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

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

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

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 - 61 hours

Relational Database Design - 36 hours
The Structured Query Language (SQL) - 25 hours
Advanced Topics and Future Trends in Database Technologies - 17 hours

Elective Courses:

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

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)
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

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

Core:

[xx] 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

Electives:

[ ] 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

ljluestc/MSDS