Course Description

The word Jupyter in grey with an orange crescent above and below suggesting a circle around the word with three grey dots around the outside


Data science is becoming increasingly popular with more and more industries beginning to value its importance, while recent advancements in open-source software have made the discipline accessible to a wide range of people. Python is a popular choice for most data scientists, owing to its ease of use and versatile nature.


In this course, we show how Jupyter Notebooks can be used with Python for various data-science applications. Aside from being an ideal "virtual playground" for data exploration, Jupyter Notebooks are equally suitable for creating reproducible data processing pipelines, visualizations, and prediction models.


We will look at various data modelling concepts using Jupyter Notebooks, and we will see the full power of Jupyter Notebooks as we work through this course.


This fast-paced practical single-day course focuses on solving challenges presented by data science in a manner that is simple to conceptualize and easy to implement.


This is achieved by leveraging Python libraries that offer abstractions to complicated underlying algorithms. The result is that data science becomes very approachable for beginners, a fact which is reflected by the strength and growing popularity of the Python ecosystem. In this course, we will cover every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data.


The Jupyter Notebook acts as an add-on tool - a virtual playground - that allows you to create and share live code, equations, visualizations, and text.


By touching on a variety of topics within the discipline, you'll be exposed to many interesting examples with real-world data.

Course Outline

  • Lesson 1: Jupyter Fundamentals
    • Basic Functionality and Features
    • Our First Analysis - The Boston Housing Dataset
  • Lesson 2: Data Cleaning and Advanced Machine Learning
    • Preparing to Train a Predictive Model
    • Training Classification Models
  • Lesson 3: Web Scraping and Interactive Visualizations
    • Scraping Web Page Data
    • Interactive Visualizations


Learn about more data analytics topics here


This course focuses on creating reproducible data analyses using Python and Jupyter, and is intended for an audience with a background in Python. As such, we do not cover the basics of Python in this course. However, we will take a brief tour of the Jupyter interface.

If you're a Python programmer stepping out into the hugely popular world of data science, opting for this course is the right way to get started.


For the best experience in this course, you should have knowledge of programming fundamentals and some experience with Python. In particular, having some familiarity with the Python libraries Pandas, Matplotlib, and scikit-learn will be useful.

Applies Towards the Following Certificates


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Virtual: Instructor Led
T, Th
3:00PM to 6:00PM
Apr 06, 2021 to Apr 13, 2021
Schedule and Location
Contact Hours
Course Fee(s)
Tuition non-credit $695.00
Section Notes

Enrollment Deadline is Tuesday, March 30, 2021  at 5 PM CST. Beyond this date, please call 314-935-4444 to register.

THIS IS A VIRTUAL COURSE--Attendee can participate from a location of their choosing. The live instructor teaches the course and provides the opportunity for remote attendees to participate in discusses and exercises with both in-person and remote attendees. Some courses involve hands-on activities and labs. These activities are performed via a secure cloud-accessible environment. Live online courses are through Zoom (or Webex); Video camera, microphone and speakers are necessary to participate in this class.


A full refund will be given when a registrant cancels more than five business days prior to the start of the class. Cancellations received within 5 business days of the start of the class and no-shows will be billed in full. Another person may be substituted at any time at no additional charge. 

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