Data Science Minor Requirements

2021 - 2022 Catalog

We have the following degrees:

Data Science - Business Analytics minor

A minor in data science-business analytics requires completion of 22 credits, as follows. In meeting the requirements of this interdisciplinary minor, a student may not use more than nine credits (including capstone) that are also used to meet the requirements of other majors or minors. Business Administration majors may not minor in Data Science- Business Analytics but may minor in Data Science.

  1. Business Foundations: ACCT 100
  2. Data Science Foundations: At least three credits chosen from among the following courses: BUS 310, 314
  3. Statistics: At least three credits chosen from among the following courses: BIOL 201; CBSC 250; DCI 202; ECON 202; INTR 202; MATH 118, 310; SOAN 218, SOAN 222
  4. Computing/Programming: At least three credits chosen from among the following: BUS 315, 316; CSCI 111
  5. Electives: At least six additional credits chosen from among the following: BUS 314, 315, 316, 359; CSCI 111, 112, 315; ECON 203; MATH 222, 309, SOAN 222
  6. Capstone: BUS 317
  7. Portfolio: DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor in addition to the capstone project, and also including at least two reflections on data science competencies, one from required course and one from BUS 317.

Additional prerequisites may be required depending on course choices above.

  1. Business Foundations:
    • ACCT 100 - Introduction to Accounting
      Credits3
      FacultyStaff

      Open only to students who have not taken ACCT 201 and/or ACCT 202. An introduction to accounting for both internal and external purposes. Students cover the fundamental principles of financial accounting (external) and an introduction to how companies process financial information in order to disclose it to the public. The course also investigates how managers prepare information for internal purposes (managerial accounting). Financial accounting is guided by external requirements, while managerial accounting generally is not.


  2. Data Science Foundations:
  3. At least three credits chosen from among the following courses:

    • BUS 310 - Management Information Systems
      Credits3
      PrerequisiteINTR 201 and at least junior standing. Preference to BSADM majors during first round of registration. See go.wlu.edu/MOS-testing and contact the department head for Microsoft testing details
      FacultyLarson

      The objective is to build an understanding of the value and uses of information systems for business operations, management decision making, and strategic advantage. Topics include basic systems concepts and major roles of information systems; computer, telecommunications, and database management concepts; and management issues in the implementation of information systems, including international, security, and ethical considerations.


    • BUS 314 - Introduction to Data Science for Business
      Credits3
      FacultyLarson

      Preference given to BSADM majors & DS / DSBA minors during the first round of registration. Not open to students with credit for BUS 316. Students looking for a more advanced business course in data analytics should register for BUS 316. No prerequisite or prior coursework assumed in statistics or programming. This course covers organizational concerns related to data science such as artificial intelligence, machine learning, predictive algorithms, Big Data, cloud computing, security and privacy, and the digitization of products and processes. Through readings, students develop a strong conceptual understanding of concepts prior to developing technical proficiency in some of them. Students learn SQL and the Exploratory UI (user interface) for R to quickly access capabilities including data wrangling and machine learning without programming. Assignments focus on how organizations can improve decision making and create new business opportunities using Data Science.


  4. Statistics:
  5. At least three credits chosen from among the following courses:

    • BIOL 201 - Statistics for Biology and Medicine
      Credits3
      PrerequisiteBIOL 111 and 113
      FacultyMarsh

      This course examines the principles of statistics and experimental design for biological and medical research. The focus is on the practical and conceptual aspects of statistics, rather than mathematical derivations. Students completing this class will be able to read and understand research papers, to design realistic experiments, and to carry out their own statistical analyses using computer packages.


    • CBSC 250 - Statistics and Research Design
      Credits4
      PrerequisiteOne course in CBSC/PSYC and at least sophomore standing
      CorequisitePSYC 250L
      FacultyBrindle, Johnson, Murdock, Whiting, Woodzicka

      Students learn about the design and analysis of psychological research, with particular emphasis on experimentation. Students learn statistical inference appropriate for hypothesis testing, and they use standard statistical packages to analyze data. Laboratory course.


