The online graduate certificate in Data Science is designed for professionals with a background in computer science, who want to enrich their expertise in this important field.
Offered by the J.B. Speed School of Engineering at the University of Louisville, the Data Science Certificate delivers comprehensive knowledge and skills through just 6 intensive courses. Developed collaboratively by UofL’s Department of Computer Engineering and Computer Science (CECS) and Department of Mathematics, this program prepares you to meet the growing demand for trained data scientists in today’s technology-driven world.
"While I had a lot of experience in data analysis and reporting, I lacked the formal training of a Data Scientist. I decided to [earn] the Data Science certificate because I wanted to learn the most cutting edge skills. I've always liked working with numbers, and I've always been fascinated by data science—and the more and more I get into it, the more interesting things I want to do."
$764 per credit hour
$250 per credit hour active-duty rate
Data scientists are specialists in finding hidden connections in large masses of stored data, and are critical to industries including insurance, health care, retail, education, banking, manufacturing, pharmaceuticals, biotechnology, travel, government and intelligence. In today's economy, organizations across all industries, can reap the enormous payoffs of data science.
Graduates of this program are well suited to apply their credentials to various fields and may obtain positions as:
|Application Deadline||Term||Start Date|
Send all materials to:
University of Louisville
2211 S. Brook Street
Louisville, KY 40292
For more information on the admission and application process, please contact our Online Learning Enrollment Counselor at 800.871.8635 or by email at email@example.com.
The online graduate Data Science Certificate is an 18 credit hour program that requires 9 credit hours in core courses and 9 credit hours in electives. You will have the option of choosing any three of the electives listed below, specializing your certificate to match your interests or career goals.
CECS 535 Intro to Databases
or CECS 536 Data Management and Analysis
|CECS 632 Data Mining||3|
|CECS 635 Data Mining with Linear Models||3|
|Total Required Hours||9|
|Course [choose 3]||Hours|
|CECS 522 Performance Evaluation of Computer Systems||3|
|CECS 545 Artificial Intelligence||3|
|CECS 619 Design and Analysis of Computer Algorithms||3|
|CECS 621 Web Mining for E-Commerce||3|
|CECS 622 Simulation and Modeling of Discrete Systems||3|
|CECS 630 Advanced Databases and Data Warehousing||3|
|CECS 660 Introduction to Bioinformatics||3|
|CECS 694 Special Topics in Data Mining: Legal Issues in Data Mining||3|
|CECS 694 Special Topics in Data Mining: BIG DATA: Document-oriented DB||3|
|MATH 560 Statistical Data Analysis||3|
|MATH 561 Probability||3|
|MATH 562 Mathematical Statistics||3|
|MATH 667 Statistical Inference||3|
|Total Required Hours||9|
CECS 522 Performance Evaluation of Computer Systems
A study of approaches to the evaluation of computer systems. Measurement techniques and evaluation techniques are treated in detail with attention to existing commercial hardware and software monitors and simulators.
CECS 535 Introduction to Databases
Course covers basics of database design, SQL, query processing, optimization, and transactions. The emphasis will be placed on Engineering design and implementation of relational systems. A written project is required.
CECS 536 Data Management and Analysis
The goal of the course is to teach, to the students who are not Computer Science majors, the basic skills needed to organize, assess and analyze data sets. The course discusses a variety of tools (file systems, database systems, and the R environment) as well as a series of basic tasks, from generating metadata to basic filtering, organizing and enrichment of data sets. This course contributes to develop analysis, modeling and problem-solving skills.
CECS 545 Artificial Intelligence
This course introduces the use of predicate calculus logic, heuristic search, and knowledge representations for solving engineering and computer science problems. The course includes coverage of rule-based expert systems, intelligent agents, and machine learning.
CECS 619 Design and Analysis of Computer Algorithms
This course covers the interrelationship between algorithmic statements, data structures, and computational complexity of computer programs. Algorithms are presented for a number of computer science and engineering applications including graph problems, string matching, dynamic programming, transitive closure, and convolution. The properties of NP-complete problems are introduced.
CECS 621 Web Mining for E-Commerce and Information Retrieval
Fundamentals of knowledge discovery in semi-structured/unstructured data with emphasis on the World Wide Web: Web usage, content, and structure mining, applications to personalization, e-commerce, information retrieval, text mining.
CECS 622 Simulation and Modeling of Discrete Systems
Engineering design of simulation languages and simulators, discrete stochastic systems, issues in large scale simulation studies and engineering evaluation methods.
CECS 630 Advanced Databases
Object-relational databases; handling of complex types, XML and text in relational databases. NoSQL databases: data models and query languages. Data warehousing: design and implementation, query processing and optimization. Big Data: cluster computing, MapReduce and extensions, advanced analytical databases, and distributed query processing.
CECS 632 Data Mining
Data mining concepts, methodologies, and techniques, including statistical and fuzzy inference, cluster analysis, artificial neural networks, and genetic algorithms, rule association and decision trees, N-dimensional visualization, Web and text mining, and advanced topics.
CECS 635 Data Mining with Linear Models
This course covers the theory and practice of linear models and mixed models as applied to different types of data.
CECS 660 Introduction to Bioinformatics
Covers the current state of the art programs designed for sequence alignment, database searching, RNA structure prediction, microarray, sequence analysis, gene prediction, repeat detection, and protein folding prediction. A detailed analysis of the algorithms behind each of these will be explored. The algorithmic techniques discussed will include dynamic programming, hidden Markov models, finite state automata, grammars, Karlin-Altschul statistics and Bayesian statistics.
MATH 560 Statistical Data Analysis
Descriptive techniques, inferential techniques, simple and multiple linear regression. Frequent use of statistical computer packages. No previous knowledge of the computer required.
MATH 561 Probability
Probability spaces, probability distributions, moments, moment-generating functions, independence, transformation of variables, sampling distributions, laws of large numbers, central limit theorem, applications.
MATH 562 Mathematical Statistics
Random samples and statistics, point estimation, sufficiency and completeness, confidence regions, classical theory of hypothesis testing, linear regression, non-classical procedures.
MATH 667 Statistical Inference
Advanced topics in mathematical statistics such as sampling distributions, exponential families, sufficiency, point and interval estimation, likelihood-based inference, hypothesis testing, Bayesian inference, statistical decision theory, and asymptotic theory.
The faculty at the University of Louisville are leaders in the field of Data Science, Data Warehousing, and Data Analysis. Their expertise spans the use of text, web, numerical, and image databases. They have worked in application areas such as medical, e-commerce, security, military, institutional, and financial research. Our faculty have authored or contributed to textbooks and professional publications and have received research awards from the National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institutes of Health (NIH), and Department of Defense (DoD).