Minor in Data Science

This course is an introductory class that aims to show the students the main problems and methods of data science with a minimal mathematical background. The course covers basic data science concepts and algorithms with an emphasis in real-life applications and gaining a broad understanding of the area.

Basic concepts of probability and statistics, and applications to the sciences, social sciences, and management. Probability, conditional probability, Bayes Formula, Bernoulli trials, expected value, frequency distributions, and measures of central tendency. Credit will not be allowed for MAT 102 if student has previously passed MAT 130; 102 will not be counted toward a major in the department.

This course emphasizes the meaning and application of the concepts of functions. It covers polynomial, rational, exponential, logarithmic and trigonometric functions and their graphs, trigonometric identities. Passing both MAT 125 and 126 is equivalent to passing MAT 130.

This course introduces fundamental concepts of computer programming. Students learn program logic, flow charting, and problem solving through analysis, development, basic debugging and testing procedures. Topics include variables, expressions, data types, functions, decisions, loops, and arrays. Students will use the knowledge and skills gained throughout this course to develop a variety of simple programs.

This course introduces students to the use of computer software and computer programming for data exploration, modeling of natural systems (from biology, chemistry, or physics), information visualization, and instrument/robot control. This is done through independent research where students work in groups to design and pursue computational projects and then critically analyze, interpret and present their findings.

Ever wondered how companies like Amazon know more about you? Ever wondered how weather data is represented in the news? Using interdisciplinary concepts, we will learn how to tackle big data. Complex data sets are being generated continuously. Many questions arise as to what these data are telling us. Are we missing something? How do we look for signals in these large datasets? Using computer programs like Excel and R programming we will learn how to manage, sort and represent these data. Students will be encouraged to identify a data set related to a real world problem and use the tools learned in class to tell their stories.

Modern day biology has generated massive amounts of data but very few experts to analyze this data. A course in Genomics and Bioinformatics will teach students how to use computer algorithms to analyze the data. Students learn applications of genomics to biomedical and biological research by performing computational exercises using databases. Topics include genome sequencing gene prediction, genetic variation, sequence database searching, multiple sequence alignment, evolutionary tree construction, protein structure prediction, proteomic analysis, interaction networks and use of genome browsers among other topics.

The course introduces students to ArcGIS Online, an online Geographic Information System (GIS) application from Esri. With GIS, the student can explore, visualize, and analyze data; create 2D maps and 3D scenes with several layers of data to visualize multiple data sets at once; and share work to an online portal. GIS analytics tools are used in many disciplines and fields of practice including public health, history, sociology, political science, business, biology, international development, and information technology. In the end of the course, students will have the opportunity to take additional training on GIS applications in their specific field of interest.

This is an introductory course in probability and statistics for science and information technology students. It covers basic concepts of probability and statistics, frequency distributions, graphical methods, measures of central tendency and variability, counting principles, Bayes' theorem, discrete and normal probability distributions, linear regression models, correlation, central limit theorem, sampling variability, confidence intervals, and hypothesis testing. Applications to different fields are included throughout.

This course will introduce the concepts, theories, and applications of biostatistics to biological, medical, and public health research. It will cover descriptive statistics, concepts of probabilities and distributions, graphical methods, comparisons of two variables, central limit theorem, sampling variability, confidence intervals, and hypothesis testing.

This course covers an introduction to statistical procedures for psychological research. Topics include distributions and graphs, measures of central tendency and variation, z-scores, probability, hypothesis testing, t-tests, Anova, correlation and regression, and Chi square. Students are introduced to the use of SPSS (or a similar program) for analysis and interpretation of data.

An introduction to descriptive statistics and methods of organizing, presenting, and interpreting data. Covers measures of central tendency, measures of association for two variables, and some multivariate analyses. Includes computer analysis of real data.