R Programming Language for Data Scientists (Data Science) TM

About R Programming Language for Data Scientists (Data Science) TM

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 1

Overview:-

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 2

Overview:-

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 Overview

Overview:-

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 Scientist

Overview:-

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 End

Overview:-

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 Overview

Overview:-

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 AIML

Overview:-

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 Science

Overview:-

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 End

Overview:-

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 2

Overview:-

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.