The University of Colorado Boulder - Master of Science degree in Data Science MSDS
- Algorithms for searching, sorting and indexing - 35 hours
- Trees and graphs: basics - 34 hours
- Dynamic programming, Greedy algorithms - 38 hours
Vital skills for Data Scientists - 68 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
Data Science Foundations: Statistical Inference - 113 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 Foundations and Practice - 82 hours
- Data Mining Pipeline - 21 hours
- Data Mining Methods - 23 hours
- Data Mining Projects - 38 hours
Statistical Modeling for Data Science - 127 hours
- Modern Regression analysis in R - 45 hours
- ANOVA Experimental Design - 40 hours
- Generalized Linear Models and nonparametric regression - 42 hours
Machine Learning - 139 hours
- Introduction to Machine Learning: Supervised Learning - 40 hours
- Unsupervised Algorithms in Machine Learning - 38 hours
- Introduction to Deep Learning - 61 hours
Databases - 61 hours
- Relational Database Design - 36 hours
- The Structured Query Language (SQL) - 25 hours
- Advanced Topics and Future Trends in Database Technologies - 17 hours
Text marketing analytics - 35 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
Effective Communication - 74 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
[ ] 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
[x] 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