    • DCI 202 - Introduction to Data Science
      FDRFM
      Credits3
      FacultyKhalifa

      Not open to students with credit for ECON 202 or INTR 202. Foundation in introductory statistics and data science which enables students to understand and participate in introductory data-science projects. The course starts with an introduction to the concepts of data science and its use in today's society. Students are exposed to a survey of the basics of statistics and probability theory; tackle the basics of regression analysis, learn a multitude of data manipulation and visualization techniques; and are introduced to programming in R.


    • ECON 202 - Statistics for Economics
      Credits3
      PrerequisiteMATH 101
      FacultyStaff

      Not open to students with credit for DCI 202 or INTR 202. Fundamentals of probability, statistics, estimation, and hypothesis testing and ending with an introduction to regression analysis. The topics are critical for success in upper-level economics electives and are important for careers that rely on empirical research in the social sciences. Students engage in a dialogue between theory and application and learn to think formally about data, uncertainty, and random processes, while learning hands-on methods to organize and analyze real data using modern statistical software.


    • INTR 202 - Applied Statistics
      Credits3

      Not open to students with credit for DCI 202 or ECON 202. An examination of the principal applications of statistics in accounting, business, economics, and politics. Topics include descriptive statistics, probability, estimation, hypothesis testing, and regression analysis.

       


    • MATH 118 - Introduction to Statistics
      FDRFM
      Credits3

      Elementary probability and counting. Mean and variance of discrete and continuous random variables. Central Limit Theorem. Confidence intervals and hypothesis tests concerning parameters of one or two normal populations.


    • MATH 310 - Mathematical Statistics
      Credits3
      PrerequisiteMATH 309

      Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.


    • SOAN 218 - Basic Statistics in the Social Sciences
      Credits3
      FacultyStaff

      Introductory statistics course designed to help students become good consumers of statistics, but especially geared for students interested in sociology, archeology, and anthropology. Topics include descriptive and inferential statistics, sampling, and regression analysis. Students also get practical experience with cleaning and analyzing real world secondary data.


    • SOAN 222 - Data Science Tools for Social Policy
      FDRSC
      Credits3
      PrerequisiteOne of the following: SOAN 218, SOAN 219, INTR 202, ECON 202, DCI 202, BIOL 201, CBSC 250, or MATH 310; or instructor consent
      FacultyEastwood

      Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice.  All work will be done in R.  No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.


  6. Computing/Programming
  7. At least three credits chosen from among the following:

    • BUS 315 - Database Management for Business
      Credits3
      PrerequisiteAt least junior standing. Preference to BSADM majors and DS minors during first round of registration
      FacultyLarson

      Not open to students who have received credit for CSCI 317. An introduction to the theories, concepts, features, and capabilities of database management systems in a business environment. This course provides a greater understanding of how to design, develop and access database-driven business applications and emphasizes the use of database-management systems in real-world business settings and how this technology can be applied effectively to solve business problems. In this project-oriented course, students acquire the skills to document, design, create, test, and access a fully functional Oracle business database application. No prior programming or application development experience is assumed.


    • BUS 316 - Business Analytics
      Credits3
      PrerequisiteINTR 202, ECON 202, DCI 202, CBSC 250 or SOAN 218; and at least junior standing. Preference to BSADM majors and DS / DSBA minors during the first round of registration
      FacultyBallenger

      Not open to students with credit for BUS 306: Data Management and Analysis for Business from Fall 2017 or Fall 2018. An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required.


    • CSCI 111 - Fundamentals of Programming I
      FDRFM
      Credits4
      FacultyStaff

      A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.


  8. Electives:
  9. At least six additional credits chosen from among the following:

    • BUS 314 - Introduction to Data Science for Business
      Credits3
      FacultyLarson

      Preference given to BSADM majors & DS / DSBA minors during the first round of registration. Not open to students with credit for BUS 316. Students looking for a more advanced business course in data analytics should register for BUS 316. No prerequisite or prior coursework assumed in statistics or programming. This course covers organizational concerns related to data science such as artificial intelligence, machine learning, predictive algorithms, Big Data, cloud computing, security and privacy, and the digitization of products and processes. Through readings, students develop a strong conceptual understanding of concepts prior to developing technical proficiency in some of them. Students learn SQL and the Exploratory UI (user interface) for R to quickly access capabilities including data wrangling and machine learning without programming. Assignments focus on how organizations can improve decision making and create new business opportunities using Data Science.


