Standardization and Transparency in Data Science

Standardization and Transparency in Data Science

a survey of data science Masters degree programs
The Data Science community has grown rapidly in the past decade, and with this growth has come a proliferation of data science degree programs. Increasingly, members of the ADSA community have been asking for resources to help navigate degree programs in data science. To help address this need, ADSA facilitated the Standardization and Transparency in Data Science (STIDS) working group, which has sought to bring some transparency to degree programs in the field.

STIDS working group members have created this intake survey for data science Masters degree programs to help us build a resource for the entire data science community. The goal is better program transparency to enable students, employers, and the universities themselves to understand the skills and student learning outcomes available across the spectrum of programs and degrees. By participating in this content creation, an additional, important benefit to you is that your responses will be published online together with other institutions - providing a new venue for highlighting your programs to prospective students, potential faculty hires, program administrators, and future employers of your students.

The Survey is organized into four overarching themes present in data science programs:

1. Foundations of Analytics
2. Systems and Implementation
3. Data Science Project Design
4. Data Science in Practice

We've broken each theme into subcategories covering major knowledge areas. For each subcategory, we have asked you to indicate whether coursework in the area is not required, offered as an elective, required for a specialization in the degree program, or required for the degree program. We also ask that, for each subcategory, you indicate what courses cover the topic. Last, we also ask for general information about the degree program and about proposed learning outcomes for the program.

Results of the survey will be shared on the ADSA website starting later this year, and we will work with the community to offer opportunities to update information as needed.

We would like to start processing responses in the next few months. Please submit survey responses by the end of the day on July 6.

Thank you for taking the time to complete the survey. Please reach out to us if you have any questions or concerns (info@academicdatascience.org).

Contributors:

Cathy Anderson, University of Virginia
Jan Dasgupta, Washington State University
H. V. Jagadish, University of Michigan
Alex Johnson, Washington State University
Purush Papatla, University Wisconsin, Milwaukee
Sungjune Park, UNC, Charlotte
Abani Patra, Tufts University
Hridesh Rajan, Iowa State
Brian Wright, University of Virginia
Next: General Program Information

General Program Information

Please provide the following information about your degree program.
PLEASE PROVIDE A SEPARATE SURVEY RESPONSE FOR YOUR EACH OF YOUR MASTERS IN DATA SCIENCE PROGRAMS.
Contact Name

First NameLast Name
Contact Email Address
example@example.com
Institution
What is the name of the university or college responsible for conferring this degree?
Degree Name (as granted on diplomas)
e.g. Masters in Data Science for Finance
Organizational Structure for this Degree
From which organizational unit (department, college, etc.) is this degree conferred and where is it situated in your institution's hierarchy (e.g. Hogwarts University - College of Lore - Department of Potions)?
Specialization or Concentration Options
If this degree includes specializations or concentrations, please list them here (e.g. Masters in Data Science with a concentration in Data Engineering).
Term Units

SemestersQuartersOther
Anticipated Time to Complete Degree Program
In the same units described above (semesters, quarters, etc.)
What general areas of knowledge do you expect your students to have before starting your program?

Linear algebraProgrammingMultivariate StatisticsCalculusStatisticsOther
Next: Foundations of Data Analysis

Student Learning Outcomes
Please use this section to fill in your programs top 4-5 student learning outcomes in the way that you frame them.
Next: Additional Input

Additional Input
If there are additional details about your program that you would like to include, please do so here.
Please verify that you are human*
Should be Empty:

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Exploratory analysis
Artificial Intelligence
Data Mining
Machine Learning

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Random variables and probability distributions
Data Collection Design (sampling concepts, experimental replication, confounding, blocking)
Inference (estimation bias, precision, hypothesis testing, false positives, errors)
Modeling - Stochastic (random errors, dimension reduction, diagnostics, feature selection)
Multivariate Analysis (principle components, clustering, discriminant analysis)
Statistical Learning and Bayesian Methods (bootstrapping, bagging, regularization)

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Arithmetic Geometry
Set theory and basic logic
Matrices and basic linear algebra
Networks and graph theory
Optimization
Calculus
Induction (and principles of recursion)
Information Theory

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Model development and deployment
Model risks and mitigation strategies
Model analysis and validation
Data visualization

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Data Structures
Algorithms (Big O Notation, analysis, proof of correctness)
Simulations

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Database design
Data preparation and cleaning
Records retention, curation and provenance
Big Data Systems
Data security and privacy
Infrastructure (local, cloud, HPC)

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Programming (R, Python, C, javascript/html, SQL)
Collaboration and Version Control (e.g. git, Github, Gitlab)

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
National and international laws and regulations on legitimate use of data
Data privacy
Data security
Data governance
Research Integrity
Analysis for security
FAIR and CARE principles
Understanding and uncovering bias
Understanding how results of analyses impact communities and groups
Interpretability and explainability
Understanding the human impacts of system design
Responsible data collection (segmenting populations, representative populations)

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
How to work with users, stakeholders, and impacted communities
Working with Domain experts
Project management
Understanding Infrastructure cost/benefits
Product management (e.g. maintenance, sunsetting)
DevOps

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Technical writing skills
Communication/presentation skills
Documentation

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Understanding who will be impacted by both the analysis process and the results, and how
Defining the User and UX design
Story-telling with data
Human-centered design

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Defining data-driven questions
Computational logic
Data-driven decision making and validation
The data lifecycle
Analysis and presentation decisions

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Data acquisition
Data governance
Data provenance and data citation

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Reproducibility vs. replicability vs. repeatability
Containers
Interactive Computing

	Not Required	Elective	Required as part of a Specialization	Required for Degree	Course Title(s) and Department(s)
Grammar of graphics
Attractive and sound static and dynamic visualizations

Standardization and Transparency in Data Science

General Program Information

Foundations of Data Analysis

Systems and Implementation

Data Science In Practice: Professional Practice and Responsible Data Science

Data Science Project Design

Student Learning Outcomes

Additional Input