Data Science Graduate Certificate
Data Science Graduate Certificate Program
Develop interdisciplinary skills in data science and gain knowledge of statistical analysis, data mining, and machine learning from one of the nation’s top-ranked Tier 1 research institutions.
Data Science Graduate Certificate
The on-campus Master of Science in Data Science program focuses on developing knowledge and skills in interdisciplinary and collaborative data science competencies including statistical analysis, data structures and algorithms, data mining, machine learning, big data architecture and data visualization. The on-campus program offers a stackable graduate certificate that can be earned on its own or applied toward the full master’s degree.
Graduates of the certificate and/or full master’s degree program will be well-prepared to apply data science skills to a specific domain area. Graduates will also be able to clearly communicate the results of data science analysis to a non-technical audience; structure effective meetings and projects using collaboration skills; and act ethically in the role of professional data scientist.
The residential Data Science Graduate Certificate requires 12 credit hours of coursework. Students must complete the required courses.
There are no formal prerequisites, but we recommend that you have prior knowledge of basic mathematical concepts and computer programming.
- Math: Calculus and Linear Algebra
- Programming: Python and R Programming
If you do not have this knowledge already, we encourage you to try out non-credit coursework before attempting for-credit courses.
If you would like to brush up on the above skills before starting the program, consider the following classes on Coursera:
- Calculus:
- Linear Algebra:
- R Programming: by Johns Hopkins University
- Python: by Rice University
What We Look For
Residential Data Science Graduate Certificate will be primarily for students who meet either of the following criteria:
- Currently matriculated Â鶹Ãâ·Ñ°æÏÂÔØBoulder residential or online (Canvas) graduate student in a participating department on Main Campus.
- Graduate or non-degree seeking students in other disciplines with an interest in data science.
Students are required to have an awarded bachelor's degree to be admitted into the residential Data Science or Online (Canvas) Graduate Certificate and will be subject to graduate main campus graduate certificate policies for admission/award.
*Program is not eligible to enroll F-1 and M-1 students in the United States.
Applications
For more information contact MS-DS .
Current Â鶹Ãâ·Ñ°æÏÂÔØstudent, staff, or faculty:
Continuing Education Students:
Important Dates
Applicants for admission to the MS-DS certificate must contact residential graduate advisor and enroll by the deadline below. Incomplete applications will not be considered.
Contact : At least 2 weeks before semester start. (fall/spring)
Application Close: 10 days before classes start (Check classes start date )
Application Fee
No application fee!
Recommendation letters
No recommendation letters!
Transcripts
We need official or unoffical transcripts.
The residential Data Science Graduate Certificate requires 12 credit hours of coursework. Students must complete the required courses listed below.
In order to earn a certificate, students must receive a minimum grade of a C or higher in each course. The cumulative GPA for certificate courses must be 3.0 or higher.
Required Courses
Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in large-scale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining-specific data types such as time-series, social networks, multimedia, and Web data.
- Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language.
- View Syllabus (Coming soon)
Choose two courses from the following:
- Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language.
- View Syllabus (Coming soon)
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning, and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.
Provides an introduction to methods in the field of statistical learning. Topics include a review of multiple regression, assessing model accuracy, classification, resampling methods, model selection and regularization, nonlinear regression, tree-based methods, support vector machines and unsupervised learning. Involves hands-on data analysis using the R programming language.
Requisites: with a grade of C- or higher AND (MS-DS major OR Department Consent)
- Acquire, clean, wrangle, and manage data
- Correctly perform exploratory data analysis in order to assist with the generation of scientific hypotheses
- Apply principles and methods of probability theory and statistics to draw rational conclusions from data
- Construct an appropriate statistical model in order to answer important scientific or business-related questions
- Assess the validity of a statistical model when applied to a particular dataset
- Use statistical techniques to design an experiment
- Understand and be able to apply the main computational techniques used to analyze large data sets, including a variety of data mining and machine learning approaches
- Understand the principles of computer representation, storage and access of large data sets and be able to determine the appropriate approaches for specific problem
- Clearly communicate the results of a data science analysis to a non-technical audience
Learn more about tuition fees .
Â鶹Ãâ·Ñ°æÏÂÔØBoulder is committed to teaching the next generation of interdisciplinary data scientists.