Master of Science in Applied Data Science (MSADS) Program

Program Overview

The main objective of this program is the training of recent graduates to help them land their first job as entry-level data scientists, business analysts, or working professionals looking to transition to a more quantitative role in their industries. The modular format allows students a large degree of flexibility to customize their own learning journey according to their time constraints, learning preferences, and professional goals. Depending on student preference, it can be completed in anywhere from 14 months to two years.

The program adopts a problem-based learning approach to design and deliver courses in three stages. Problem-based learning is increasingly considered an effective instructional approach to facilitate students’ attainment of the high-level competencies and transferable skills increasingly being demanded by the public and private sectors. Through this approach, students will progressively acquire and hone a well-integrated and carefully targeted set of skills in Python programming, statistical modeling, deep machine learning, and decision theory in the context of business analytics. At the completion of Stage 1 (Fundamentals), students will acquire a basic foundation of Python coding and statistical modeling skills and earn a certificate. At the completion of Stage 2 (Intermediate), students will earn another certificate for mastering more specialized Python code for accessing databases, understanding the general linear model and other statistical methods in greater breadth and depth, and consolidating their skills with an intermediate-level course of analytics-for-decision-making challenges. At the completion of Stage 3 (Professional), students will earn the MS degree after learning how to incorporate machine learning techniques for classification and clustering, pattern recognition and forecasting, and image and text processing into their analytical skills and applying all of their technical skills in a capstone project and professional-level course of analytics-for-decision-making challenges. 

Program Outcomes 

The student learning outcomes are grouped into the categories of analytical, technical, and communication, and there is substantial intersection between categories. The program has been designed so that students will be able to: 

Analytical Competencies

• Build statistical models and understand their power and limitations.
• Design an experiment.
• Apply problem-solving strategies to open-ended questions.

Technical Competencies

• Develop software code.
• Acquire, clean, and manage data.
• Manage and analyze massive data sets.
• Use machine learning and optimization to make decisions.
• Assemble computational pipelines to support data science from widely available tools.

Communication Competencies

• Create relevant and impactful visual representations of data for exploration, analysis, and communication.
• Collaborate within and across functional teams.
• Deliver reproducible data analysis.
• Conduct data science activities aware of and according to policy, privacy, security, and ethical considerations.

Program Summary 

The program is made of a total of 36 credits and consists of 11 core courses and a Capstone Project. The program is designed to be completed in sequence, as indicated by the prerequisites noted under Course Descriptions below. The Capstone Project is designed to allow you to integrate and apply your degree program learnings. As a result, this course is undertaken after you have successfully completed all other courses apart from the Professional Analytics for Decision Making course, with which it may be taken simultaneously if desired.

Program Requirements 

In order to be eligible for the award of a degree, students must:

• successfully complete and pass all core courses (33 credits);
• successfully complete and pass the Capstone Project (3 credits);
• maintain acceptable program performance and remain in good academic standing, which includes maintaining a cumulative GPA of 3.00 or above and no more than 2 “C” grades.
• have no outstanding student disciplinary sanctions or investigations; and
• be in good financial standing.

Course Descriptions

Fundamental Python for Data Science 1 (DAT 501)
3 credits

In this course, you will learn fundamental programming skills that enable you to search and sort data. You will be introduced to programming in Python and will learn how to develop and run programs in Jupyter Notebooks. You will learn key programming principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using data.

Fundamental Python for Data Science 2 (DAT 502)
3 credits

In this course, you will learn fundamental programming skills that enable you to process textual and time-series information. You will further learn programming concepts in Python. You will learn advanced programming principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using data.

Prerequisite: DAT 501

Fundamental Math for Data Science 1 (DAT 505)
3 credits

In this course, you will learn fundamental statistical skills that enable you to analyze data. You will be introduced to basic descriptive and exploratory statistics and will learn how to implement them using software code. You will learn key statistical principles and practice applying them to real business problems. These skills will form the basis of your ability to address business problems using data.

Fundamental Math for Data Science 2 (DAT 506)
3 credits

In this course, you will learn fundamental statistical skills that enable you to analyze data. You will be introduced to basic statistical analysis with a focus on regression models and you will learn how to implement them using software code. You will learn key statistical principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using data.

Prerequisite: DAT 505

Intermediate Python for Data Science (DAT 601)
3 credits

In this course, you will learn advanced programming skills that enable you to process large datasets and access external databases. You will further learn programming concepts in Python. You will learn advanced programming principles and will practice applying them to real business problems. These skills will further enhance your ability to address business problems using data.

Prerequisite: DAT 502

Intermediate Math for Data Science 1 (DAT 605)
3 credits

In this course, you will learn advanced statistical skills that enable you to analyze panel data. You will be introduced to advanced statistical analysis and will learn how to apply them using software code. You will learn key statistical principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using complex data structures.

Prerequisite: DAT 506

Intermediate Math for Data Science 2 (DAT 606)
3 credits

In this course, you will learn advanced statistical skills that enable you to develop experiments and causation analysis. You will be introduced to generalized linear models and will learn how to apply them using software code. You will learn advanced statistical principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using complex statistical techniques.

Prerequisite: DAT 605

Intermediate Analytics for Decision Making (DAT 611)
3 credits

In this course, you will apply the concepts, methodologies, and techniques from previous courses to tackle realistic analytical challenges. You will develop analytical skills and competencies required to inform decision-making processes in business contexts. This course will allow you to consolidate your analytical skills and competencies.

Prerequisite: DAT 502, DAT 606

Machine Learning for Data Science 1 (DAT 705)
3 credits

In this course, you will learn advanced statistical skills that enable you to classify, forecast, and find patterns in data. You will be introduced to clustering, forecasting, and classification techniques, and will learn how to apply them using software code. You will learn basic machine learning principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using machine learning techniques.

Prerequisite: DAT 606

Machine Learning for Data Science 2 (DAT 706)
3 credits

In this course, you will learn advanced techniques that will enable you to use state-of-the-art techniques to process images, and text, classify and forecast data, and will learn how to apply them using software code. You will learn advanced machine learning principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using advanced machine-learning techniques.

Prerequisite: DAT 705

Professional Analytics for Decision Making (DAT 711)
3 credits

In this course, you will apply the concepts, methodologies, and techniques from previous courses to tackle real analytical challenges in collaboration with industrial partners.  You will develop analytical skills and competencies and gain real experience as an aspiring data scientist.

Prerequisite: DAT 601, DAT 706

Capstone Project (DAT 721)
3 credits

In this course, you will apply the concepts, methodologies, and techniques from previous courses to provide a solution to a real business challenge. This course will allow you to consolidate your analytical skills and competencies and gain real experience as an aspiring data scientist.

Prerequisite: DAT 601, DAT 706