Mastering Data in the Real World with Python

Course Director: Ruiqing Cao

Content

This course introduces the fundamentals of Python programming and Data Science to students interested in data-oriented professional careers in business and academia. Course materials begin with a preliminary module on Python programming, and continue with two main modules covering fundamental topics in data science such as data modeling, data collection, and data wrangling, with a variety of practical examples and hands-on exercises to help students gain data proficiency by working with real-world data from government agencies, digital traces, and open-source project archives. The course ends with a final module focusing on recent advances in textual analysis and large language models (LLMs), and provides actionable strategies for using these new technologies to accelerate your learning and help solve data science tasks.

Topics covered include, but are not limited to:

• Python Programming Fundamentals: Scalar and non-scalar data objects, variables and constants, expressions and operations, conditionals, control flow, string operations, file input/output, functions and modules, list comprehensions, and regular expressions
• Collecting and Summarizing Data: Data storage formats, web scraping and APIs, working with tabular data, the relational model, Pandas and essential Python libraries for data science, and conducting exploratory data analysis
• Combining and Wrangling Data: Creating new variables and datasets, aggregating and sorting data, performing row-by-row operations, merging datasets from various sources, transforming raw data, converting between data formats, and addressing missing data and measurement errors
• Text Data and Large Language Models: Opportunities and challenges with unstructured data, text data preprocessing, bag-of-words and sequence models, common text analysis methods, word embeddings, large language models (LLMs), and open-source tools

Structure and Format

This course consists of 14 sessions covering both the theoretical and practical aspects of each topic, along with 7 optional computer lab sessions designed to help students complete graded assignments and the final project. These interactive lab sessions provide an opportunity for students to ask the instructor any course-related questions. Attendance is not required but recommended.

The workload for this course is comparable to other data analytics courses; however, it is front-loaded, with a focus on using Python to solve real-world data tasks. By the end of the first week, students will have set up the software tools required for the rest of the course. In the second week, an in-class diagnostic Python quiz will assess students’ coding proficiency and contribute a minor portion to the overall grade.

Course Administrator: Anna.Burell@hhs.se

After completing this course, students will be able to:

• Produce, debug, and interpret functional Python code to automate real-world tasks
• Prepare, transform, and analyze tabular data using Pandas and other Python libraries
• Construct high-quality datasets by retrieving and combining data from real-world sources
• Design data collection strategies and develop data models to meet business needs
• Perform text analysis and extract insights from unstructured data
• Use large language models (LLMs) and online resources to accelerate data science learning

Open to all masters students (including 1st year).

Students are encouraged to embrace computational thinking and ready to work with real-world data, but no mathematics or programming background is assumed.

Minor adjustments may be made

• The Official Python Tutorial
• McKinney, W. (2022). Python for data analysis
• (Optional) Munro, K., Papp, S., Toth, Z., Weidinger, W., Nikolic, D., Vesela, B.A., Bruckmüller, K., Cadonna, A., Eder, J., Gorzala, J. and Hahn, G.A., 2024. The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics. Carl Hanser Verlag GmbH Co KG.

The only grade granted is Pass

The course assessment will be conducted on a continuous basis through:

• An in-class quiz in the 2nd week (10%)
• Two group assignments (25% each), due at the beginning of the 5th and 7th weeks
• An individual final project (40%), to be submitted by the final exam period

Group assignments may be completed individually or in pairs (2-student groups). Use of AI tools (such as ChatGPT) is permitted, with grading based solely on the quality of submitted code and written answers.

A passing grade requires at least 25% on quiz and final project, and at least 25% on the two group assignments.

Find the course schedule in TimeEdit

Have you been granted educational support because of a documented disability? If so, please contact the course director as soon as possible so that adequate accommodations can be made.

Contact specialneeds@hhs.se if you have questions about being granted educational support or need a copy of your educational support certificate.

For more information, visit the Specific Educational Needs page on the Student Hub (login required).