What you'll learn:
R Programming Language for Data Scientists (Data Science)
Data Science Session 1
Data Science Session 2
Data Science Process Overview
Data Scientist
Data Scientist AIML End to End
Data Science Process Overview
Data Science Process Overview End to End AIML
Introduction to R for Data Science
R Programming Basics AIML End to End
R Programming Part 2
Requirements:
Anyone can learn this class it is very simple.
Description:
1. R Programming Language for Data Scientists (Data Science)Overview:-This course introduces the R programming language specifically tailored for Data Science applications. It covers the fundamentals of R and its application in data analysis, visualization, and machine learning.Learning Outcomes:-Master the basics of R programming.Apply R in data manipulation, visualization, and modeling.2. Data Science Session 1Overview:-The first session introduces core concepts of Data Science, including data collection, preprocessing, and exploration.Learning Outcomes:-Understand the foundational concepts of Data Science.Learn how to collect and prepare data for analysis.3. Data Science Session 2Overview:-This session delves deeper into data analysis techniques and introduces the basics of statistical modeling.Learning Outcomes:-Explore advanced data analysis techniques.Begin working with statistical models in Data Science.4. Data Science Process OverviewOverview:-Provides a comprehensive overview of the Data Science process, from data collection to model deployment.Learning Outcomes:-Gain a holistic understanding of the Data Science workflow.Learn about each stage of the Data Science process.5. Data ScientistOverview:-Focuses on the role of a Data Scientist, covering key skills, tools, and methodologies used in the field.Learning Outcomes:-Understand the responsibilities and skillset of a Data Scientist.Get acquainted with essential tools and techniques.6. Data Scientist AIML End to EndOverview:-Explores the end-to-end process of applying Artificial Intelligence and Machine Learning in Data Science projects.Learning Outcomes:-Learn how to integrate AI and ML techniques in Data Science workflows.Complete an end-to-end AIML project.7. Data Science Process OverviewOverview:-Another overview focused on reinforcing the understanding of the Data Science process.Learning Outcomes:-Solidify your understanding of the Data Science lifecycle.Review key concepts and stages in the process.8. Data Science Process Overview End to End AIMLOverview:-This session provides a detailed walkthrough of the entire Data Science process with an emphasis on AIML integration.Learning Outcomes:-Master the end-to-end Data Science process.Apply AIML techniques to real-world Data Science problems.9. Introduction to R for Data ScienceOverview:-Introduces R programming with a focus on its application in Data Science, including data manipulation and visualization.Learning Outcomes:-Get started with R programming for Data Science.Learn to use R for basic data analysis tasks.10. R Programming Basics AIML End to EndOverview:-Covers the basic syntax and structures of R, with a focus on applying them in AIML contexts.Learning Outcomes:-Learn the fundamentals of R programming.Apply R in basic AIML tasks and projects.11. R Programming Part 2Overview:-This section builds on the basics, introducing more advanced R programming techniques, including data wrangling and modeling.Learning Outcomes:Develop advanced R programming skills.Implement complex data wrangling and modeling tasks using R.
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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