Data Analytics

An introduction to computational problem-solving using Python. Hands-on labs are used to motivate basic programming concepts, including basic data types and structures, functions, conditionals, and loops. Additional topics may include building and scraping HTML webpages. The course is recommended for all who wish to explore data science and/or computer science.

Prerequisite(s): Math ACT score of at least 21 or permission of instructor.

A study of managing, manipulating, and summarizing data using Excel and SQL. Topics in Excel include, but are not limited to: functions, filters, charts and visualizations, pivot tables, and macros. Topics in SQL include, but are not limited to: queries, joins, and basic database management.

An introduction to data analytics from three perspectives: inferential thinking, computational thinking and real-world relevance. Topics include, but are not limited to: organizing real-world data by filtering, sorting, and using pivot tables; exploring data; visualizing data; using programming tools to analyze data through a statistical lens. Statistical topics include: center and spread of data, descriptive statistics, inferential statistics, regression, causality, classification and prediction.

A study of data visualization, including principles and techniques. Students will analyze the effectiveness of visualizations, create a wide array of visualizations using the programming language R, and communicate a story through them. Significant emphasis will be placed on getting and cleaning data.

Prerequisite(s): Grade of "C" or better inCMPSC 1100 Python Programming I and grade of "C" or better in one of the following statistics courses: BUSAD 2100 Business and Economic Statistics, MATH 1300 Statistics, MATH 3100 Differential Equations, POLSC 2000 Introduction to Political Science Statistics, PSYCH 2100 Psychological Statistics, or SOC 2910 Social Statistics.

An introduction to descriptive and inferential statistics. Topics include gathering, organizing, interpreting, and presenting data with emphasis on hypothesis testing as a method for decision making in the fields of business and economics. Procedures include z-tests, t-tests, ANOVAs, correlation, and simple regression.
Cross listed with ECON 2100.
Prerequisite(s): Demonstrated proficiency in high school algebra or permission of the instructor.
(Normally offered each semester.)

An introduction to statistics concepts with an emphasis on applications. Topics include descriptive statistics, discrete and continuous probability distributions, the central limit theorem, confidence intervals, hypothesis testing, and linear regression.
(Normally offered each fall semester.)

An introduction to basic probability and statistics concepts with an emphasis on applications. Topics include descriptive statistics, probability, Bayes' Theorem, discrete and continuous probability distributions, joint probability distributions, estimation and hypothesis testing.
Prerequisite(s): Grade of "C" or better in MATH 1610 Calculus II.
(Normally offered fall of even-numbered years.)

This course introduces students to the statistical techniques commonly used to answer questions concerning the political world. This course teaches students how to construct and describe data, examine relationships between variables, and build and evaluate statistical models. In addition, students will learn to apply these statistical techniques to draw conclusions about the political world and make policy decisions. Throughout the semester, students will be introduced to the datasets, software, and techniques most commonly employed in the quantitative analysis of politics and policy.

(Normally offered each spring semester.)

An introduction to descriptive and inferential statistics as decision-making guides in psychology and related fields. Topics include organization, analysis, presentation, and interpretation of data with emphasis on the hypothesis testing model of inference. Specific procedures include z-tests, t-tests, analysis of variance, and correlation. A laboratory section is required for computational experience.
Prerequisite(s): PSYCH 1010/PSYCH 1010FYW Introduction to Psychological Science and sophomore standing.
Recommended: College level mathematics course.
(Normally offered each semester.)

In this course students are introduced to descriptive and inferential statistics and their applications to sociological research. Statistical procedures include central tendency measures, variability, t-test, one-way ANOVA, correlation, regression, and chi square. The course also includes specific training in using SPSS for analysis.
Prerequisite(s): SOC 1110 Introduction to Sociology.
(Normally offered each spring semester.)