Consortium for International Studies | Evaluated Learning Experience
Data Analysis Using Python CIS 205
Varies (self-study, self-paced).
April 2025 - Present.
Upon successful completion of the course, students will be able to: implement Python programming techniques and work with key libraries for data analysis; use NumPy for numerical computing, array manipulation, and vectorized operations; apply pandas for data handling, cleaning, and transformation of structured datasets; load, store, and process data from various sources, including text, CSV, JSON, and databases; perform data wrangling, merging, and reshaping operations for effective data manipulation; create visualizations using Matplotlib and Seaborn to present data insights; analyze time series data and implement aggregation and group operations; and apply introductory statistical and machine learning techniques to model and interpret data.
Major topics include Python programming fundamentals for data analysis, working with Jupyter Notebooks, and using libraries such as NumPy, pandas, Matplotlib, and Seaborn; data wrangling, merging, reshaping, and handling missing values; reading and writing data from various sources including CSV, JSON, Excel, and databases; visualizing data through charts and graphs; time series analysis, aggregation, and introductory statistical and machine learning techniques using scikit-learn and statsmodels. Instruction is based on Python for Data Analysis (3rd Edition) by Wes McKinney, supported by practical assignments, real-world case studies, and a final data analysis project. Students are assessed through lesson assignments, a comprehensive project, and a multiple-choice final exam.
In the lower division baccalaureate/associate degree category, 3 semester hours in Computer Science, Information Systems, Business, Engineering Science or Informatics (4/25).