Program Overview
Data Science Graduate Certificate
The Data Science Graduate Certificate is a multidisciplinary program that focuses on extracting knowledge and insight from large datasets. There is a growing need for data scientists, data analysts, and statisticians equipped with knowledge and essential skills to work across diverse organizations.
Program Description
The Data Science Graduate Certificate can qualify as credit towards a Master's Degree. The program is designed to prepare students for a successful career in this high-paying, in-demand field. Learners will become proficient in new skills, build a portfolio through hands-on projects, and earn an industry-recognized credential to land the job they want.
Curriculum
To earn the Data Science Graduate Certificate (12 credits), students must complete the following required specializations:
- Data Mining Foundations and Practice Specialization (3 credits)
- DTSA 5504 Data Mining Pipeline (1-credit)
- DTSA 5505 Data Mining Methods (1-credit)
- DTSA 5506 Data Mining Project (1-credit)
- Data Science Foundations: Statistical Inference Specialization (3 credits)
- DTSA 5001 Probability Theory: Foundation for Data Science (1-credit)
- DTSA 5002 Statistical Inference for Estimation in Data Science (1-credit)
- DTSA 5003 Hypothesis Testing in Data Science Applications (1-credit)
As well as two specializations from the following:
- Introduction to Statistical Learning for Data Science Specialization (3 credits)
- DTSA 5020 Regression and Classification (1-credit)
- DTSA 5021 Resampling, Selection, and Splines (1-credit)
- DTSA 5022 Trees, SVM and Unsupervised Learning (1-credit)
- Machine Learning Specialization (3 credits)
- DTSA 5509 Introduction to Machine Learning: Supervised Learning (1-credit)
- DTSA 5510 Unsupervised Algorithms in Machine Learning (1-credit)
- DTSA 5511 Introduction to Deep Learning (1-credit)
- Statistical Modeling for Data Science Specialization (3 credits)
- DTSA 5011 Modern Regression Analysis in R (1-credit)
- DTSA 5012 ANOVA and Experimental Design (1-credit)
- DTSA 5013 Generalized Linear Models and Nonparametric Regression (1-credit)
Skills You Will Gain
- Probability Theory
- Unstructured Data
- Machine Learning
- Artificial Intelligence
- Deep Learning
- Data Visualization
- Big Data Analytics
Required Background
There are no formal prerequisites, but prior knowledge of basic mathematical concepts and computer programming is recommended.
- Math: Calculus and Linear Algebra
- Programming: Python and R Programming
If you do not have this knowledge already, consider taking non-credit coursework before attempting for-credit courses.
Price
The Data Science Graduate Certificate on Coursera is $525 per credit hour. The program requires 12 credit hours of coursework, so the total program cost is $6,300.
Earn Credit towards a Master's Degree
This Graduate Certificate can be stacked toward the Master of Science in Data Science on Coursera degree if you are interested in continuing your education. The Data Science Foundations Specialization, a requirement to earn this Data Science Graduate Certificate, is a pathway for admissions to the Master of Science in Data Science (MS-DS) degree program.
To pursue admission to the MS-DS degree program, you'll need to complete the following four required protocols:
- Pass one pathway with a pathway GPA of 3.0 or higher
- Earn a C or better in all pathway courses within your chosen pathway
- Earn an overall cumulative GPA of 3.0 or higher
- Indicate interest in degree admission (via the enrollment form)
By completing these steps, you will go from working on a certificate to earning a degree. Upon completion of the Data Science Graduate Certificate, you can apply these 12 credits to the Master of Science in Data Science degree.
Master of Science in Data Science
The Master of Science in Data Science is offered through an interdisciplinary partnership and is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science. The program is awarded by the College of Engineering & Applied Science.