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Free Download Udemy - Data Science Methodology Last updated: 6/2022 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz Language: English | Size: 1.07 GB | Duration: 2h 36m Understand steps and tasks needed for designing and building a Data Driven AI engagement What you'll learn Learn to articulate data science process and methodology steps Understand how to analyze data sources Create and validate data science models by applying analytics techniques Explore how users and experts will be engaged for model measurement and monitoring Learn how to apply the methodology on a practice problem Requirements This is an introductory course. There are no pre-requisites. Description Data Science grew through our experiences with Business Intelligence or BI, a field that became popular in 1990s. However, the last 20 years have seen unprecedented improvement in our ability to take actions using Artificial Intelligence. As we adopt the BI methodologies to AI deployments, how will these methodologies morph to add considerations needed for model deployment, and machine learning.Today's Data Science work deals with big data. It introduces three major challenges:How to deal with large volumes of data. Data understanding and data preparation must deal with large scale observations about the population. In the world of BI on small samples, the art of data science was to find averages and trends using a sample and then projecting it using universal population measures such as census to project to the overall population. Most of the big data provides significant samples where such a projection may not be needed. However, bias and outliers become the real issuesData is now available in high velocity. Using scoring engines, we can embed insights into high velocity. Data Science techniques offer significant real-time analytics techniques to make it possible. As you interact with a web site or a product, the marketer or services teams can provide help to you as a user. This is due to insight embedded in high velocity.Most of the data is in speech, unstructured text or videos. This is high variety. How do we interpret an image of a driver license and extract driver license. Understanding and interpreting such data is now a central part of data science.As these deployed models ingest learning in real-time and adjust their models, it is important to monitor their performance for biases and inaccuracies. We need measurement and monitoring that is no longer project-based one-time activity. It is continuous, automated, and closely monitored. The methodology must be extended to include continuous measurement and monitoring.The course describes 7 steps methodology for conducting data science /AI driven engagement.Step 1: Understand Use Case - We use illustrative examples and case studies to show the power of data science engagement and will provide strategies for defining use case and data science objectives.Step 2: Understand Data - We will define various characteristics of big data and how one should go about understanding and selecting right data sources for a use case from data science perspectiveStep 3: Prepare Data - How should one go about selecting, cleaning and constructing big data for data modeling purposes using analytics or AI techniquesStep 4: Develop Model - Once you have ingested structured and un-structured data from many sources, how do you go about building models to gain data insights using AI and AnalyticsStep 5: Evaluate Model - How do you engage users and evaluate decisions? What measurements do you need on models?Step 6: Deploy Model- How do you deploy your AI models and apply learning of AI system from production use for enhancing your model.Step 7: Optimize Model - How would you fine-tune the model and optimize its performance over time using feedback from production use? What guide rails would you need to make sure field use does not result in biases or sabotage.If you are a developer and are interested in learning how to do a data science project using Python, we have designed another course titled "Data Science in Action using Python". The course is must for those embarking on a data science projects for the first time in their organization.,Example: Project managers and IT executives responsible for the data science project execution.,Example: Business analyst who would like to learn about how to incorporate data science in their business analysis.,Example: Developers can use it to get exposure to data science and CRISP-DM methodology and enhancements,There are several possible careers where this course can be used as introductory material, such as Data Scientist, Data, AI or Automation Engineer, Test Engineer Homepage: https://www.udemy.com/course/data-science-methodology/ [b]AusFile[/b] https://ausfile.com/usztatvkmhxa/gkcjr.Data.Science.Methodology.part1.rar.html https://ausfile.com/xnya59n771zc/gkcjr.Data.Science.Methodology.part2.rar.html Rapidgator https://rg.to/file/0fa2344db37502b5453cd6cb1ec64573/gkcjr.Data.Science.Methodology.part1.rar.html https://rg.to/file/d0662d36529e6ba42d30e09eb1fed3ed/gkcjr.Data.Science.Methodology.part2.rar.html Fikper Free Download https://fikper.com/IpSnfLObgy/gkcjr.Data.Science.Methodology.part1.rar.html https://fikper.com/7E8SdpYBLe/gkcjr.Data.Science.Methodology.part2.rar.html No Password - Links are Interchangeable

