The University of Colorado Boulder - Master of Science degree in Data Science MSDS

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Pathway:

Data Science Foundations: Data Structure and Algorithms - 107 hours

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

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Vital Skills:

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

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Core Courses:

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 - 53 hours

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

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Elective Courses:

Databases - 25 hours

• The Structured Query Language (SQL) - 25 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

• Deep Learning Applications for Computer Vision - 23 hours

Data Science Methods for Quality Improvement Specialization - 42 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

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

Finance for Technical Managers Specialization - 50 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

Statistical Learning for Data Science

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

Software Architecture for Big Data Specialization - 63 hours

• 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

Modeling and Predicting Climate Anomalies - 44 hours

• Global Climate Change Policies and Analysis - 14 hours
• Modeling Climate Anomalies with Statistical Analysis - 7 hours
• Predicting Extreme Climate Behavior with Machine Learning - 23 hours

Elective courses (9 credits)

• 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)
• Global Climate Change Policies and Analysis (1 credit)
• Modeling Climate Anomalies with Statistical Analysis (1 credit)
• Predicting Extreme Climate Behavior with Machine Learning (1 credit)
• Internet Policy (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

Finished:

Expressway:

[X] Expressway to Data Science: Essential Math Specialization

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

[X] Expressway to Data Science: Python Programming Specialization

• [x] Introduction to Python Fundamentals
• [x] Introduction to Python Functions
• [x] 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

Core:

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

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

[X] Vital Skills for Data Science Specialization

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

[x] Data Science Foundations: Statistical Inference Specialization

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

[x] Databases for Data Scientists Specialization

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

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

• [x] Introduction to Machine Learning: Supervised Learning
• [x] Unsupervised Algorithms in Machine Learning
• [x] Introduction to Deep Learning

[x] Statistical Modeling for Data Science Applications

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

[x] Data Mining Foundations and Practice

• [x] Data Mining Pipeline
• [x] Data Mining Methods
• [x] Data Mining Project

Electives:

[x] Databases

• [x] The Structured Query Language (SQL) - 25 hours

[x] Project Management

• [x] Foundations and Initiation
• [x] Project Planning and Execution
• [x] Agile Project Management

[x]