Fundamentals of data science

 Module-I (Introduction)

1. What is Data science?
2. The Data science process
   
3. A data scientist role in this process
4. NumPy Basics:
5. A Multidimensional Array Object
6. Creating ndarrays
7. Data Types for ndarrays
8. Operations between Arrays and Scalars
9. Basic Indexing and Slicing
10. Boolean Indexing
11. Fancy Indexing
12. Data Processing Using Arrays 

Module-II (Getting Started with pandas)

1. Introduction to pandas
2. Library Architecture,-Features
3. Applications
4. Data Structures(Series, DataFrame, Index Objects)
5. Essential Functionality
6. Sorting and ranking
7. Summarizing and Computing Descriptive Statistics
8.  Handling Missing Data

Module-III (Data Loading, Storage, and File Format)

1. Reading and Writing Data in Text Format
2. JSON Data
3. Binary Data Formats
   
4. Interacting with HTML and Web APIs

Module-IV (Data Wrangling)

1. Combining and Merging Data Sets
2. Reshaping and Pivoting
3. Data Transformation

Module-V (Plotting and Visualization)

1. A Brief matplotlib API Primer
2. Plotting Functions in pandas

Module-VI (Data Aggregation and Group Operations)

1. GroupBy Mechanics
2. Data Aggregation
3. Group-wise Operations and Transformations