# Data Science Certificate Information

## Information you will find here:

• Data Science Certificate Course Descriptions
• Preparatory Course Descriptions (not part of the Certificate Program)
• Example Programs of Study
• Cost to Attend

Those wishing to pursue the Data Science Certificate should have the following background.

• An introductory course in statistics equivalent to SDSU course STAT 281 Introduction to Statistics. For those needing such a course, STAT 281 is available online every fall, spring and summer.
• Prior statistical programming experience is strongly recommended, including some experience with SAS, R and SQL. SDSU online courses STAT 510 SAS Programming I (3 credits), STAT 514 Basic R Programming (1 credit) or STAT 515 R Programming (3 credits) provide the necessary background. It is also possible to self‐teach most of this material using available online resources.

### Data Science Certificate Course Descriptions

• STAT 541 Statistical Methods II ‐ Analysis of variance, various types of regression and other statistical techniques and distributions. Sections offered in the areas of Biological Science and Social Science. Prerequisites: STAT 281, MATH 381 or STAT 381. Credit not given for both STAT 541 and STAT 582.
• STAT 600 Statistical Programming - Fundamentals of statistical programming languages including descriptive and visual analytics in R and SAS, and programming fundamentals in R and SAS including logic, loops, macros and functions.
• STAT 601 Modern Applied Statistics I - Topics include statistical graphics, modern statistical computing languages, nonparametric and semiparametric statistical methods, longitudinal and repeated measures, metaanalysis and large‐scale inference. Prerequisite: STAT 600, STAT 541 or equivalent.
• STAT 602 Modern Applied Statistics II - Topics include data mining techniques for multivariate data, including principal component analysis, multidimensional scaling and cluster analysis; supervised learning methods and pattern recognition; and an overview of statistical prediction analysis relevant to business intelligence and analytics. Prerequisite: STAT 601

### Preparatory Course Descriptions (not part of the Certificate Program)

• STAT 281 Introduction to Statistics ‐ A study of descriptive statistics including graphs, measures of central tendency and variability and an introduction to probability theory, sampling and techniques of statistical inference with an emphasis on statistical applications.
• STAT 510 SAS Programming I ‐ The Base SAS programming language for data reading and manipulation, data display, summarization and graphing. Introduction to statistical procedures, high resolution graphics, the Output Delivery System and some menu‐driven interfaces. Includes basic SQL techniques.
• STAT 514 Basic R Programming (1 credit) – introduction to the R programming language. Topics will include the R programming language and environment, preparation and summarization of data, presentation of data, programming basics and additional selected advanced topics.
• STAT 515 R Programming (3 credits) - The R programming language and environment, preparation and summarization of data, programming basics, data presentation and visualization, app creation and advanced programming techniques.

### Sample Programs of Study

#### Base plan of study, for those having all necessary background:

• Spring I - Stat 541, 3 credits
• Summer - Stat 600, 3 credits
• Fall - Stat 601, 3 credits
• Spring II - Stat 601, 3 credits

#### Alternate plan of study for those needing statistical programming background:

• Fall I - Stat 510, 3 credits
• Spring I - Stat 541, 3 credits; Stat 514, 1 credit
• Summer - Stat 600, 3 credits
• Fall II - Stat 601, 3 credits
• Spring II - Stat 602, 3 credits

### Cost to Attend

For the 2018/19 academic year, cost per credit hour will be \$410.75 for state residents and \$711.55 for non‐residents.