    • BUS 315 - Database Management for Business
      Credits3
      PrerequisiteAt least junior standing. Preference to BSADM majors and DS minors during first round of registration
      FacultyLarson

      Not open to students who have received credit for CSCI 317. An introduction to the theories, concepts, features, and capabilities of database management systems in a business environment. This course provides a greater understanding of how to design, develop and access database-driven business applications and emphasizes the use of database-management systems in real-world business settings and how this technology can be applied effectively to solve business problems. In this project-oriented course, students acquire the skills to document, design, create, test, and access a fully functional Oracle business database application. No prior programming or application development experience is assumed.


    • BUS 316 - Business Analytics
      Credits3
      PrerequisiteINTR 202, ECON 202, DCI 202, CBSC 250 or SOAN 218; and at least junior standing. Preference to BSADM majors and DS / DSBA minors during the first round of registration
      FacultyBallenger

      Not open to students with credit for BUS 306: Data Management and Analysis for Business from Fall 2017 or Fall 2018. An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required.


    • CSCI 111 - Fundamentals of Programming I
      FDRFM
      Credits4
      FacultyStaff

      A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.


    • CSCI 112 - Fundamentals of Programming II
      FDRSC
      Credits4
      PrerequisiteCSCI 111
      FacultyStaff

      A continuation of CSCI 111. Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object-oriented design and programming with Python. Laboratory course.


    • CSCI 315 - Artificial Intelligence
      Credits3
      PrerequisiteCSCI 209
      FacultyLevy

      Basic concepts of heuristic search, game playing, natural language processing, and intelligent systems, with a focus on writing programs in these areas. Course combines a discussion of philosophical issues with hands-on problem solving.


    • ECON 203 - Econometrics
      Credits3
      PrerequisiteECON 202 or INTR 202 or consent of instructor or department head
      FacultyAnderson, Blunch

      Explorations of regression models that relate a response variable to one or more predictor variables. The course begins with a review of the simple bivariate model used in INTR 202, and moves on to multivariate models. Underlying model assumptions and consequences are discussed. Advanced topics include non-linear regression and forecasting. Examples in each class are drawn from a number of disciplines. The course emphasizes the use of data and student-directed research.


    • MATH 222 - Linear Algebra
      FDRSC
      Credits3
      PrerequisiteThe equivalent of MATH 102 with a C grade or better or MATH 201 or 221

      Linear algebra is the backbone of much of mathematics. Students in this course learn to identify and explain the basic principles, terminology, and theories used in linear algebra, and apply quantitative and/or qualitative reasoning skills to solve problems posed in linear algebra, primarily through applications of to both mathematics and the sciences, and also by writing proofs In mathematics.


    • MATH 309 - Probability
      Credits3
      PrerequisiteThe equivalent of MATH 221 with C grade or better

      Probability, probability density and distribution functions, mathematical expectation, discrete and continuous random variables, and moment generating functions.


    • SOAN 222 - Data Science Tools for Social Policy
      FDRSC
      Credits3
      PrerequisiteOne of the following: SOAN 218, SOAN 219, INTR 202, ECON 202, DCI 202, BIOL 201, CBSC 250, or MATH 310; or instructor consent
      FacultyEastwood

      Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice.  All work will be done in R.  No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.


  10. Capstone:
    • BUS 317 - Data Mining for Business Analytics
      Credits3
      PrerequisiteBUS 316, BIOL 185, CBSC 240, or instructor consent; and at least junior standing. Preference to BSADM majors or DS, DSBA, ENTR minors during initial registration
      FacultyBallenger

      A prerequisite for this course is the successful completion of an R tidyverse centric data analytics course. Data mining is the science of discovering structure and making predictions in large, complex data sets. In the era of e-commerce and information economy, enormous amounts of data are generated daily from business transactions, networked sensors, social networking activities, website traffic, GPS systems, etc. Data-driven decision-making has become essential across a wide variety of functional areas in businesses such as targeted advertising, market segmentation, personalized recommendation, supplier/customer relationship management, product design, credit scoring, fraud detection and workforce management. This course serves as an introduction to Data Mining for students interested in Business Analytics. Students will learn about many commonly-used methods for predictive and descriptive analytics tasks. They will also learn to assess the methods' predictive and practical utility. 