Free Download Udemy - Research Methodology 2025 Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 2h 20m | Size: 600 MB Types of Research, Main contents of Research Documents, Basic Research Design What you'll learn students can expect to achieve after completing the course to Understand Research Fundamentals :Students will gain a clear understanding of research concepts students can expect to achieve after completing the course to Develop Research Skills: Learners will be able to formulate research problems, develop hypotheses students can expect to achieve after completing the course to Analyze and Interpret Data: Students will learn how to collect, process, and analyze data students can expect to achieve after completing the course to Write and Present Research Findings: Parti[beeep]nts will be able to structure a research report, Requirements Basic understanding of academic writing and critical thinking. Familiarity with fundamental concepts of statistics (for quantitative research) Ability to read and comprehend scholarly articles. Some exposure to research papers or projects (preferred but not mandatory). This course is designed to be accessible for beginners and other intermediate experts. Even if you have no prior research experience, we will guide you through the fundamentals, helping you develop critical research skills step by step. so if you are not it is also helps for all. Description Course Title: Introduction to Research MethodologyCourse Description:This course provides an introduction to the fundamental concepts, techniques, and methods used in research. It is designed to equip students with the essential knowledge and skills to conduct independent research in various fields. The course covers both qualitative and quantitative research methods, emphasizing their application in business and social science research. Students will learn how to formulate research questions, design research studies, collect and analyze data, and present research findings. Additionally, the course explores the ethical considerations involved in conducting research.Course Objectives:Understand the principles and importance of research in academic and professional contexts.Gain knowledge of different research methodologies, including qualitative, quantitative, and mixed methods.Learn how to formulate research questions and hypotheses.Develop skills in designing research studies and selecting appropriate research methods.Master techniques for data collection, analysis, and interpretation.Explore the ethical considerations in research.Gain experience in writing research reports and presenting findings.Course Topics:Introduction to Research and Its ImportanceTypes of Research: Basic vs. Applied ResearchResearch Design: Qualitative, Quantitative, and Mixed MethodsLiterature Review and Research Problem IdentificationSampling Techniques and Data Collection MethodsData Analysis: Statistical Methods and Qualitative AnalysisResearch Ethics and IntegrityWriting Research Proposals and ReportsPresenting Research FindingsAssessment:Research Proposal (25%)Literature Review (20%)Data Collection and Analysis Project(30%)Final Exam (25%) Who this course is for his course is designed for master's level students who seek a solid foundation in research methodology, particularly in the fields of business and applied research. It is ideal for: Graduate students preparing for thesis writing or research projects. Professionals and practitioners looking to enhance their research skills for data-driven decision-making. Academics and aspiring researchers who need to understand research design, data collection, and analysis techniques. Anyone interested in mastering qualitative, quantitative, and mixed-method research approaches for practical application in their respective fields. By the end of the course, learners will be equipped with the critical thinking, analytical, and methodological skills needed to conduct rigorous and impactful research. Homepage: https://www.udemy.com/course/research-methodology-r/ Rapidgator https://rg.to/file/2a1f3740f8605e8299b7803d73f63639/kcyws.Research.Methodology.2025.rar.html Fikper Free Download https://fikper.com/QvNjY45G9O/kcyws.Research.Methodology.2025.rar.html No Password - Links are Interchangeable

