Overview of Python programming

Setting up the Python environment (Anaconda, Jupyter Notebook, and IDEs)

Basic Python syntax (variables, data types, loops, and conditionals)

Functions and modules in Python

Introduction to Python libraries for data analysis (NumPy, Pandas, Matplotlib)

Introduction to NumPy

Creating and manipulating NumPy arrays

Array operations and broadcasting

Statistical functions with NumPy

Introduction to Pandas and data frames

Loading and reading datasets (CSV, Excel, JSON, databases)

Exploratory data analysis (EDA): filtering, sorting, and summarizing data

Data cleaning: handling missing values, duplicates, and data transformations

Merging and joining datasets

Introduction to data visualization

Creating plots with Matplotlib: line plots, bar charts, histograms, scatter plots

Data visualization with Seaborn: box plots, heatmaps, pair plots, and more

Customizing visualizations (labels, titles, and themes)

Understanding statistical concepts: mean, median, mode, variance, and standard deviation

Data distributions and probability

Correlation and regression analysis

Hypothesis testing

Case study 1: Analyzing a sales dataset

Case study 2: Understanding customer behavior through data

Project: Analyzing a dataset of your choice

Overview of machine learning concepts

Introduction to scikit-learn library

Building a simple predictive model: linear regression and classification