Curriculum

The Master of Science in Data Science program prepares students for positions conducting high-volume data management, predictive analytics, and data visualization.

The program covers 10 sequential courses. Order of classes may vary based on your start date.

12 months, full-time, in-person

Industry focused

Immersive Education

5 mini-mesters of 2 classes each
(8 instructional courses, Capstone Project, Internship)

Courses

300 Hour Projects

Course Name Credits
DSCI 6010: Data Science Industry Internship 3
DSCI 6051: Data Science Capstone Project 3
Total Credits 6

Mathematics for Data Scientists

Mathematics for Data Scientists will give students the necessary understanding of the mathematical foundations and coding skills required in order effectively manipulate features and optimize function parameters defined on data sets they will work with during the program as well as professionally. This course provides a review of core skills in linear algebra, analysis, and differential calculus with a focus on hands on applications for data science use cases.

Data Exploration, Feature Engineering, and Statistics for Data Scientists

This course serves as an introduction the statistical and probabilistic applications to data sets. Students will be exposed to the fundamentals of data infrastructure as well as the tools and platforms they will be working with in analyzing datasets professionally and throughout the program. The focus of this course is placed on querying, statistical exploration, and understanding data through statistical applications.

Machine Learning and Data Analysis 1

This course focuses on several elements of machine learning. Students gain core skills in implementing, developing and applying core supervised and unsupervised learning algorithms, applying statistical modeling, and following key best practice techniques for building well trained models. This course is designed with coding lab practice to develop implementation skills.

Unstructured Data and Natural Language Processing

Most of the world’s available data is unstructured. This course provides the core skills used to normalize, transform, and otherwise manipulate text data are commonly used in multiple contexts in addition to text analysis. This course will prepare students with the core skills necessary to work with search engine and information retrieval technologies, and cutting edge techniques for transforming unstructured data into structured data types able to be analyzed, processed and used for machine learning and information retrieval algorithms.

Machine Learning and Data Analysis 2

This course covers the advanced machine learning skills that are expected of high performing data scientists in the industry. Covering the skills in this course will be one of the keys that allows graduates of the program to differentiate themselves in their theoretical and implementation knowledge from students educated in the elements of machine learning but have not taken deeper coursework. Students in this course will will gain proficiency working with artificial neural network algorithms, master advanced techniques in optimization, and will gain skills for applying effective high parameter space optimization to deep learning architectures.

Data Science Leadership & Entrepreneurism

This course provides the core skills necessary for professional data scientists to succeed in an industry setting. Students learn the skills of data visualization in parallel with the skills of communicating with a non-technical audience, interviewing skills, and core data science leadership skills. Emphasis is placed on enabling students to listen to articulated business needs or problem cases and learn how to propose as well as execute data science solutions to effectively meet these needs.

Distributed and Scalable Data Engineering

This course covers the advanced topics in “Big Data” infrastructure and architectures. Specifically, the focus is on computing resources and programming environments to support the development of efficiently scalable high volume distributed machine learning algorithms. Students will leave this course with a mastery of distributed computing technologies including hadoop and spark.

Special Topics in Data Science

This course focuses on select data science, data engineering, and machine learning application topics. Each course iteration will select a particular topic of focus. Topic may include:

  • Neural Networks
  • Probabilistic Graphical Modeling and Social Networks
  • Advanced Optimization techniques
  • Fraud and Anomaly detection
  • Current Special Topics course: Probabilistic Graphical Models

Capstone Project

Students are given the opportunity to practice the data science skills developed in the program on a protracted real world project. Various industry partners pitch potential data science projects to the masters students. Students will work with an industry partner, leveraging a data set provided by the sponsor. Each student will develop his or her project by writing project proposals, proposing a solution, developing an experimental process for achieving the solution, and identify success criteria for completion.

Internship

Students will work with a Galvanize industry partner that has data science or data engineering needs for 300 hours. Students will be working closely under the supervision of our expert faculty and Galvanize selected partners to gain the full real world industry experience expected of a top performing data scientists in the market.