and developing large-scale analytics with a focus on statistical machine learning, he is finishing his PhD at the University of Maryland where he studies machine learning and artificial intelligence. Jenny Kim, a software engineer in the San Francisco Bay Area, develops, teaches, and writes about big data analytics applications and specializes in large-scale, distributed computing infrastructures and machine-learning algorithms to support recommendations systems. DOWNLOAD http://rapidgator.net/file/07f80eaafae06cc09f62aa6b9fe3522d/Hadoop.part1.rar.html http://rapidgator.net/file/6f5025e9217a586d4c29bf699b6d47d6/Hadoop.part2.rar.html http://rapidgator.net/file/ee9b0b9682aac8647599415c7cbb1dea/Hadoop.part3.rar.html http://rapidgator.net/file/393c6cbbf887d3be2be721ff070858b3/Hadoop.part4.rar.html http://rapidgator.net/file/993a2a6b96c2e0e154ca802cd6c2973b/Hadoop.part5.rar.html http://rapidgator.net/file/79a9e1e1695987cb81f87dea2849e015/Hadoop.part6.rar.html http://rapidgator.net/file/aca4a4cf53532ebdba8059c5ec421efd/Hadoop.part7.rar.html http://rapidgator.net/file/29217eced3ae3cd4189377f339c0224f/Hadoop.part8.rar.html http://uploaded.net/file/q9e0a9xf/Hadoop.part1.rar http://uploaded.net/file/wruf0r11/Hadoop.part2.rar http://uploaded.net/file/b30sbk2x/Hadoop.part3.rar http://uploaded.net/file/8y1oodzy/Hadoop.part4.rar http://uploaded.net/file/jdwniztt/Hadoop.part5.rar http://uploaded.net/file/xx2eryor/Hadoop.part6.rar http://uploaded.net/file/agq0fgr9/Hadoop.part7.rar http://uploaded.net/file/sfkg86jt/Hadoop.part8.rar http://www.uploadable.ch/file/bXR3WyJPJ2mE/Hadoop.part1.rar http://www.uploadable.ch/file/mBBbTdh4WseV/Hadoop.part2.rar http://www.uploadable.ch/file/EqUmVcqaHqkU/Hadoop.part3.rar http://www.uploadable.ch/file/nxExDAkj56zx/Hadoop.part4.rar http://www.uploadable.ch/file/mPYGhJczwWtb/Hadoop.part5.rar http://www.uploadable.ch/file/spEJ2QuQEpza/Hadoop.part6.rar http://www.uploadable.ch/file/UVUaUJENHqcy/Hadoop.part7.rar http://www.uploadable.ch/file/pf6hyAUmExxb/Hadoop.part8.rar