  11. Portfolio:
  12. DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor in addition to the capstone project, and also including at least two reflections on data science competencies, one from required course and one from BUS 317.

    • DS 401 - Directed Individual Study
      Credits1
      FacultyData Science faculty

      To be taken during the fall or winter term of the senior year. Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio.


Data Science minor

A minor in data science requires completion of at least 19 credits, as follows. In meeting the requirements of this interdisciplinary minor, a student may not use more than nine credits (including capstone) that are also used to meet the requirements of other majors or minors.

  1. Data Science Foundations: At least three credits chosen from among the following: BIOL 185, 187; BUS 310, 314; CBSC 240
  2. Statistics: At least three credits chosen from among the following: BIOL 201; CBSC 250; DCI 202; ECON 202; INTR 202; MATH 118, 310; SOAN 218, 219, 222
  3. Computing/Programming: At least three credits chosen from among the following: BUS 306, 315, 316; CSCI 111; DCI 110
  4. Electives: At least six additional credits chosen from among the following: BIOL 282; BUS 306, 315, 316; CSCI 111, 112, 315; DCI 102, 110; ECON 203; GEOL 260; MATH 222, 309, 310; PHYS 265; SOAN 219, 220, 222, 264, 265, 266, 268, 276; and, when appropriate BIOL 297
  5. Capstone: at least three credits chosen from among the following: BIOL 325, 385; BUS 317; CBSC 359; SOAN 395, DS 395; 399; or another relevant course, individual study, senior thesis, or honors thesis in the student's major approved in advance by the DS core faculty.
  6. Portfolio: DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor in addition to the capstone project in which students reflect on data-science competencies

Additional prerequisites may be required depending on course choices above.

  1. Data Science Foundations
  2. At least three credits chosen from among the following:

    • BIOL 185 - Data Science: Visualizing and Exploring Big Data
      Credits3
      FacultyWhitworth

      No prior programming experience required. We live in the era of Big Data. Major discoveries in science and medicine are being made by exploring large datasets in novel ways using computational tools. The challenge in the biomedical sciences is the same as in Silicon Valley: knowing what computational tools are right for a project and where to get started when exploring large data sets. In this course, students learn to use R, a popular open-source programming language and data analysis environment, to interactively explore data. Case studies are drawn from across the sciences and medicine. Topics include data visualization, machine learning, image analysis, geospatial analysis, and statistical inference on large data sets. We also emphasize best practices in coding, data handling, and adherence to the principles of reproducible research. Fulfills the computer science requirement for biology and neuroscience majors.


    • BIOL 187 - Introduction to Data Science in Python
      Credits4
      PrerequisiteNo prior programming experience required
      FacultyToporikova

      In this era of data science, major discoveries in science and medicine are being made by exploring datasets in novel ways using computational tools. The challenge in the biomedical sciences is the same as in Silicon Valley: knowing what computational tools are right for a project and where to get started when exploring large data sets. In this course, students learn to use Python, a popular open-source programming language and Jupyter Notebook data-analysis environment, to explore data interactively. Case studies are drawn from across the sciences and medicine. Topics include data visualization, physiological modeling, image analysis, and statistical inference on large data sets. We also emphasize best practices in coding, data handling, and adherence to the principles of reproducible research.


    • BUS 310 - Management Information Systems
      Credits3
      PrerequisiteINTR 201 and at least junior standing. Preference to BSADM majors during first round of registration. See go.wlu.edu/MOS-testing and contact the department head for Microsoft testing details
      FacultyLarson

      The objective is to build an understanding of the value and uses of information systems for business operations, management decision making, and strategic advantage. Topics include basic systems concepts and major roles of information systems; computer, telecommunications, and database management concepts; and management issues in the implementation of information systems, including international, security, and ethical considerations.


