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!
On this page, scroll down on the page until you see the "Apply for a CSDE Account"
A new window will open, sign in with your UW NetID.
You should receive an email confirming your application.
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.
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.
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.
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.
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.
This tutorial walks through some of the most basic SQL queries you can perform. It's a general tutorial perfect for learning basic syntax.
Spring 2024
04/03/2024 Workshop Series
Winter 2024
02/28/2024 Tableau Workshop
02/14/2024 Major Applications Workshop
01/24/2024 Analyzing a Dataset Workshop
Autumn 2023
11/29/2023 AI Ethics Open Forum Slides
11/15/2023 Intro to SQL Workshop
11/08/2023 Guest Speaker Alan Liu Slides
10/18/2023 Intro to R Workshop
10/11/2023 Recruitment Info Session and Intro to Case Competition
10/04/2023 Club Information Session
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
12/07/2022 End of Quarter Fall 2022
12/07/2022 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
05/18/2022 Data Science Pathway and Resources
04/20/2022 Analytical Framework to Assessing Investments
04/13/2022 Web Scraping with BeautifulSoup and Selenium in Python: Code
03/28/2022: Investment project intro. Resources:
Winter 2022
02/09/2022 recording: BinarySearch in Java
01/12/2022 recording: Applied Analytics in
Stock Trading
01/05/2022: Intro: Data Science and its Various Applications
Autumn 2021
12/01/2021: 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
03/04/2020: Workshop: Predictive Analytics 2
02/26/2020: Special Topic: Accounting & Finance Analytics
02/26/2020: R Markdown: Stock/Finance Analysis in R
02/05/2020: Workshop: Data Visualization
01/29/2020: Company Visit: Symetra
01/22/2020: Workshop: Data Cleansing
01/15/2020: 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)
11/06/2019: Workshop: Tableau
10/23/2019: Workshop: Intro to SQL
10/16/2019: Guest Speaker: Stewart Pearson Slides
10/09/2019: Fall 2019 Information Session
Spring 2019
ML PROJECT LINK
04/16/2019: Meet & Greet Spring 19
04/23/2019: Intro to Logistic Regression and ML
05/07/2019: Logistic ML_R File
Winter 2019
01/23/2019: Workshop: Data Collection
01/30/2019: Meetup: Meet Your Team
02/06/2019: Snow Day (Cancelled)
02/13/2019: Guest Speaker: Marco Zamora
02/20/2019: Workshop: Data Cleansing in R
02/27/2019: Workshop: Intro to Analysis and Modeling
_ _ _R Markdown File
Fall 2018
10/24/2018: Meeting Slides
10/30/2018: R Files
11/07/2018: Meeting Slides
11/14/2018: No Slides
11/28/2018: No Slides