Here you'll find resources to help you improve your analytics skills. Included below are links to videos, tutorials, and UW affiiliated programs expanding on the topics discussed during our workshops and guest speaker events, categorized by topic. Click any of the topics below to see more!

The following tutorial will walk you through the steps to access various visualization and analysis tools provided to UW students for FREE. Follow the steps below to gain access to SPSS, SAS, Stata, Tableau, and more.

The best part? You won't use space on your device, because it's all available via remote access to a UW server!

Step 1: Go to UW CSDE's site: Site link here

On this page, scroll down on the page until you see the "Apply for a CSDE Account"

Step 2: Click "New Account"

A new window will open, sign in with your UW NetID.

Step 3: Fill out information

You should receive an email confirming your application.

Step 4: Remote Desktop Connection

For Windows:
If you're on a Windows device, you should already have this installed.

For Mac:
You'll have to download the Microsoft Remote Connection application Here.

Step 5: Connect to CSDE Server

This is the final step! Go to this page and expand the section on "Connecting to Windows Terminal Servers". Follow the steps listed there and sign into your newly created CSDE account (uses NetID) and you'll have access!

When you access the CSDE server, you will be prompted to enter a username and password. For username enter "NETID\-YourNetID-" (you need to add the "NETID\" part), then for password enter your NetID password.

Visualization Software

If you’re working with large data sets, visualization tools can help you quickly make sense of what the data is telling you. This section is broken into three parts: Programs, Usage, and Learning. The Programs section lists software (click to go to company documentation), Usage overviews the main benefits of each tool, and Learning provides additional resources to help you learn each tool in greater depth.


A desktop and cloud based data visualization tool allowing users to quickly create powerful visuals from any dataset. Great for quickly presenting geographical trends, sales data, and much more. Allows you to create stylish dashboards to track your most important data. If you’re into coding, Tableau integrates with R and Python, giving you much more power over the tool.


SPSS allows you to quickly visualize large data sets and model them with click-and-run commands (no need to code). You can run anything from simple regressions to Bayesian and ARIMA models with the tool. Used in Marketing Mix Modeling, Biological research, Market research, and more. Capable of producing predictive models for advanced analytics work. You can perform all popular statistical methods within SPSS.


Stata is a command line-based analysis tool primarily used in academia (Economics). The language requires knowledge of syntax (link to syntax provided in Learning below), and allows users to regress large data sets and generate graphs. The software is separated into two main functions: Graphics and Statistics. There is an excellent forum community where users posts solutions to various functions. Stata syntax here.

Power BI

A Microsoft-built application allowing users to create custom visualization dashboards locally and from the cloud. Easy to learn the basics and upload datasets to visualize. Integrate with automatically updating databases so you can track your performance in real time. Power BI also has a slider feature to test different hypotheses and see the outcomes.


The languages and tools below are used in the data science fields in multiple areas. Python is used in computer science for artificial intelligence and predictive modeling (...and seemingly infinite other things). R is used in the business analysis world for data visualization, modeling, and analytics. You’ll find usage overviews for each programming language under the “Usage” section and tutorials and other resources under “Learning”. Included at the bottom is a section titled “Tools”. These tools are useful for the languages listed.

Data Science Practice for All Levels Here.

Also, searching "Data analytics projects for beginners" on Google will give you some great examples to try out!


Used for Deep Learning, Artificial Intelligence, Neural Network Construction, Application building, Analytics, and more. It’s an extremely powerful programming language with lots of support for anything you’ll want to do. For analytics, IDEs like Jupyter and PyCharm (see below) are excellent choices.


Used for analysis, modeling, data visualization, and more. Primarily used in business applications for statistics and data analytics in combination with data visualization tools such as Tableau. R is primarily used with RStudio.


Stands for Structured Query Language. SQL is used to navigate and select data within large relational databases.


General Coding Course

Data Science Specific Course

Good Tutorial Website

Python Documentation


Data Science Course

Paid Courses on Udemy

R Documentation


SQL Syntax

Basic Free Intro Series

This tutorial walks through some of the most basic SQL queries you can perform. It's a general tutorial perfect for learning basic syntax.

Meeting Resources

Look no further. Here you'll find slides and files from our workshops!

Spring 2023
05/17/2023 End of Year Recap
05/10/2023 Summer Without an Intership Workshop
04/19/2023 Interview Prep Workshop
03/29/2023 Major Application Information Session

Winter 2023
01/25/2023 Networking Event
01/04/2023 Winter 2023 Club Information Session

Autumn 2022
12/7/2022 End of Quarter Fall 2022
12/7/2022 Data Science and Data Analytics Project
11/16/2022 Microsoft Data Scientist Speaker Slides
10/26/2022 Data Science in Investing
10/12/2022 Introduction to Data Science

Spring 2022
5/18/2022 Data Science Pathway and Resources
4/20/2022 Analytical Framework to Assessing Investments
4/13/2022 Web Scraping with BeautifulSoup and Selenium in Python: Code
3/28/2022: Investment project intro. Resources:

Winter 2022
2/09/2022 recording: BinarySearch in Java
1/12/2022 recording: Applied Analytics in Stock Trading
1/5/2022: Intro: Data Science and its Various Applications

Autumn 2021
12/1/2021: Tableau Workshop

Spring 2021
04/06/2021: Web Scraping. Codes and slides obtained from CSE 163 Lec29

Winter 2021
02/17/2021: Workshop: Presidential Election Visualization in R slides
01/13/2021: Workshop: Python demo slides
Some codes are also stored on Github

Autumn 2020
11/12/2020: Workshop: Introductory ML
10/28/2020: Workshop: Tableau
10/06/2020: Workshop: Data Collection/Cleaning
Codes are stored on Github

Winter 2020
3/4/2020: Workshop: Predictive Analytics 2
2/26/2020: Special Topic: Accounting & Finance Analytics
2/26/2020: R Markdown: Stock/Finance Analysis in R
2/5/2020: Workshop: Data Visualization
1/29/2020: Company Visit: Symetra
1/22/2020: Workshop: Data Cleansing
1/15/2020: Workshop: Data Science Basics

Fall 2019
11/20/2019: Special Topic: Random Forest Using Python
11/13/2019: Workshop: Predictive Analytics Intro
11/13/2019: R File (Decision Tree)
11/6/2019: Workshop: Tableau
10/23/2019: Workshop: Intro to SQL
10/16/2019: Guest Speaker: Stewart Pearson Slides
10/9/2019: Fall 2019 Information Session

Spring 2019
4/16/2019: Meet & Greet Spring 19
4/23/2019: Intro to Logistic Regression and ML
5/7/2019: Logistic ML_R File

Winter 2019
1/23/2019: Workshop: Data Collection
1/30/2019: Meetup: Meet Your Team
2/6/2019: Snow Day (Cancelled)
2/13/2019: Guest Speaker: Marco Zamora
2/20/2019: Workshop: Data Cleansing in R
2/27/2019: Workshop: Intro to Analysis and Modeling
_ _ _R Markdown File

Fall 2018
10/24/2018: Meeting Slides
10/30/2018: R Files
11/7/2018: Meeting Slides
11/14/2018: No Slides
11/28/2018: No Slides

Fun stats:


Pizzas Eaten


Oreos Consumed

15 gal.

Lemonade Crushed



Get in touch

Become a Member

Join our mailing list.