    • BUS 314 - Introduction to Data Science for Business
      Credits3
      FacultyLarson

      Preference given to BSADM majors & DS / DSBA minors during the first round of registration. Not open to students with credit for BUS 316. Students looking for a more advanced business course in data analytics should register for BUS 316. No prerequisite or prior coursework assumed in statistics or programming. This course covers organizational concerns related to data science such as artificial intelligence, machine learning, predictive algorithms, Big Data, cloud computing, security and privacy, and the digitization of products and processes. Through readings, students develop a strong conceptual understanding of concepts prior to developing technical proficiency in some of them. Students learn SQL and the Exploratory UI (user interface) for R to quickly access capabilities including data wrangling and machine learning without programming. Assignments focus on how organizations can improve decision making and create new business opportunities using Data Science.


    • CBSC 240 - Data Science: Mind Analytics
      Credits3
      FacultyJohnson

      Psychological tests promise to match you with your soul mate, reveal the hidden depths of your personality and attitudes, and predict your success in college. How would you determine if these promises are being kept? Students build data-science skills while teaming on how to assess a test's reliability and validity, including tests of abilities, personality, attitudes, and more. No programming experience is required while we use R, a popular open-source programming language, to learn data management, data visualization, model-comparison metrics, and statistical inference in a reproducible and ethically responsible manner.


  3. Statistics
  4. At least three credits chosen from among the following:

    • BIOL 201 - Statistics for Biology and Medicine
      Credits3
      PrerequisiteBIOL 111 and 113
      FacultyMarsh

      This course examines the principles of statistics and experimental design for biological and medical research. The focus is on the practical and conceptual aspects of statistics, rather than mathematical derivations. Students completing this class will be able to read and understand research papers, to design realistic experiments, and to carry out their own statistical analyses using computer packages.


    • CBSC 250 - Statistics and Research Design
      Credits4
      PrerequisiteOne course in CBSC/PSYC and at least sophomore standing
      CorequisitePSYC 250L
      FacultyBrindle, Johnson, Murdock, Whiting, Woodzicka

      Students learn about the design and analysis of psychological research, with particular emphasis on experimentation. Students learn statistical inference appropriate for hypothesis testing, and they use standard statistical packages to analyze data. Laboratory course.


    • DCI 202 - Introduction to Data Science
      FDRFM
      Credits3
      FacultyKhalifa

      Not open to students with credit for ECON 202 or INTR 202. Foundation in introductory statistics and data science which enables students to understand and participate in introductory data-science projects. The course starts with an introduction to the concepts of data science and its use in today's society. Students are exposed to a survey of the basics of statistics and probability theory; tackle the basics of regression analysis, learn a multitude of data manipulation and visualization techniques; and are introduced to programming in R.


    • ECON 202 - Statistics for Economics
      Credits3
      PrerequisiteMATH 101
      FacultyStaff

      Not open to students with credit for DCI 202 or INTR 202. Fundamentals of probability, statistics, estimation, and hypothesis testing and ending with an introduction to regression analysis. The topics are critical for success in upper-level economics electives and are important for careers that rely on empirical research in the social sciences. Students engage in a dialogue between theory and application and learn to think formally about data, uncertainty, and random processes, while learning hands-on methods to organize and analyze real data using modern statistical software.


    • INTR 202 - Applied Statistics
      Credits3

      Not open to students with credit for DCI 202 or ECON 202. An examination of the principal applications of statistics in accounting, business, economics, and politics. Topics include descriptive statistics, probability, estimation, hypothesis testing, and regression analysis.

       


    • MATH 118 - Introduction to Statistics
      FDRFM
      Credits3

      Elementary probability and counting. Mean and variance of discrete and continuous random variables. Central Limit Theorem. Confidence intervals and hypothesis tests concerning parameters of one or two normal populations.


    • MATH 310 - Mathematical Statistics
      Credits3
      PrerequisiteMATH 309

      Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.


    • SOAN 218 - Basic Statistics in the Social Sciences
      Credits3
      FacultyStaff

      Introductory statistics course designed to help students become good consumers of statistics, but especially geared for students interested in sociology, archeology, and anthropology. Topics include descriptive and inferential statistics, sampling, and regression analysis. Students also get practical experience with cleaning and analyzing real world secondary data.


