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Data Science Fundamentals
Introduction to Data & Information
Probability and Probability Distribution
1. Introduction to Probability
Python Programming
1. Introduction to Python Programming
R Programming
1. Introduction to R Programming
2. Features and Advantages of R Programming
3. Why Use R for Data Science and Statistics
4. Installing R and RStudio – Step‑by‑Step Guide
5. R vs Python for Data Science:
6. Tidyverse Package for Data Wrangling
7. ggplot2 Package for High-Quality Graphics in R
8. Install gglot2 and tidyverse
9. Key R & Data Science Keywords
10. Introduction to Data Types and Data Structures in R
11. Everything in R is an Object
12. Fundamental Data Types in R
13. Basic Syntax of R
14. Understanding Data Structures in R Programming
15. Functions on Vector Objects in R
16. Accessing and modifying element of a vector
17. Identifying and Handling Missing Data in R
18. Inf NaN and List in R Programming
19. Lists in R
20. Creation of Matrix
21. Access Data
Statistical Inference
1. Sampling and Sampling Distribution
2. Estimates and Estimators
3. Why Interval Estimation Is Essential in Statistics
4. Properties of Point Estimators: Unbiasedness, Consistency, Efficiency, and Sufficiency
Mathematics
Matrices
Chapter 1 – Algebra of Matrices
Personal Academic & Career Mentorship
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