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DAT 5561: INTRODUCTION TO PYTHON AND DATA SCIENCE (3.0 UNITS)

Olin’s Pillars of Excellence:

Olin students will:

1.   Embody values-based and data-driven methods in their approach to all business situations.

2.   Understand the global opportunities and challenges facing businesses.

3.   Engage with business issues through the application of experiential knowledge, in addition to the rigorous technical skills acquired in the classroom.

4.   Pursue world-changing initiatives with an entrepreneurial and innovative mindset and skill set.

Course Delivery & Lab Structure:

Course Format

•   This course is offered in one in-person section designed to provide hands-on experience.

•   It follows a flipped-classroom model:

o Students must watch weekly video lectures on Canvas before attending the in- person lab sessions.

o These videos cover essential concepts needed for lab activities.

•   Additional new topics may be introduced during labs.

Lab Sessions

•   Held weekly for approximately 80 minutes in the assigned classroom.

•   Labs apply video lecture concepts to create multiple charts and graphs using Tableau.

•   Exercises completed during labs are considered individual lab assignments.

o These must be submitted to CANVAS by the due date.

o If some problems are not completed during the lab, students are responsible for adding them to the lab exercises before submission.

•   Attendance is required for all lab sessions.

Remote Learning Option

•   Students approved for remote learning will:

•   Receive a Zoom link for live-streamed lab sessions.

•   Be expected to participate in labs virtually.

•   Instructor office hours will be held virtually via Zoom.

TA Support

•   There will be weekly TA office hours via Zoom.

•   A one-hour TA help session will also be available each week.

•   The TA team is available to assist with questions and support student success.

Please pay attention to Canvas pages and announcements. To ensure you’re receiving Canvas announcements, please check your notification settings:

https://community.canvaslms.com/t5/Student-Guide/How-do-I-set-my-Canvas-notification- preferences-as-a-student/ta-p/434

Course Description:

This 3-credit introductory course in data science using Python is designed for students with no prior programming experience. The curriculum is divided into two main units:

Unit 1: Introduction to Python Programming

Students will learn the fundamentals of Python, including syntax, data types, control structures, and basic programming concepts. This unit provides a solid foundation for applying Python in data science contexts.

Unit 2: Data Analytics with Python

Building on the programming skills developed in Unit 1, students will explore real-world datasets from industries such as finance, sports, and technology. They will learn how to analyze, visualize, and interpret data using Python libraries and tools commonly used in the field.

Course Setup:

This course is structured into two key components:

1. Online Video Sessions

Each week, students are required to watch instructional videos covering essential techniques and concepts. These sessions typically take 60 to 90 minutes and should be completed before attending the lab.

2. In-Class Lab Sessions

Labs are held in person for 80 minutes every Monday. These sessions provide hands-on practice with the material covered in the previous week’s videos and offer opportunities to deepen understanding through guided instruction and exercises.

Learning Objectives:

By the end of this course, students will be able to:

1.   Write basic Python scripts to solve real-world algorithmic and optimization problems.

2.   Access, clean, and manipulate data from various sources (e.g., Excel, CSV, text files) using Python and the Pandas library.

3.   Preprocess and analyze data to extract meaningful business insights.

4.   Create visualizations to identify patterns and trends using Python-based tools.

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.

Computer Software Tool Used