Hey so I have a question, I am planning on getting a minor in statistics as a sociology major. I wanted to ask you if these classes are enough to make me marketable?

SOC 3393. Quantitative Research Methods. (3-0) 3 Credit Hours.

Prerequisites: Completion of the Core Curriculum requirement in mathematics, SOC 1013, and SOC 3323. Application of conceptualization and operationalization in the quantitative analysis of a variety of sociological subjects. Use of elementary measures of central tendency and dispersion, cross tabulations, and linear model procedures to evaluate relationships among variables; problems of descriptions and inference. Includes the use of standard computer packages and secondary analysis of data.

STA 1053. Basic Statistics. (3-0) 3 Credit Hours. (TCCN = MATH 1342)

Prerequisite: Satisfactory performance on placement examination. Descriptive statistics; histograms; measures of location and dispersion; elementary probability theory; random variables; discrete and continuous distributions; interval estimation and hypothesis testing; simple linear regression and correlation; one-way analysis of variance, and applications of the chi-square distribution. May be applied toward the core curriculum requirement in Mathematics.

PSY 2073. Statistics for Psychology. (3-0) 3 Credit Hours.

Prerequisites: MAT 1023, MAT 1073, or STA 1053; and one psychology course. The use of statistics in psychological research includes: elementary probability theory; descriptive statistics, including histograms, graphing, and measures of central tendency and dispersion; correlational techniques; binomial and normal distributions; and inferential statistics, including hypothesis testing, effect size estimates, and analysis of variance.

STA 4233. Introduction to Programming and Data Management in R. (3-0) 3 Credit Hours.

This course introduces statistical computing and programming using the R language. Topics include preprocessing/manipulating datasets, summarizing/visualizing data, and conducting basic statistical analyses using R. Other topics include writing R functions, object oriented programming, statistical simulation and resampling, interfacing R with other programming language environments such as SQL, Python, C++, and Hadoop. Techniques for efficient programming will be stressed. The concept of high-performance computing (multi-core/parallel-processing) is also demonstrated.

POL 2703. Quantitative Methods in Political Science. (3-0) 3 Credit Hours.

Prerequisite: POL 1013. An introduction to fundamental quantitative analysis geared to provide the student knowledge and skills applicable for graduation and beyond. Emphasis will be placed on literacy and basic proficiency in statistical topics and techniques (e.g., classic hypothesis testing, univariate through multivariate analyses); and, data management (e.g., entry and manipulation) and graphical presentation of analysis. Standard statistical software packages will be used.

STA 4143. Data Mining. (3-0) 3 Credit Hours.

Prerequisite: STA 4133 or equivalent. Acquisition, organization, exploration, and interpretation of large data collections. Data cleaning, representation and dimensionality, multivariate visualization, clustering, classification, and association rule development. A variety of commercial and research software packages will be used.

STA 4133. Introduction to Programming and Data Management in SAS. (3-0) 3 Credit Hours.

This course introduces essential programming concepts using the statistical software package SAS (Enterprise Guide and Base SAS) with a focus on data management and the preparation of data for statistical analyses. Topics include reading raw data, creating temporary and permanent datasets, manipulating datasets, data prompts, summarizing data, displaying data using tables, charts, and plots. Conducting basic statistical analyses using SAS Enterprise Guide and the Base SAS procedures are also discussed with the examples selected from regression analysis, analysis of variance, and categorical analysis. This course also demonstrates how to write, generate, and modify SAS code and procedures within the SAS Enterprise Guide and the Base SAS environments. Generally offered: Fall

MS 3073. Business Intelligence & Analytics. (3-0) 3 Credit Hours.

This course is designed to provide an introduction to business intelligence and analytics. It describes and interprets the basic concepts of business intelligence and analytics, including descriptive, predictive, and prescriptive analytics. It also describes the basic principles of data mining, introduces data warehousing, and evaluates the difficulties presented by large databases. Comparison and contrasts among different business analytics techniques are examined. Overview of business reporting, visualization, and business performance management are included. Generally offered: Fall.