Master of Science in Data Science

Vital Skills for Data Scientists (4 credits)

1. Data Science as a field
2. Ethical issues in Data Science
3. Cybersecurity for Data Science
4. Fundamentals of Data Visualization

Vital Skills for Data Science introduces students to several areas that every data scientist should be familiar with. Each of the topics is a field in itself. This specialization provides a "taste" of each of these areas which will allow the student to determine if any of these areas is something they want to explore further. In this specialization, students will learn about different applications of data science and how to apply the steps in a data science process to real life data. They will be introduced to the ethical questions every data scientist should be aware of when doing an analysis. The field of cybersecurity makes the data scientist aware of how to protect their data from loss.

Data Mining Foundations and Practice Specialization (3 credits)

1. Data Mining Pipeline
2. Data Mining Methods
3. Data Mining Project

The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.

Machine Learning: Theory and Hands-on Practice with Python Specialization (3 credits)

1. Introduction to Machine Learning: Supervised Learning
2. Unsupervised Algorithms in Machine Learning
3. Introduction to Deep Learning

In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.

Statistical Modeling for Data Science Applications Specialization(3 credits)

1. Modern Regression Analysis in R
2. ANOVA and Experimental Design
3. Generalized Linear Models and Nonparametric Regression

Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.

Databases for Data Scientists Specialization (2 credits)

1. Relational Database Design
2. The Structured Query Language (SQL)
3. Advanced Topics and Future Trends in Database Technologies (elective)

Whether you are a beginning programmer with an interest in Data Science, a data scientist working closely with content experts, or a software developer seeking to learn about the database layer of the stack this specialization is for you! We focus on the relational database which is the most widely used type of database. Relational databases have dominated the database software marketplace for nearly four decades and form a core, foundational part of software development.

• Introduction to High-Performance and Parallel Computing (1 credit)
• Managing, Describing, and Analyzing Data (1 credit)
• Stability and Capability in Quality Improvement (1 credit)
• Measurement Systems Analysis (1 credit)
• Deep Learning Applications for Computer Vision (1 credit)
• Effective Communication: Writing, Design, and Presentation Specialization (2 credits)
• Regression and Classification (1 credit)
• Fundamentals of Software Architecture for Big Data (1 credit)
• Software Architecture Patterns for Big Data (1 credit)
• Applications of Software Architecture for Big Data (1 credit)
• Supervised Text Classification for Marketing Analytics (1 credit)
• Unsupervised Text Classification for Marketing Analytics (1 credit)
• Network Analysis for Marketing Analytics (1 credit)
• Product Cost and Investment Cash Flow Analysis (1 credit)
• Project Valuation and the Capital Budgeting Process (1 credit)
• Financial Forecasting and Reporting (1 credit)
• Project Management: Foundations and Initiation (1 credit)
• Project Planning and Execution (1 credit)
• Agile Project Management (1 credit)
• Fundamentals of Software Architecture for Big Data
• Software Architecture Patterns for Big Data
• Applications of Software Architecture for Big Data
• IBM Applied Data Science Capstone (1 credit)

Elective courses are also required to earn the degree, but allow more flexibility. Students must complete 9 elective credits to earn the degree, and can choose from a variety of available options.