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Upcoming

CORRELATION DOES NOT EQUAL CAUSATION: An Introduction to Exploratory Analyses and Inferential Statistics

September 22 @ 7:00 pm – 8:00 pm on Zoom

This session will go over common exploratory analysis workflows, inferential statistics, and associated best practices. Exploratory analyses and inferential statistics are fundamental to data science, but many people struggle in these realms. This session will work to improve your understanding, teach some best practices, and encourage you to think deeply about your data. This will be a fairly introductory course, but will also be in the style of a review. Some familiarity with data science, statistics, and common analysis approaches is assumed.

 

Past

How to Survive An Encounter with Pythons and Pandas: Introduction to Data Processing in Python

This session will be a quick review of common Python workflows and the Pandas package. It will move fairly quickly as it is intended as more of a review, but is still suitable for beginners as it will cover main introductory data science concepts. This session will assume some Python and data science familiarity.

Data Cleaning

Hosted on Zoom on 4/21 at 7pm EST, lead by Amit!

Intro to R Shiny

Your graphs look good, but they can look better! People see static graphs all the time nowadays; don’t let your work blend in with the crowd! See how using R Shiny can elevate your visualizations in R by adding interactivity and wow your audience. Join us in our Intro to R Shiny workshop to really make your graphs shine!

Data Visualization in R

Everything’s better with a good picture, and data science is no exception! Why resort to using base packages or the built-in plot function in R when you could learn how to use the good stuff? Join our workshop on R visualizations to learn about how to take your visuals up a notch.

Intro to Machine Learning Part 3

If you’re curious about machine learning and its applications, this workshop is for you! Regardless of your technical background, this workshop will lead you through the major steps in the machine learning pipeline and overview the applications of different types of learning. Part 3 will wrap up the workshop series.

Intro to Machine Learning Part 2

If you’re curious about machine learning and its applications, this workshop is for you! Regardless of your technical background, this workshop will lead you through the major steps in the machine learning pipeline and overview the applications of different types of learning. Part 2 covers more detailed examples in Python.

Intro to Machine Learning Part 1

If you’re curious about machine learning and its applications, this workshop is for you! Regardless of your technical background, this workshop will lead you through the major steps in the machine learning pipeline and overview the applications of different types of learning.

Bracketology/Sports Analytics

Hoping to put your ML skills to the test? Look no further than the CADS annual bracketology challenge, where participants are asked to use data science to predict this year’s winners! In the workshop, we’ll introduce to you how to go about utilizing data science with sports as well as discussing submission guidelines for this year’s competition. It is a great chance for you to combine your passion for sports with what you learned about data analysis. Join if you want to look at basketball from a new perspective!

Statistical Testing in Python

Wondering about the differences between t-tests, z-tests, and ANOVA? Or how to set up statistical tests faster and easier than in Excel? Then this workshop is for you! We will begin with a basic overview of hypothesis testing, compare different test statistics, and demonstrate the coding and interpretation of statistical tests in Python. No prior experience is required.

Week of Workshops

A series of workshops held Fall 2020 to help beginners and experienced hackers learn or refresh their data science skills which can be used for the challenge, coursework, or career

  • Intro to R (R Series: Part 1)
  • Intro to Tableau
  • Intro to Git & Github
  • Intro to Spatial Data (R Series: Part 2)