    • SOAN 219 - Applied Bayesian Regression for the Social Sciences
      FDRSC
      Credits3
      FacultyEastwood

      This course is an introduction to applied Bayesian regression, emphasizing applications for social scientists. We begin by introducing some philosophical and mathematical bases of Bayesian inference. We then move on to a sustained focus on applied regression, starting with bivariate regression and moving on to regression with multiple predictors, up to and including models with interactions. Along the way, students will be exposed to the use of directed acyclic graphs (DAGs) in thinking about causality with observational data. Throughout the course students will carry out numerous analyses of data, learning by doing. Examples are drawn from anthropology, sociology, political science, and related fields.


    • SOAN 222 - Data Science Tools for Social Policy
      FDRSC
      Credits3
      PrerequisiteOne of the following: SOAN 218, SOAN 219, INTR 202, ECON 202, DCI 202, BIOL 201, CBSC 250, or MATH 310; or instructor consent
      FacultyEastwood

      Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice.  All work will be done in R.  No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.


  5. Computing/Programming
  6. At least three credits chosen from among the following:

    • BUS 306 - Seminar in Management Information Systems
      Credits3 in fall, winter; 3 or 4 in spring
      PrerequisiteMay vary with topics. Preference to BSADM or JMCB majors during the first round of registration

      Topics vary by term and instructor. May be repeated for degree credit if the topics are different.

      Spring 2022, BUS 306B-01: Seminar in Management Information Systems: Multimedia Design and Development (3).  Introduction to the study and creation of multimedia content primarily used in business. Students explore the steps used to plan and create multimedia content that effectively targets and delivers business information. This is a hands-on, project-oriented course with emphasis on the design and creation of media elements such as interactive web, graphic, audio, and video content. The course focuses on WordPress development using GeneratePress and Elementor with emphasis on Cascading Style Sheets, and Adobe Photoshop, as the foundation for creating online multimedia content.  Ballenger.


    • BUS 315 - Database Management for Business
      Credits3
      PrerequisiteAt least junior standing. Preference to BSADM majors and DS minors during first round of registration
      FacultyLarson

      Not open to students who have received credit for CSCI 317. An introduction to the theories, concepts, features, and capabilities of database management systems in a business environment. This course provides a greater understanding of how to design, develop and access database-driven business applications and emphasizes the use of database-management systems in real-world business settings and how this technology can be applied effectively to solve business problems. In this project-oriented course, students acquire the skills to document, design, create, test, and access a fully functional Oracle business database application. No prior programming or application development experience is assumed.


    • BUS 316 - Business Analytics
      Credits3
      PrerequisiteINTR 202, ECON 202, DCI 202, CBSC 250 or SOAN 218; and at least junior standing. Preference to BSADM majors and DS / DSBA minors during the first round of registration
      FacultyBallenger

      Not open to students with credit for BUS 306: Data Management and Analysis for Business from Fall 2017 or Fall 2018. An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required.


    • CSCI 111 - Fundamentals of Programming I
      FDRFM
      Credits4
      FacultyStaff

      A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.


    • DCI 110 - Web Programming for Non-Programmers
      FDRSC
      Credits4
      PrerequisiteNo prior programming experience is needed, but a desire to learn and to be challenged is a must
      FacultyMickel

      Computer science and IT graduates are no longer the only people expected to have some knowledge of how to program. Humanities and social science majors can greatly increase their job prospects by understanding the fundamentals of writing computer code, not only through the ability itself but also being better able to communicate with programming professionals and comprehending the software development and design process as a whole. The most centralized and simple platform for learning is the Web. This course starts with a brief introduction to/review of HTML and CSS and then focuses on using JavaScript to write basic code and implement preexisting libraries to analyze and visualize data. Students become familiar with building a complete Web page that showcases all three languages.


