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.
