Master of Applied Statistics, Plan C, Data Science Specialization

Program Overview

The Master of Applied Statistics, Plan C, Data Science Specialization at Colorado State University provides students with practical skills in statistics and data science. Students will gain a strong foundation in statistical theory, machine learning, and statistical consulting. The program is designed to prepare graduates for careers in data science, including positions such as Data Scientist, Data Analyst, and Machine Learning Engineer.

Program Outline

Degree Overview:

The Master of Applied Statistics, Plan C, Data Science Specialization at Colorado State University is designed to equip students with practical skills in statistics and data science, focusing on real-world applications and computational aspects rather than theoretical foundations. The program aims to prepare graduates for immediate employment as data scientists in various sectors, including business, industry, and government. The program emphasizes a strong foundation in statistical and business computing, enabling students to effectively analyze and interpret data. Students who excel in data science typically possess strong quantitative skills, analytical thinking abilities, and a desire to solve problems.

Outline:

The program requires a minimum of 30 credits for completion. Students can choose to pursue the degree full-time, completing it in less than a year, or part-time, either online or on campus.

Required Courses:

• CIS 605 Business Visual Application Development (3 credits)
• CIS 655 Business Database Systems (3 credits)
• STAA 551 Regression Models and Applications (2 credits)
• STAA 552 Generalized Regression Models (2 credits)
• STAA 553 Experimental Design (2 credits)
• STAA 555/STAT 555 Statistical Consulting Skills (1 credit)
• STAA 556 Statistical Consulting (2 credits)
• STAA 561 Probability with Applications (2 credits)
• STAA 562 Mathematical Statistics with Applications (2 credits)
• STAA 565 Quantitative Reasoning (1 credit)
• STAA 577 Statistical Learning and Data Mining (2 credits)
• STAA 578 Machine Learning (2 credits)

Electives:

Students must choose 3 credits from the following:

• STAA 566 Data Visualization Methods
• STAA 567 Computational and Simulation Methods
• STAA 572 Nonparametric Methods
• STAA 573 Analysis of Time Series
Students must also choose 3-4 credits from the following electives:
• CIS 570 Business Intelligence
• CIS 575 Applied Data Mining and Analytics in Business
• STAA 554 Mixed Models
• STAA 574 Methods in Multivariate Analysis
• STAA 575 Applied Bayesian Statistics

Careers:

The program prepares graduates for a variety of data science roles in various industries. Potential career paths include:

• Data Scientist
• Data Analyst
• Machine Learning Engineer
• Business Analyst
• Quantitative Analyst