  7. Electives
  8. At least six additional credits chosen from among the following:

    • BIOL 282 - Modeling and Simulations in Public Health
      FDRSL
      Credits4
      PrerequisiteMATH 101
      FacultyToporikova

      Where are infections spreading? How many people will be affected? What are some different ways to stop the spread of an epidemic? These are questions that all of us ask during an outbreak or emergency. In a process known as modeling, scientists analyze data using complex mathematical methods to provide answers to these and other questions during an emergency response.  Models provide the foresight that can help decision-makers better prepare for the future. In this course you will learn how to develop a simple mathematical models using data. You will learn basic epidemiological concepts, computational data analysis tools and relevant mathematical techniques to integrate existing data into the model and generate relevant predictions. In an open-ended project, you and several of your classmates will develop a model and recommendation about potential public health threat. No prior programming experience required - you will learn to use Python, a popular open-source programming language and Jupyter Notebook data analysis environment, to interactively explore data. Laboratory course.


    • BUS 306 - Seminar in Management Information Systems
      Credits3 in fall, winter; 3 or 4 in spring
      PrerequisiteMay vary with topics. Preference to BSADM or JMCB majors during the first round of registration

      Topics vary by term and instructor. May be repeated for degree credit if the topics are different.

      Spring 2022, BUS 306B-01: Seminar in Management Information Systems: Multimedia Design and Development (3).  Introduction to the study and creation of multimedia content primarily used in business. Students explore the steps used to plan and create multimedia content that effectively targets and delivers business information. This is a hands-on, project-oriented course with emphasis on the design and creation of media elements such as interactive web, graphic, audio, and video content. The course focuses on WordPress development using GeneratePress and Elementor with emphasis on Cascading Style Sheets, and Adobe Photoshop, as the foundation for creating online multimedia content.  Ballenger.


    • BUS 315 - Database Management for Business
      Credits3
      PrerequisiteAt least junior standing. Preference to BSADM majors and DS minors during first round of registration
      FacultyLarson

      Not open to students who have received credit for CSCI 317. An introduction to the theories, concepts, features, and capabilities of database management systems in a business environment. This course provides a greater understanding of how to design, develop and access database-driven business applications and emphasizes the use of database-management systems in real-world business settings and how this technology can be applied effectively to solve business problems. In this project-oriented course, students acquire the skills to document, design, create, test, and access a fully functional Oracle business database application. No prior programming or application development experience is assumed.


    • BUS 316 - Business Analytics
      Credits3
      PrerequisiteINTR 202, ECON 202, DCI 202, CBSC 250 or SOAN 218; and at least junior standing. Preference to BSADM majors and DS / DSBA minors during the first round of registration
      FacultyBallenger

      Not open to students with credit for BUS 306: Data Management and Analysis for Business from Fall 2017 or Fall 2018. An examination of how business analytics help management make sound business decisions. Analysis of data is becoming a vital component of business decision-making. The course consists of three modules: 1) how to communicate and present data in business reports and presentations; 2) how to extract data from relational databases using MySQL and Structured Query Language (SQL) and to prepare data for analysis; and 3) data analytics -- the process of data wrangling, data visualization, discovery, interpretation, and communication of meaningful insights and patterns in data. Students learn to use industry-standard, data analysis software, though no previous programming experience is required.


    • CSCI 111 - Fundamentals of Programming I
      FDRFM
      Credits4
      FacultyStaff

      A disciplined approach to programming with Python. Emphasis is on problem-solving methods, algorithm development, and object-oriented concepts. Lectures and formal laboratories.


    • CSCI 112 - Fundamentals of Programming II
      FDRSC
      Credits4
      PrerequisiteCSCI 111
      FacultyStaff

      A continuation of CSCI 111. Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object-oriented design and programming with Python. Laboratory course.


    • CSCI 315 - Artificial Intelligence
      Credits3
      PrerequisiteCSCI 209
      FacultyLevy

      Basic concepts of heuristic search, game playing, natural language processing, and intelligent systems, with a focus on writing programs in these areas. Course combines a discussion of philosophical issues with hands-on problem solving.


