Python for Data Science

Tools: Jupyter Notebook, Google Colab, Pandas, NumPy, Matplotlib, Seaborn.
Materials: Kaggle datasets, guided data analysis projects, tutorials on data visualization.

Module 1: Python Basics for Data Science (10 Lessons)

This module introduces the foundational concepts of Python programming necessary for data science.

• Introduction to Python and its applications in data science.


• Setting up Python and working with Jupyter Notebook and Google Colab.


• Understanding variables, data types, and operators.


• Control structures: conditional statements and loops.


• Defining and using functions, including lambda functions.


• Working with Python data structures (lists, tuples, dictionaries, sets).


• String manipulation techniques.


• File handling and reading CSV files.


• Introduction to essential Python libraries for data science (NumPy, Pandas, Matplotlib).


• Assessment: Quiz on Python basics and an assignment involving CSV file processing.

Module 2: Data Manipulation and Visualization (10 Lessons)

This module focuses on core data manipulation techniques and visualization tools.

• Working with NumPy arrays: indexing, slicing, and mathematical operations.


• Advanced NumPy features: broadcasting and vectorized operations.


• DataFrames and Series objects in Pandas.


• Data cleaning techniques: handling missing values and data transformation.


• Data aggregation and grouping using Pandas.


• Introduction to Matplotlib: creating line, bar, scatter, and pie charts.


• Advanced Matplotlib: subplots, labels, and exporting visualizations.


• Using Seaborn for statistical data visualization (histograms, box plots, heatmaps).


• Real-world data analysis project using Pandas, Matplotlib, and Seaborn.


• Mid-term assessment with an assignment on data manipulation and visualization.

Module 3: Advanced Data Science Concepts (10 Lessons)

This module introduces more advanced data analysis techniques, machine learning concepts, and real-world projects.

• Statistical analysis using Python: mean, median, variance, and probability distributions.


• Working with time-series data: handling datetime objects in Pandas.


• Introduction to machine learning and Scikit-Learn.


• Supervised learning: regression models and classification algorithms.


• Unsupervised learning: clustering techniques (K-means clustering).


• Introduction to Flask for integrating data science applications with web frameworks (optional).


• Advanced visualization with Plotly for interactive charts and dashboards.


• Capstone project: data cleaning, visualization, and applying machine learning techniques.


• Final presentation of the capstone project with peer and instructor feedback.

Additional Features

• Weekly Assignments: Real-world datasets (e.g., weather data, stock prices) for hands-on practice.


• Quizzes and Tests: Regular quizzes and module-based assessments.


• Discussion and Collaboration: Dedicated forums for sharing insights and asking questions.


• Certification and Portfolio: Upon successful completion, students receive a certificate.


• Portfolio Building: Projects are designed to showcase skills for career advancement.