Free Download Role of QA in agile methodology Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 32m | Size: 151 MB Introduction to Quality assurance. Role, Report bugs, build test cases What you'll learn Define role of QA in an agile enviroment Give a view of the task as a Quality assurance Report bugs Write test cases Requirements No previous skill is required as this is an introduction Description This course provides an introduction to the role of Quality Assurance (QA) within Agile software development environments. Agile emphasizes iterative, rapid delivery, and QA plays a crucial role in ensuring that the software is continuously tested for quality at every stage of the development process. Parti[beeep]nts will learn how to effectively integrate QA practices into Agile sprints, enabling teams to identify and address issues early in the development cycle.Parti[beeep]nts will explore how QA contributes to cross-functional teams by parti[beeep]ting in daily standups, sprint reviews, and retrospectives to ensure that quality is embedded throughout the process.Additionally, the course covers the essentials of bug reporting, focusing on using tools like Asana to track, prioritize, and communicate defects clearly. Learners will practice writing detailed, actionable bug reports that facilitate quick resolution. Parti[beeep]nts will also develop the skills needed to create comprehensive, well-structured test cases that cover a wide range of scenarios to ensure thorough testing.By the end of this course, attendees will have a solid understanding of QA's role in Agile and be equipped with the basic skills to report bugs effectively and write precise test cases. All these tolls will help the student to start working on an Agile project and fulfill the QA role. Who this course is for The course is ideal for aspiring QA professionals, junior testers, software developers seeking to understand QA, and project managers who want to learn about the testing process. No prior QA experience is necessary. Homepage https://www.udemy.com/course/role-of-qa-in-agile-methodology/ Rapidgator https://rg.to/file/c4d5ed1ca99f35bd9b2ca8a397dcf18b/eacpu.Role.of.QA.in.agile.methodology.rar.html Fikper Free Download https://fikper.com/FsGeEESib1/eacpu.Role.of.QA.in.agile.methodology.rar.html No Password - Links are Interchangeable

Free Download Network Troubleshooting - Troubleshooting Methodology and Tools for CompTIA Network+ Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 49m | Size: 398 MB Data networks are complex and require strong troubleshooting skills to keep them running. This course will teach you the foundations of troubleshooting as well as tools you can use to troubleshoot cabling and configurations. The purpose of a data network is to move information so user applications function properly. The demands of a data network change as the applications evolve, which can often lead to issues which require troubleshooting. In this course, Network Troubleshooting: Troubleshooting Methodology and Tools for CompTIA Network+, you'll learn troubleshooting methodology and tools. First, you'll explore a framework for troubleshooting. Next, you'll discover how to evaluate and troubleshoot cabling issues. Finally, you'll learn how to use command line tools to troubleshoot possible configuration issues on devices. When you're finished with this course, you'll have the skills and knowledge of troubleshooting data networks needed to isolate and evaluate common network issues. Homepage https://app.pluralsight.com/library/courses/network-troubleshooting-methodology-tools-comptia-network-plus-cert/table-of-contents TakeFile https://takefile.link/qv0z33hcbxl5/ywrpl.Network.Troubleshooting.Troubleshooting.Methodology.and.Tools.for.CompTIA.Network.rar.html Rapidgator https://rg.to/file/21fa2412e7e61d0c492e06c02a314531/ywrpl.Network.Troubleshooting.Troubleshooting.Methodology.and.Tools.for.CompTIA.Network.rar.html Fikper Free Download https://fikper.com/TKNOy2sATz/ywrpl.Network.Troubleshooting.Troubleshooting.Methodology.and.Tools.for.CompTIA.Network.rar.html No Password - Links are Interchangeable

SystemVerilog Functional Coverage Language Methodology Apps MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | Lec: 9 | 400 MB Genre: eLearning | Language: English Step-by-step overview of SystemVerilog Functional Coverage features, syntax/semantics, methodology/apps FROM SCRATCH The knowledge gained from this course will help you cover those critical and hard to find design bugs. SystemVerilog Functional Coverage Language and Methodology is a very important part of overall functional verification methodology and all verification engineers need this knowledge to be successful. The knowledge of FC will indeed be a highlight of your resume when seeking a challenging job or project The course offers step-by-step guide to learning of FC with real life applications to help you solidify your concepts and apply FC to your project in shortest possible time. FC helps the critical part of Functional/Temporal domain coverage which is simply not possible with code coverage.The course does not require any prior knowledge of SystemVerilog or OOP (Object oriented programming) or UVM. The course has 9 lectures that will