Data Analysis Using Python

1.0 Introduction and Installations
1.1 Intro to Computer Programming

2.0 Python Basics

2.1 Variables and Datatypes 2.2 Operators
2.3 Program Control Flow 2.4 Iteration

2.5 Functions
2.6 Class
2.7 Python OOP Features 2.8 Exception handling 2.9 File Handling
2.10 Modularization

3.0 NumPy

3.1 Understanding NumPy arrays and vectorization 3.2 Sta-s-cal operations (mean, std, correla-on) 3.3 Array manipulation.

4.0 Pandas and Dataframes

4.1 DataFrame and Series objects
4.2 Loading Data from different sources
4.3 Advanced indexing (loc, iloc) and selection;
4.4 Dataframe methods (isna, drop, length etc)
4.5 Manipula-ng dataframe (Concatena-on, Merging etc)

5.0 Data Cleaning, Transformation, and Feature Engineering

5.1 Handling missing data (fillna, dropna) 5.2 Categorical data encoding
5.3 Data transformation (apply, map)
5.4 Reshaping data

5.5 Feature Engineering
6.0 Data Analysis and Visualization

6.1 Matplotlib
6.2 Seaborn (Statistical visualizations)

6.3 Creating interactive plots with Plotly

7.0 Linear Regression and Time Series

7.1 Linear Regression
7.2 Conducting basic Hypothesis Testing 7.3 Decomposition of time series
7.4 Forecasting

8.0 Data Export and ETL tasks

8.1 Connecting Python to SQL Databases (using sqlalchemy) 8.2 Automating ETL tasks.
8.3 Exporting data in different formats.