Python for Data Science
Leverage data in creative, relevant ways to solve real-world problems.
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At a Glance
- Enrollment:
- Open Enrollment
- Length:
- 8 weeks
Upcoming Dates
Students may register up to 7 days after the course start.
Learn to design and write high-performing Python code.
The eight-week Python for Data Science course at the University of Chicago introduces the basic concepts of Python as a programming language. This highly technical course is project-based and will present many practical examples to equip you to create and run your own Python projects.
Designed For
Designed for professionals across data-driven industries eager to work with Python and delve deeper into data science.
Learning Objectives to Master Python
In today’s data-driven world, proficiency in Python is indispensable for data science practitioners. Our course offers a thorough initiation into this versatile programming language, empowering you to tackle real-world challenges with its tools and techniques.
After completing the course, you will be able to:
- Create persistence models to be deployed as an API or used for batch scoring.
- Design code that runs in parallel using multiprocessing and multithreading functionalities.
- Discuss advanced Python functionalities like classes and functions.
- Earn a credential certifying completion from the University of Chicago and become part of the UChicago network.
Ready to Take Your Career to the Next Level?
Register today and unite your professional practice with our distinctive blend of academic rigor and real-world application.
Enroll NowPython Curriculum
You will learn to:
- Identify the core components of Python language.
- Perform advanced data analysis.
- Write production-level Python code.
- Train and evaluate machine-learning models.
- Design and optimize Python code for performance and speed.
- Write Python code to efficiently process large datasets.
- Prepare machine-learning models for production use.
Methodologies and Techniques:
Anaconda
Beautiful Soup
Jupyter
NumPy
Python
Seaborn
Sklearn
Online Format Features
- Self-paced interactive learning modules with a variety of engaging learning activities, assignments, and resources.
- Live sessions that bring you, your peers, and your instructor together to learn collaboratively about the current state of the field, engage with real-world problems, and explore authentic solutions.
- Continuous support from your instructional assistant, who will accompany you on your journey through the content, answer your questions, and provide feedback on your work.
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, participants will receive credentials certified by the University of Chicago including a digital badge to recognize their achievement.
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.
These instructors teach this course regularly. If you wish to know who the current teacher is, please speak to your enrollment advisor.
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.
C...
Josh Goldberg, MSc
Data Scientist, Amazon
Joshua Goldberg is a data scientist with eight years of industry experience. He currently works at Amazon as a data scientist, building machine learning models to support Amazon’s supply chain operations in Private Brands. Previously, he worked at Nuveen Investments, focusing on personalization for...
Patrick McQuillan, MBA
Analytics Executive and Strategy Consultant
Patrick McQuillan is passionate about data as a tool for change and decision-making. He has held leadership roles, most recently as the Global Head of Data Governance and Operational Effectiveness at Wayfair. Previously, he led international consulting teams to drive AI strategy and technology...
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 annual base pay for a 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 2021 to 2031.
Potential Job Titles for Professionals with Python Skills
- Entry-Level Software Developer
- GIS Analyst
- Junior Python Developer
- Machine Learning Engineer
- Python Full-Stack Developer
- Quality Assurance Engineer
- Senior Python Developer
How Do I Get Started?
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Complete the form on the registration page.
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Pay the tuition fee through our secure gateway.
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Receive a welcome email with your login information for the virtual campus.
- Gain access to the course content prior to the start date.
Offered by The University of Chicago's Professional Education