Python for Data Science
Python is one of the most powerful and widely used programming languages available. Its easily understood syntax makes it a popular choice for new coders, while its' many open source modules for everything from web development to data analysis make it the tool of choice for many data scientists. These courses will familiarize students with the use of Python for data science and the common libraries to perform typical data science operations.
Python Professonal Training Course Offerings
Upcoming schedule for instructor-led, live, online courses.
- Introduction to Python for Data Science: Apr. 7, 9, 14 & 16, 2025
Learn more and register
A Certificate of Completion is provided with each course showing number of contact hours (instruction), and field of study as "Information Technology" for continuing education purposes. Most courses provides 14 contact hours and are equivalent to 1.4 CEU's.
The term CEU is not a trademarked term; therefore, any educational institution may use it to describe their courses. Professions and industries usually regulate their approved continuing education within their bylaws and not one institute or accrediting body has become a standard to accept in this regard.
Professionals should always consult their Association or regulating body prior to embarking on continuing education and not assume a CEU will be accepted as part of their professional development.
- ntroduction to Python for Data Science
- Intermediate Python for Data Science
- Advanced Python for Data Science or Fundamentals of Machine Learning
To achieve a Certificate of Competency, all classes must be taken through the Center for Business Analytics. In the event that professional experience meets the prerequisite for a class, another approved class may be substituted (limited to 1 class per Certificate of Competency)
Introduction to Python for Data Science
This course is an introduction to Python and its uses as a data analytics tool, requiring no previous Python experience. It begins with the core concepts of Python itself: data types, functions, and objects. With that foundational knowledge, students will be introduced to the core tools in Python’s data science toolkit: the Pandas package for data wrangling and the matplotlib package for visualization. Students will spend some of their time working through a case study, in which they can apply the concepts they’ve learned while instructors are available to help with questions.
Open to all. 4 online sessions
No prior experience required
| Day One & Two | Day Three & Four | |
|---|---|---|
| Introduction Python and Jupyter Overview Fundamentals Packages, Modules, Methods, Functions Importing Data Selecting and Filtering Data Working with Columns Case Study: Part 1 Q & A |
Case Study: Part 1 Review Summarizing Data Summarizing Grouped Data Joining Data Exporting Data Visualizing Data Case Study: Part 2 Case Study: Part 2 Review Q & A |
- 4 online sessions
- Time commitment 12-16 hours
- Certificate of Completion.
- Required class for “Python for Data Sciencel” Certificate of Competency
Online Course Fees: See registration page
These trainings can also be customized and delivered at your location.
Intermediate Python for Data Science
This workshop builds on concepts taught in the introductory course (i.e. the basics of Python and its data science stack). Students will learn how to integrate control flow into their code, write their own reusable functions, and build a variety of models using the cutting-edge scikit-learn library. They will also get more exposure to practical concerns of using Python in a reliable and scalable way: how to manage multiple Python environments using conda, what other packages exist in the data science ecosystem, and the basics of running Python from the command line.
Open to all. 4 online sessions
PREREQUISITE: Attendance at the Introduction to Python for Data Science training or previous experience using Python for data analysis in a professional environment.
| Day One & Two | Day Three and Four | |
|---|---|---|
| Introduction Working with Data using Pandas Conditions Iterations Functions Applying Functions to Pandas Dataframes Case Study: Part 1 Q & A |
Case Study Part 1 Review Python from the Shell Kernels and Environments Python Data Science Ecosystem Modeling with Scikit-learn Case Study: Part 2 Case Study: Part 2 Review Q & A |
- 4 online sessions
- Time commitment 12-16 hours
- Certificate of Completion.
- Required class for “Python for Data Sciencel” Certificate of Competency
Online Course Fees: See registration page
These trainings can also be customized and delivered at your location.
Advanced Python for Data Science
This is a two-day course that introduces how one can use Python for advanced machine learning applications. Most of the time will be spent working through example problems end-to-end in the classroom. Students will learn the fundamentals of the scikit-learn library along with exploring several other tools and methodologies that allow you to implement a robust end-to-end machine learning workflow. Some additional time will be reserved for discussion of real programming challenges students have encountered, and for an overview of related relevant technologies students may need in an industry setting (e.g. Git and GitHub).
Objectives
- Develop an intuition for the machine learning workflow and Python tooling.
- Build familiarity with common software engineering tooling and methodologies for implementing a machine learning project.
- Gain a high-level understanding of the function of data science-adjacent technologies that students will encounter in the workplace, focusing on Git and GitHub.
Prerequisites
- Strong understanding of core Python concepts: variables, loops, conditionals, and functions
- Some experience using Jupyter Notebooks or Jupyter Lab
- Solid grasp of Pandas and how to use it for data manipulation: filtering, selecting, aggregating, slicing (indexing), and updating
- High-level understanding of modeling concepts: training and test data, model accuracy, and overfitting
| Day One & Two | Day Three and Four | |
|---|---|---|
| Introduction Setting the Stage Git and Version Control; EDA and first scikit-learn model Modular Code Feature Engineering Case Study: Part 1 Q & A |
Model Evaluation and Selection More on Modular Code Unit Tests ML Lifecycle Management Case Study: Part 2 Case Study: Part 2 Review Q & A |
- 4 online sessions
- Time commitment 12-16 hours
- Certificate of Completion.
- Required class for “Python for Data Sciencel” Certificate of Competency
Online Course Fees: See registration page
These trainings can also be customized and delivered at your location.
Python for Data Science Instructors
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Jay Cunningham Lead Data Scientist / Machine Learning Engineer at 84.51°
Courses Taught:
- Python for Data Science Introduction, Intermediate, Advanced
Jay Cunningham is a Lead Data Scientist / Machine Learning Engineer at 84.51°. He currently works at the Forecasting Center of Excellence, developing and evaluating new methodologies for highly granular sales forecasts. Prior to his forecasting work he created and maintained several in-house Python and R packages to facilitate the work of data scientists and engineers throughout 84.51°. His interests both in and out of work involve broadening his deep learning skillset and applying functional programming concepts. His educational background is in mathematics, computer science, and economics, having graduated with a master’s degree in economics from UNC Greensboro.
Gus Powers: Lead Data Scientist at 8451º
Courses Taught:
- Python for Data Science Introduction, Intermediate, Advanced
Gus Powers is a Lead Data Scientist at 8451º, where he works on developing data-focused Python tools for data scientists and engineers. He has given several Python-based trainings on tool development and maintenance. He holds a MS in Chemistry and a MS in Business Analytics from UC.
For more information about these classes, or for custom training classes, please contact
