Python for Data Science
Learn to design and write high-performing Python code to manipulate data, train models, and deploy them effectively.
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Learn to design and write high-performing Python code to manipulate data, train models, and deploy them.
The eight-week Python for Data Science course at the University of Chicago introduces the basic concepts of Python as a versatile programming language that can process a wide range of data types and analysis tasks. This highly technical course is project-based and will present many practical examples to equip you to create and run your own Python projects for complex data analysis.
Designed For
Designed for professionals across data-driven industries eager to work with Python and delve deeper into data science.
Dive Deeper Into Data Science.
Register today and unite your professional practice with our distinctive blend of academic rigor and real-world application.
Learn moreMaster the Python Skills Required for Data-Driven Careers
This course provides a comprehensive, practical foundation in Python for data science, equipping you with the coding discipline, analytical techniques, and model-building capabilities essential for modern data roles. Through hands-on exercises and real-world applications, you will develop the ability to write efficient, scalable, and production-ready Python code that supports data analysis, machine learning, and deployment workflows.
By the end of the course, you will be able to:
- Understand and apply the core components of the Python language, including functions, classes, and advanced features.
- Perform sophisticated data analysis and process large datasets with optimized, high-performing code.
- Build, train, evaluate, and deploy machine-learning models using industry-standard libraries.
- Design Python scripts for production environments, including creating persistence models ready for API and batch-scoring use cases.
- Implement multiprocessing and multithreading to run code in parallel and improve performance.
Together, these capabilities will prepare you to contribute immediately in data-driven roles and accelerate your transition into careers where Python proficiency is a core requirement.
Methodologies and Techniques:
Anaconda
Beautiful Soup
Jupyter
NumPy
Python
Seaborn
Sklearn
Weekly Course Schedule
Learn basic Python scripting, utilizing built-in datatypes, containers, functionality, and comprehensions to speed up iteration. Discover how to integrate error-handling techniques into scripts and leverage itertools and the collection module to manipulate iterables.
Learn to create user-defined functions (UDFs) and utilize built-in features such as args and kwargs to enhance UDFs and use them within comprehensions. Discover how to create lambda functions, understand what a class is, and develop one to perform basic linear regression. Use timeit to profile functions and find the most performant functions.
Learn to load external data using Pandas and manipulate data frames for adding and dropping columns and subsetting. Use Pandas and NumPy to calculate summary statistics, perform correlation analysis, and execute group by operations. Discover how to join and concatenate data frames using Pandas and create visualizations with Seaborn.
Explore broadcasting and how to use it to perform matrix and element-wise operations for efficient data manipulation. Learn to use advanced Pandas concepts to manipulate data frames, including melt and pivot, perform window functions, and calculate rolling metrics.
Gain the skills to perform feature extraction and transformation using Sklearn and use it to train and evaluate machine-learning models.
Learn to use a generator to handle large datasets and perform parallel modeling training using Sklearn. Discover how to run programs in parallel using the multiprocessing module.
Learn to use urllib and requests to ping websites, Beautiful Soup to parse HTML data, and a ThreadPoolExecutor to scrape in parallel.
Learn to save and reload trained models and make batch predictions. Discover how to deploy a model as an API and monitor models in production.
Earn a Credential in Python for Data Science
After successful completion of this course, students will receive credentials certified by the University of Chicago, including a certificate of completion and a digital course badge to recognize their achievement.
Meet the Faculty Director
Jonathan Williams, MS
Assistant Clinical Professor
Jonathan Williams has been working in statistical consulting and data science education for fourteen years and currently teaches full-time at the University of Chicago. Previously, he managed data science teams at Civis Analytics, working on behalf of public sector clients, and before that he worked...
Meet Your Instructor
Our highly trained instructors are courageous thinkers and passionate leaders who leverage years of industry expertise and up-to-date knowledge of terminology, tools, and trends to deliver an unparalleled learning experience. Through their rigorous discourse, cross-disciplinary collaboration, and field-shaping contributions, they create practical solutions and pioneering innovations that enrich our world.
Michael Colella, MS, MA
Senior Director of Global Data Strategy and Analytics, AXS
Michael Colella is the senior director of Global Data Strategy and Analytics at AXS, where he leads business intelligence, analytics engineering, and web analytics. His past leadership roles include spearheading global analytic innovation at Kraft Heinz and serving as Upshop’s chief data scientist.
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Career Outlook
Python is one of the most popular and versatile programming languages. It is widely used in data science, machine learning, and web development. Available positions requiring familiarity with Python are on the rise; world-class companies like Accenture, Amazon, Apple, Deloitte, Google, Microsoft, and Netflix are among those behind the significant jump in job openings for Python developers.
The average median pay for an entry-level Python developer in the United States.
Python’s rank among programming languages that developers want to learn.
The projected employment growth for software developers from 2024 to 2034.
Potential Job Titles for Professionals with Python Skills
- Data Analyst Data Scientist (Entry-Level or Associate)
- Python Developer Machine Learning Engineer (Junior)
- Business Intelligence (BI)
- Analyst Data Engineer (Junior or Associate)
- AI Engineer (Entry-Level)
- Quantitative Analyst (Junior)
- Marketing Data Analyst
- Product Data Analyst
- Automation Engineer
- Research Data Analyst
How Do I Register for This Course?
- Log into UChicago's course registration portal by creating an account using your username and password.
- Navigate to the Python for Data Science course page and click on "Add to Cart" to include it in your course selection.
- Review your course selection and click on "Proceed to Checkout" to complete the registration process.
Of Interest
Data Science
Gain fundamental skills in programming, data visualization, natural language processing, statistics...