Gallery
MADS Degree Tracker
Share
Explore
Gallery
MADS Degree Tracker
⁠
⁠
⁠

I built this doc to better visualize and understand the dependencies and trade offs while selecting courses for the upcoming semester.
To get started, first mark off any courses that you’ve already completed:

Mark off completed courses

Courses that are required are shaded differently than electives.
Category
Mark Completed
Course
Name
Required
Data Science Core
3
​
SIADS 501
Being a Data Scientist
​
SIADS 503
Data Science Ethics
​
SIADS 601
Qualitative Inquiry
Analytic Technique
13
​
SIADS 502
Math Methods for Data Science
​
SIADS 532
Data Mining I
​
SIADS 542
Supervised Learning
​
SIADS 543
Unsupervised Learning
​
SIADS 602
Math Methods II
​
SIADS 630
Causal Inference
​
SIADS 631
Experiment Design and Analysis
​
SIADS 632
Data Mining II
​
SIADS 642
Deep Learning
​
SIADS 643
Machine Learning Pipelines
​
SIADS 644
Reinforcement Learning Algorithms
​
SIADS 652
Network Analysis
​
SIADS 655
Natural Language Processing
Computational Methods
5
​
SIADS 505
Data Manipulation
​
SIADS 511
SQL & Databases
​
SIADS 515
Big Data: Efficient Data Processing
​
SIADS 516
Big Data: Scalable Data Processing
​
SIADS 611
Database Architectures & Technologies
Communication
5
​
SIADS 521
Visual Exploration of Data
​
SIADS 522
Information Visualization I
​
SIADS 523
Communicating Data Science Results
​
SIADS 524
Presenting Uncertainty
​
SIADS 622
Information Visualization II
Data Science Application
5
​
SIADS 680
Learning Analytics
​
SIADS 682
Social Media Analytics
​
SIADS 685
Search and Recommender Systems
​
SIADS 687
Sports Analytics
​
SIADS 688
Data Science for Social Good
Milestone and Capstone
5
​
SIADS 591
Project I: synthesis of computational techniques to collect and process big data
​
SIADS 592
Project II: synthesis of analytics and machine learning techniques to analyze data and present results
​
SIADS 694
Project II: synthesis of analytics and machine learning techniques to analyze data and present results
​
SIADS 695
Project II: synthesis of analytics and machine learning techniques to analyze data and present results
​
SIADS 697
Project III: capstone that applies end-to-end data science techniques to real world scenarios
⁠

⁠
And then you can proceed to
Scheduling
⁠
to select courses for the upcoming semester(s). Or you can view your
Progress
⁠
towards completing the Michigan Masters of Applied Data Science degree.

Gallery
Share
 
Want to print your doc?
This is not the way.
Try clicking the ⋯ next to your doc name or using a keyboard shortcut (
CtrlP
) instead.