1. Python Basics
Build a solid foundation in Python programming. This module covers everything you need to get started writing clean, effective Python code.
- Setting up your Python environment (Anaconda, Jupyter Notebooks)
- Variables, data types, and operators
- Control flow: conditionals and loops
- Functions, scope, and modules
- File I/O and error handling
2. Data Structures
Master Python's built-in data structures and learn how to choose the right one for each task.
- Lists, tuples, and sets
- Dictionaries and nested structures
- List comprehensions and generator expressions
- Sorting, filtering, and transforming data collections
3. NumPy & Pandas
Dive into the two most essential libraries for data science in Python — NumPy for numerical computing and Pandas for data manipulation.
- NumPy arrays: creation, indexing, and broadcasting
- Mathematical and statistical operations with NumPy
- Pandas Series and DataFrames
- Loading, cleaning, and transforming datasets
- Merging, grouping, and aggregating data
4. Data Visualization with Matplotlib
Learn to communicate insights visually by creating compelling charts and plots using Matplotlib and Seaborn.
- Line plots, bar charts, histograms, and scatter plots
- Customizing figures: titles, labels, legends, and styles
- Subplots and multi-panel figures
- Introduction to Seaborn for statistical visualizations
5. Exploratory Data Analysis (EDA)
Develop a systematic approach to understanding new datasets before modeling — the most critical skill in any data scientist's toolkit.
- Summarizing datasets: shape, types, and descriptive statistics
- Identifying and handling missing values and outliers
- Correlation analysis and feature relationships
- EDA workflow on real-world datasets
6. Introduction to Machine Learning with Scikit-learn
Get hands-on with machine learning fundamentals using Scikit-learn, the industry-standard Python ML library.
- Supervised vs. unsupervised learning concepts
- Train/test splits and cross-validation
- Linear regression and classification algorithms
- Model evaluation: accuracy, precision, recall, and F1-score
- Feature engineering and preprocessing pipelines
7. Final Project
Apply everything you've learned in a comprehensive end-to-end data science project. You'll work with a real-world dataset, perform full EDA, build and evaluate a machine learning model, and present your findings.
- Choosing and scoping a real-world dataset
- End-to-end data pipeline: ingestion, cleaning, and transformation
- Building and tuning a predictive model
- Visualizing and communicating results with a final report