take you step by step through FC language from scratch. Download link: http://rapidgator.net/file/f2ccd0ff826012c91b3b8836100bf479/nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar.html]nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar.html http://nitroflare.com/view/2948EA4D86CF7D9/nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar]nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar http://uploaded.net/file/skxqw2mq/nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar]nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar https://www.bigfile.to/file/Wc7NN9SncMrb/nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar]nug56.SystemVerilog.Functional.Coverage.Language.Methodology.Apps.rar Links are Interchangeable - No Password - Single Extraction

SystemVerilog Functional Coverage Language Methodology Apps MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | Lec: 9 | 400 MB Genre: eLearning | Language: English Step-by-step overview of SystemVerilog Functional Coverage features, syntax/semantics, methodology/apps FROM SCRATCH The knowledge gained from this course will help you cover those critical and hard to find design bugs. SystemVerilog Functional Coverage Language and Methodology is a very important part of overall functional verification methodology and all verification engineers need this knowledge to be successful. The knowledge of FC will indeed be a highlight of your resume when seeking a challenging job or project The course offers step-by-step guide to learning of FC with real life applications to help you solidify your concepts and apply FC to your project in shortest possible time. FC helps the critical part of Functional/Temporal domain coverage which is simply not possible with code coverage.The course does not require any prior knowledge of SystemVerilog or OOP (Object oriented programming) or UVM. The course has 9 lectures that will take you step by step through FC language from scratch. DOWNLOAD http://rapidgator.net/file/cb685d069ace5cafac454bf101617b1f/4vado.SystemVerilog.Functional.Coverage.Languagemethodologyapps.rar.html http://uploaded.net/file/fekykxnv/4vado.SystemVerilog.Functional.Coverage.Languagemethodologyapps.rar https://www.bigfile.to/file/tmrCevkY8JNq/4vado.SystemVerilog.Functional.Coverage.Languagemethodologyapps.rar http://nitroflare.com/view/AF1B6F9CE39CAE9/4vado.SystemVerilog.Functional.Coverage.Languagemethodologyapps.rar http://uploadgig.com/file/download/93B9719df12ae832/4vado.SystemVerilog.Functional.Coverage.Languagemethodologyapps.rar

Research Methodology and Fundamentals of Market Research English | .MP4, AVC, 1000 kbps, 1280x720 | AAC, 64 kbps, 2 Ch | 136 Mb Genre: eLearning This training is about demystifying research and research methods and learning about market research. This course is about interpreting research and research methods. It will summarize the fundamentals of doing research. This course will appeal to those of you who require an understanding of research approaches and skills, and importantly an capability to use them in your studies or in your professional lives. Through this course we are going to understand the relevance & scope of research in management, the detailed process/steps that are involved in research. It involves understanding how to identify a research problem, defining the MR problems and preparing the research design. It will deal with management of data in relation to research which involves data collection methodology, primary data - collection methods / measurement, techniques, characteristics of measurement techniques, reliability, validity etc. and secondary data collection. We will dig into the various methods used for research such as library research, references - bibliography, abstracts, etc. Next would be understanding the primary and secondary data sources and data collection instruments including in-dept. interviews, projective techniques and focus groups, data management plan - sampling & measurement, data analysis - tabulation, SPSS applications data base, testing for association, analysis techniques and finally research report writing. In particular, this training will assist those of you who have to conduct research but do not perhaps have access to research methods courses, or for those of you who feel you would like extra support for self-improvement. No prior knowledge or experience in research is required to take this course and as such, the course is for everyone. DOWNLOAD http://rapidgator.net/file/ed43956b486d1f9532d37894238c0ab8/Research.part1.rar.html http://rapidgator.net/file/2fd790fbdf2aadb21bf221f6c3af59e2/Research.part2.rar.html http://uploaded.net/file/pofo39iu/Research.part1.rar http://uploaded.net/file/rez3me8j/Research.part2.rar http://www.uploadable.ch/file/SW6fuWHNGp2n/Research.part1.rar http://www.uploadable.ch/file/Ufg6XmnJ2UEQ/Research.part2.rar http://www.hitfile.net/2Utu/Research.part1.rar.html http://www.hitfile.net/2TAa/Research.part2.rar.html