Programming for Data Analysis

The goal of this course is to master the Python programming language and use it for storing, analyzing, and visualizing data.

1. Review of Python Programming (2 sessions)
   • Working with Python in IPython and Jupyter interactive environments
   • Modular programming and use of libraries
   • Generating random numbers and Monte Carlo simulation
2. Data Storage and Handling (5 sessions)
   • Data storage structures
   • Organizing data using dataframes
   • Relational and non-relational databases, and data warehouses
   • Data manipulation with Pandas
3. Data Preparation (2 sessions)
   • Data formatting, normalization, and binning
   • Filling in missing data
4. Data Analysis (5 sessions)
   • Understanding data distribution
   • Creating data pipelines
   • Applying analysis techniques to real datasets using Numpy and Scipy libraries
5. Data Visualization and Charting (5 sessions)
   • Exploratory data analysis
   • Plotting in Python using Matplotlib, Seaborn, and Plotly libraries
   • Various data visualization techniques
   • Important considerations for effective data visualization

• Exercises: 20%
• Quizzes: 20%
• Final Exam: 60%

1. Wes McKinney. Python for Data Analysis: Data Wrangling with pandas. NumPy, and Jupyter, 3rd Edition, 2022.