Course Description:

This course is a 3-credit introduction course to data science in Python, which assumes no prior programming experience. The course is broken down into two units. In the first unit, students will be introduced to the basics of Python as a programming language. The second unit of the course is devoted to data analytics; students will use Python to explore and visualize real-world data sets from various industries, including finance, sports, and technology.

Course Setup:

This course consists of two parts: an online video session and an offline in-class lab session. Students must finish watching online video sessions on the techniques and knowledge each week, which normally take 60 to 90 minutes. In addition, students need to join the lab for 80 minutes every Wednesday or Thursday, depending on their section’s day and time to practice what they have learned from the videos in the previous week and learn more hands-on knowledge from the class instructor.

Learning Objectives:

After this course, you will be able to:

1.   Program basic Python scripts to solve real-world algorithms or optimization problems

2.   Access and clean data from multiple sources (e.g., Excel, CSV, Text file, etc.) using Python and Pandas.

3.   Pre-process and analyze data using Python to extract business insights

4.   Visualize data patterns and trends using Python

Course Materials (Recommended):

1.   Learning Python 3 the Hard Way:https://learncodethehardway.org/python/

2.   Y. Daniel Liang, Revel  for Introduction to Python Programming and Data  Structures Access

3.   Charles Severance, Python for Everybody, Exploring Data with Python 3

4.   K.S. Kaswan and J. S. Dhatterwal, Python for Beginners, CRC Press, 2023.

5.   Pandas Official Documentation:http://pandas.pydata.org/pandas-docs/stable/

Lecture notes and corresponding Jupyter notebook (IPython notebook) will be distributed in class or online (an electronic version will be available on Canvas). Supplemental and optional readings will be posted on Canvas.

Grading Policy:

You will receive your letter grade based on the below table.

Note: If you submit your assignments or projects late, Canvas will automatically decrease 10% of the grade for every day late. After three days, any late labs, assignments, or projects will not be accepted. Please be aware that you must submit every assignment by the due date.