This course provides a practical introduction to the data science workflow using Python. Successful completion will involve using advanced features of Python, retrieving information in data files, working with the Numpy and Pandas libraries, visualizing information, and completing an end-to-end data science project. Credits: 4.
Prerequisite(s): COS 160, and MAT 152 or MAT 210 or MAT 220.
Learning Outcomes
By the end of this course, students will be able to:
- Apply advanced features of python including classes, sequences, maps and lambda functions.
- Retrieve information in data files and prepare it for analysis and visualization.
- Utilize the numpy library for algebraic computation and pandas libraries for data storage and retrieval.
- Construct graphs and plots to visualize information.
- Identify ethical issues in Data Science.
- Work with data in different forms: numbers, text, images, and sound.
- Complete an end to end data science project.
Textbook
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd or 3rd Edition, Wes McKinney.
Syllabus
Offered
Fall Semesters alternating years