• Algorithms for searching, sorting and indexing - 35 hours
• Trees and graphs: basics - 34 hours
• Dynamic programming, Greedy algorithms - 38 hours

• Data Science as a field - 11 hours
• Cybersecurity for data science - 19 hours
• Ethical issues in Data Science - 24 hours
• Visualization fundamentals - 14 hours

• Probability theory: Applications for Data Science - 48 hours
• Statistical Inference for estimation in Data Science - 28 hours
• Statistical Inference and Hypothesis testing in Data Science Applications - 37 hours

• Data Mining Pipeline - 21 hours
• Data Mining Methods - 23 hours
• Data Mining Projects - 38 hours

• Modern Regression analysis in R - 45 hours
• ANOVA Experimental Design - 40 hours
• Generalized Linear Models and nonparametric regression - 42 hours

• Introduction to Machine Learning: Supervised Learning - 40 hours
• Unsupervised Algorithms in Machine Learning - 38 hours
• Introduction to Deep Learning - 61 hours

• Relational Database Design - 36 hours
• Advanced Topics and Future Trends in Database Technologies - 17 hours

• The Structured Query Language (SQL) - 25 hours

• Supervised Text Classification for Marketing Analytics - 12 hours
• Unsupervised Text Classification for marketing analytics - 13 hours
• Network analysis for marketing analytics - 10 hours

• Deep Learning Applications for Computer Vision - 23 hours

• Managing, Describing, and Analyzing Data - 17 hours
• Stability and Capability in Quality Improvement - 9 hours
• Measurement Systems Analysis - 16 hours

• Introduction to High-Performance and Parallel Computing - 24 hours

• Product Cost and Investment Cash Flow Analysis - 20 hours
• Project Valuation and the Capital Budgeting Process - 17 hours
• Financial Forecasting and Reporting - 13 hours

• Business Writting - 12 hours
• Graphic Design - 29 hours
• Successful Presentation - 20 hours
• Effective communication capstone project - 13 hours

• Regression and Classification - 34 hours
• Resampling, Selection, and Splines - Coming soon!
• Trees, SVM, and Unsupervised Learning - Coming soon!

• Applications of Software Architecture for Big Data - 17 hours
• Fundamentals of Software Architecture for Big Data - 23 hours
• Software Architecture Patterns for Big Data - 23 hours

• Deep Learning Applications for Computer Vision (1 credit)
• Regression and Classification (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)
• Managing, Describing, and Analyzing Data (1 credit)
• Stability and Capability in Quality Improvement (1 credit)
• Measurement Systems Analysis (1 credit)
• Introduction to High-Performance and Parallel Computing (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)
• Effective Communication: Writing, Design, and Presentation Specialization (2 credits)
• 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)
• Project Management: Foundations and Initiation (1 credit)
• Project Planning and Execution (1 credit)
• Agile Project Management (1 credit)
• IBM Applied Data Science Capstone (1 credit)

Students must complete 9 elective credits to earn the degree, and can choose from a variety of available options.

https://www.coursera.org/degrees/master-of-science-data-science-boulder/academics

[X] Expressway to Data Science: Essential Math Specialization

• Algebra and Differential Calculus for Data Science
• Essential Linear Algebra for Data Science
• Integral Calculus and Numerical Analysis for Data Science

[X] Expressway to Data Science: Python Programming Specialization

• Introduction to Python Fundamentals
• Introduction to Python Functions
• Python Packages for Data Science

[ ] Expressway to Data Science: R Programming and Tidyverse

• Introduction to R Programming and Tidyverse
• Data Analysis with Tidyverse
• R Programming and Tidyverse Capstone Project

[X] Data Science Foundations: Data Structures and Algorithms Specialization

• Algorithms for Searching, Sorting, and Indexing
• Trees and Graphs: Basics
• Dynamic Programming, Greedy Algorithms

[X] Vital Skills for Data Science Specialization

• Cybersecurity for Data Science
• Data Science as a Field
• Ethical Issues in Data Science
• Fundamentals of Data Visualization.

[x] Data Science Foundations: Statistical Inference Specialization

• Probability Theory: Foundation for Data Science
• Statistical Inference for Estimation in Data Science
• Statistical Inference and Hypothesis Testing in Data Science Applications

[ ] Databases for Data Scientists Specialization

• Relational Database Design
• The Structured Query Language (SQL)
• Advanced Topics and Future Trends in Database Technologies

[ ] Machine Learning: Theory and Hands-on Practice with Python

• Introduction to Machine Learning: Supervised Learning
• Unsupervised Algorithms in Machine Learning
• Introduction to Deep Learning

[ ] Statistical Modeling for Data Science Applications

• Modern Regression Analysis in R
• ANOVA and Experimental Design
• Generalized Linear Models and Nonparametric Regression

[ ] Data Mining Foundations and Practice

• Data Mining Pipeline
• Data Mining Methods
• Data Mining Project

[ ] Data Science Methods for Quality Improvement

• Managing, Describing, and Analyzing Data
• Stability and Capability in Quality Improvement
• Measurement Systems Analysis

[x] Deep Learning Applications for Computer Vision

• Deep Learning Applications for Computer Vision

[ ] Finance for Technical Managers Specialization

• Product Cost and Investment Cash Flow Analysis
• Project Valuation and the Capital Budgeting Process
• Financial Forecasting and Reporting