Master of Science in Applied Data Science and Machine Learning (MSADSML) Program

Program Overview

The primary aim of this program is to train recent graduates in securing their first job as entry-level data scientists or business analysts, as well as to assist working professionals in transitioning to more quantitative roles in their respective industries. 

In order to create and provide courses effectively, the program utilizes a problem-based learning method. This approach is esteemed for its ability to cultivate advanced competences and versatile skills valued by both public and private industries. By following this method, students gradually gain and enhance a comprehensive and precisely tailored set of skills which includes Python programming, statistical modeling, deep machine learning, and decision theory within the realm of business analytics.

The program is divided into three stages. Stage 1 will ensure that students acquire basic programming skills and introduce them to basic statistical analysis techniques and data visualization. Stage 2 will expose students to advanced programming and statistical analysis, and Stage 3 will introduce them to machine learning techniques and allow them to apply their skills in industry contexts.

Overall, this program equips students with relevant skills and knowledge to excel in the data science and business analytics fields, catering to the needs of both entry-level job seekers and professionals looking for a quantitative career transition. 

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 Competences

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

Technical Competences

• 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 Competences

• Visualize 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 consists of a total of 36 credits and encompasses 11 core courses along with a Capstone Project. It is structured to be completed in a specific sequence, as indicated by the prerequisites mentioned in the Course Descriptions section. The Capstone Project serves as a platform to integrate and apply the knowledge acquired throughout the degree program. Consequently, the Capstone Project course is taken after successfully fulfilling all other course requirements, except for the Machine Learning for Data Science 2 course, which can be pursued simultaneously if desired.

Program Requirements

In order to be eligible for the award of 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

All courses will be offered either 100% online or in hybrid format, depending on the program format that students have selected.

Python for Data Science 1 (MTI-521)
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 learn how to develop and run programs in Jupyter Notebooks. You will learn key programming principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using data.

Python for Data Science 2 (MTI-522)
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 or industry problems. These skills will form the basis of your ability to address business or industry problems using data.
Prerequisite: Python for Data Science 1

Math for Data Science 1 (MDS-511)
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 or industry problems. These skills will form the basis of your ability to address business or industry problems using data.

Math for Data Science 2 (MDS-512)
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 learn how to implement them using software code. You will learn key statistical principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using data.
Prerequisite: Math for Data Science 1

Python for Data Science 3 (MTI-621)
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 practice applying them to real business or industry problems. These skills will further enhance your ability to address business or industry problems using data.
Prerequisite: Python for Data Science 2

Math for Data Science 3 (MDS-641)
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 learn how to apply it using software code. You will learn key statistical principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using complex data structures.
Prerequisite: Math for Data Science 2

Math for Data Science 4 (MDS-642)
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 learn how to apply them using software code. You will learn advanced statistical principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using complex statistical techniques.
Prerequisite: Math for Data Science 3

Analytics for Decision Making (MDS-741)
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 Competences required to inform decision-making processes in business and industry contexts. This course will allow you to consolidate your analytical skills and Competences.
Prerequisites: Python for Data Science 3, Math for Data Science 3

Machine Learning for Data Science 1 (MDS-731)
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 learn how to apply them using software code. You will learn basic machine learning principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using machine learning techniques.
Prerequisites: Python for Data Science 3, Math for Data Science 3

Machine Learning for Data Science 2 (MDS-732)
3 credits

In this course you will learn advanced techniques that will enable you to use state of art techniques to process images, 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: Machine Learning for Data Science 1

Generative AI for Data Science 1 (MTI-771)
3 credits

In this module you will learn how to apply generative artificial intelligence to process and transform complex, unstructured datasets. You will be introduced to prompt engineering and learn how to apply it

for analytical purposes. These skills will complement your ability to process textual information and extract relevant insights from it.
Prerequisites: Python for Data Science 3, Math for Data Science 3

Capstone Project (MAF-731)
3 credits

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