Data Science Expo

Learning Kits

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Data Science Expo / Learning Kits

Learning Kits

Once registered, students and educators will receive credentials to access the below Learning Modules hosted on Anaconda’s platform via the email they registered with.

Getting Started with Anaconda
Time: Self-Paced- 40 minutes


In this entry-level course, we’ll show you when and how to use Anaconda. You’ll learn about packages, conda environments, Jupyter Notebooks, integrated development environments (IDEs), and more. We’ll walk you through a Python program (in a notebook and in an IDE) and see what happens when a bug in Python code is identified.

By the end of this course, you’ll understand how to:

  • Understand how different parts of the technology stack work together

  • Understand basic data science concepts and tools

  • Install Anaconda

  • List at least 5 common Python packages and their uses

  • Identify useful libraries for data science and machine learning

  • Open and work with a Jupyter notebook or IDE

Introduction to Python Programming
Time: Self Paced- Approximately 5 hours


This beginner course is designed to help you learn the foundations of Python quickly so that you can start using Python in the real world as soon as possible! You’ll learn how to read and write code, how to choose and use data structures, and how to organize and refactor programs. More importantly, we’ll help you understand what to learn “now,” “next,” and “later” so you can focus on the fundamentals first.

By the end of this online course, you’ll understand:

  • How to read and write Python code

  • How to solve problems with loops and functions

  • How to continuously learn and grow with the language

And you’ll be able to:

  • Choose the right data structure for the right problem

  • Effectively create and use objects and functions

  • Refactor problematic pieces of code

Introduction to Machine Learning
Time: Self Paced- Approximately 2 hours


Why is it important to understand machine learning? Many machine learning techniques can solve interesting problems, from identifying email spam to classifying images. It is important to understand how libraries like scikit-learn work under the hood, to know their strengths and weaknesses, and to determine if they should be used for a given problem.

What you'll learn—and how you can apply it

By the end of this course, you’ll understand:

  • What machine learning is and the various algorithms under its umbrella

  • How regression and classification techniques work, from linear regression to neural networks

  • The API patterns of scikit-learn

And you'll be able to:

  • Use scikit-learn to make predictions on different types of problems, from email spam to image recognition

  • Pair the proper application of supervised machine learning algorithms to a given problem and context appropriately

  • Explain how different machine learning algorithms work, including decision trees and neural networks

Introduction to Data Visualization with Python
Time: Self Paced- 1.5 hours

Description: T

Data visualization is an essential part of data science and data analytics. It is a critical skill for data professionals and anyone who works with data. Remember: “Pictures speak louder than words.” Our brain processes visual information faster than words. Data visualization allows us to use graphical representation of data to communicate information effectively.

This course will help you learn the essential data visualization concepts and tools, focusing on creating static, non-interactive figures and charts. You will learn data visualization techniques using the most popular and fundamental Python visualization tool, Matplotlib. This course will also focus on the convenient Matplotlib interface provided by Pandas and on high-level statistical plotting provided by Seaborn. You will learn how to understand the patterns and relationships of your data through different plotting techniques. You will walk through a visualization project to understand the research and preparation work needed for a complete project. Plus, you will apply the visualization skills you have learned to solve real-world problems.

By the end of this course, you’ll understand how to:

  • Describe basic concepts of data visualization using Python.

  • Explain your data through visualization.

  • Visualize a pandas Dataframe using the pandas .plot() method.

  • Use statistical plots and facet plots with Seaborn.

  • Use Matplotlib’s object-oriented API to fine-tune and customize plots.

Supplemental Resources

These are external resources that will help supplement our Anaconda learning kits.

Data Visualization and Storytelling

Artificial Intelligence and Machine Learning

Supplemental Links for Coding and Visualization


Common Online Data Analysis Platform (CODAP)

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