MASTER OF SCIENCE IN 
DATA SCIENCE

The Master of Science in Data Science is 30 credit hours.  

Required Courses - 18 credit hours 
15 credit hours in core courses and 3 credit hours of capstone 

GRAD 50500 – Foundations in Data Science (3 credits) 
GRAD 50600- Big Data Tools and Technologies (3 credits) 
GRAD 50700 – Cross Domain Data Communication and Visualization (3 credits) 
GRAD 50800 – Data Analytics (3 credits) 
GRAD 50900 – Applied Machine Learning: From Foundations to Latest Advances (3 credits) 
GRAD 58900 – Capstone (3 credits) 

Industry Aligned Focus Areas – 9-12 credit hours 
Students are encouraged to select a focus area from the data science-related topics below to enhance their professional experience.   

Applied Statistics (12 credits)  
Managing IT Projects (12 credits)  
IT Business Analysis (12 credits)  
Spatial Data Science (12 credits)   

Free Electives – 3 credit hours, if needed 
Any Graduate Level Course with advisor approval  

TOTAL CREDITS  30

GRAD 50500 – Foundations in Data Science (3 credits)

Course Description:
Foundations in Data Science is the inaugural course in the Data Science portfolio. Tailored for students with a technical background, this course provides a comprehensive introduction to key concepts, statistical techniques, and tools foundational to the field of data science.
The syllabus integrates hands-on experience, ethical considerations, programming refresher, and agile project management principles to equip students with a robust foundation for their data science journey.

Learning Outcomes:
– Demonstrate a comprehensive understanding of core data science concepts and articulate the significance of data in various domains.
– Acquire skills to perform exploratory data analysis, identify patterns, and make informed decisions based on statistical inference.
– Critically analyze ethical dilemmas arising in data-driven contexts, apply ethical decision-making models, and navigate the legal implications of data handling and analysis.
– Demonstrate a comprehensive understanding of programming fundamentals and the application of agile methodologies to manage and execute data science projects.
– Access, navigate, and employ Purdue’s computing resources available through Purdue’s Rosen Center for Advanced Computing (RCAC).

GRAD 50600 – Big Data Tools and Technologies Courses (3 credits)

Course Description:
This course is designed to equip students with the essential skills to handle big data effectively. It covers proficiency in big data technologies, scalable data processing techniques, and the integration of big data tools into data science workflows.

Learning Outcomes:
– Demonstrate proficiency in big data technologies.
– Apply scalable data processing techniques to handle large datasets.
– Integrate big data tools into comprehensive data science workflows.
– Demonstrate proficiency with cloud computing.

GRAD 50700 – Cross Domain Data Communication and Visualization (3 credits) 

Course Description:
This course focuses on the proficient use of data communication strategies and competencies. The course will focus on
identifying data narratives, generating stories from data, illustrating with powerful and self-explanatory visualization, and basic principles of ethical use of non-data narrative frames for data communication. Designed for students with a technical background, this course aims to enhance students’ ability to extract and communicate meaning and narratives from raw data and visually represent it.

Learning Outcomes:
– Create effective and aesthetically pleasing data visualizations.
– Communicate complex data findings clearly to diverse audiences.
– Utilize interactive visualization techniques for dynamic exploration of datasets.
– Proficiency of integrating ethics and data privacy when communicating with data.

GRAD 50800 – Data Analytics (3 credits) 

Course Description:
This course provides an in-depth exploration of advanced data analysis techniques, predictive modelling, ensemble methods, and proficiency in data manipulation and transformation. It is designed for students with a technical background.

Learning Outcomes:
– Apply advanced data analysis techniques to extract meaningful insights from complex datasets.
– Implement predictive modelling and ensemble methods for accurate data-driven predictions.
– Demonstrate proficiency in data manipulation and transformation techniques.

GRAD 50900 – Applied Machine Learning: From Foundations to Latest Advances (3 credits) 

Course Description:
This course provides an in-depth exploration of machine learning algorithms and data mining techniques, building on the foundational concepts introduced in the GRAD 50300 Foundations of Data Science course. Students will develop a comprehensive understanding of various machine learning algorithms, focusing on practical applications and hands-on experience. Additionally, the course will cover data mining techniques for dimension reduction and pattern discovery.

Learning Outcomes:
– Demonstrate a comprehensive understanding of popular machine learning algorithms, including supervised and unsupervised learning techniques.
– Demonstrate the ability to analyze mathematical foundations and principles behind machine learning models.
– Understand the nuances of applying various machine learning models, emphasizing the trade-offs between performance, computational complexity, and interpretability.
– Students will actively explore unsupervised learning techniques, emphasizing clustering algorithms, and recognizing their vital role in solving real-world challenges.
– Demonstrate proficiency into the principles of deep learning and investigate recent advancements, such as optimization using diffusion models, learning from unlabeled data using consistency models, and decentralized data processing through federated learning.

GRAD 58900 – Capstone (3 credits)

Course Description:
The capstone course aims to provide students with an opportunity to integrate their accumulated knowledge, technical, and social skills to identify and solve a real-world data science problem, with an emphasis on the application domain. The capstone course for the Master of Science in Data Science provides students with practical experience applying the collective set of skills developed through the program
to complete a professional project in support of a private, public, or non-profit partner. Students, in teams of 3-5 students each, will work with a product owner to scope out the project via a project charter that will include a timeline, milestones, and metrics that will yield benefits and a strategy for measuring the outcome of the project compared to a baseline.

Learning Outcomes:
– Identify the public, non-profit, or business objectives in a complex problem.
– Evaluate and define an applied problem using data science that requires practical analysis and recommendations / and analytic output to a stakeholder.
– Design a professional-level applied project that provides meaningful input to a targeted beneficiary.
– Evaluate the proposed solution and interpret results concerning the “business” objectives.
– Demonstrate an understanding of translational communication skills by communicating technical information to both technical and non-technical audiences.

Students are encouraged to select a focus area from the data science-related topics below to enhance their professional experience.  

Applied Statistics (12 credits)  
Required 
STAT 51600 – Basic Probability and Applications  
STAT 51700 – Statistical Inference  
Choose 2 of the following 4 courses below:
STAT 51400 – Design of Experiments  
STAT 52000 – Time Series and Applications 
STAT 52500 – Intermediate Statistical Methodology  
STAT 52600 – Advanced Statistical Methodology  

IT Business Analysis (12 credits)
Required: 
CNIT 53000 BAE – IT Business Analysis (3 credits)
Three courses from the list below:  
CNIT 53100 RMA – IT Requirements Analysis & Modeling (3 credits)
CNIT 53200 EA – IT Enterprise Analysis (3 credits)
CNIT 53500 ABA – Advanced Topics in IT Business Analysis (3 credits)
CNIT 57000 BDA – IT Data Analytics (3 credits)
CNIT 58500 PCM – Organizational and Change Management for IT Projects (3 credits)

Managing Information Technology Projects (12 credits)
Required:  
CNIT 55200 PME – IT Project Management  
Choice of: 
CNIT 55100 EPM – IT Economics 
CNIT 58000 ATP – Advanced Topics in IT Project Management  
CNIT 58200 EST – IT Estimating-Scheduling-Control  
CNIT 58300 PPM – IT Program and Portfolio Management  
CNIT 58500 PCM – Organizational and Change Management for IT Projects 
CNIT 58600 RMP – IT Requirements Management  

Spatial Data Science (12 credits) 
ABE 65100 – Environmental Informatics 
AGRY 54500 – Remote Sensing of Land Resources 
ASM 54000 – Geographic Information System Application 
FNR 58700 – Advanced Spatial Ecology and GIS