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—- data course —- Code: 3010 Title: Programming for Data Analysis English Title: Programming for Data Analysis Level: MicroMaster Specialization: Machine Learning Credits: 3 Type: Theoretical Category: Specialized Priority: 13 Prerequisite: Objective Co-requisite: – Image: data.png ID: 3010 Instructor: Education Term: Summer 2025 Schedule: Sundays, 17:00–20:00

General Objective

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

Topics

  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

Assessment

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

References

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