    • DCI 102 - Data in the Humanities
      FDRSC
      Credits3
      FacultyBrooks

      This course introduces students to the creation and visualization of data in humanities research. The course is predicated on the fact that the digital turn of the last several decades has drastically changed the nature of knowledge production and distribution. The community and set of practices that is digital humanities (DH) encourages fluency in media beyond the printed word such as text mining, digital curation, data visualization, and spatial analysis. Readings and discussions of theory complement hands-on application of digital methods and computational thinking. While the objects of our study come primarily from the humanities, the methods of analysis are widely applicable to the social and natural sciences. Three unit-long collaborative projects explore the creation, structure, and visualization of humanities data. This course meets in two-hour blocks to accommodate a lab component.


    • DCI 110 - Web Programming for Non-Programmers
      FDRSC
      Credits4
      PrerequisiteNo prior programming experience is needed, but a desire to learn and to be challenged is a must
      FacultyMickel

      Computer science and IT graduates are no longer the only people expected to have some knowledge of how to program. Humanities and social science majors can greatly increase their job prospects by understanding the fundamentals of writing computer code, not only through the ability itself but also being better able to communicate with programming professionals and comprehending the software development and design process as a whole. The most centralized and simple platform for learning is the Web. This course starts with a brief introduction to/review of HTML and CSS and then focuses on using JavaScript to write basic code and implement preexisting libraries to analyze and visualize data. Students become familiar with building a complete Web page that showcases all three languages.


    • ECON 203 - Econometrics
      Credits3
      PrerequisiteECON 202 or INTR 202 or consent of instructor or department head
      FacultyAnderson, Blunch

      Explorations of regression models that relate a response variable to one or more predictor variables. The course begins with a review of the simple bivariate model used in INTR 202, and moves on to multivariate models. Underlying model assumptions and consequences are discussed. Advanced topics include non-linear regression and forecasting. Examples in each class are drawn from a number of disciplines. The course emphasizes the use of data and student-directed research.


    • GEOL 260 - GIS and Remote Sensing
      Credits4
      PrerequisiteGEOL 100, GEOL 101, or GEOL 102. For GEOL or ENV majors only, or by instructor consent
      FacultyHarbor

      A laboratory course introducing the use of a Geographic Information System (GIS) and remote sensing in geological/environmental analyses and decision making. Students use state-of-the-art software with a wide variety of spatial geologic, environmental, economic and topographic data derived from satellites; remote databases and published maps to evaluate geologic conditions; local landscape processes; environmental conditions; and hypothetical land-use cases.


    • MATH 222 - Linear Algebra
      FDRSC
      Credits3
      PrerequisiteThe equivalent of MATH 102 with a C grade or better or MATH 201 or 221

      Linear algebra is the backbone of much of mathematics. Students in this course learn to identify and explain the basic principles, terminology, and theories used in linear algebra, and apply quantitative and/or qualitative reasoning skills to solve problems posed in linear algebra, primarily through applications of to both mathematics and the sciences, and also by writing proofs In mathematics.


    • MATH 309 - Probability
      Credits3
      PrerequisiteThe equivalent of MATH 221 with C grade or better

      Probability, probability density and distribution functions, mathematical expectation, discrete and continuous random variables, and moment generating functions.


    • MATH 310 - Mathematical Statistics
      Credits3
      PrerequisiteMATH 309

      Sampling distributions, point and interval estimation, testing hypotheses, regression and correlation, and analysis of variance.


    • PHYS 265 - Modeling and Simulation of Physical Systems
      Credits4
      PrerequisitePHYS 112 and MATH 102 or instructor consent
      FacultyI. Mazilu

      An introduction to the innovative field of modeling and analysis of complex physical systems from such diverse fields as physics, chemistry, ecology, epidemiology, and a wide range of interdisciplinary, emerging fields such as econophysics and sociophysics. Topics vary according to faculty expertise and student interest. The goal is to seek the underlying physics laws that govern such seemingly diverse systems and to provide contemporary mathematical and computational tools for studying and simulating their dynamics. Includes traditional lectures as well as workshops and computational labs, group presentations, and seminars given by invited speakers


    • SOAN 219 - Applied Bayesian Regression for the Social Sciences
      FDRSC
      Credits3
      FacultyEastwood

      This course is an introduction to applied Bayesian regression, emphasizing applications for social scientists. We begin by introducing some philosophical and mathematical bases of Bayesian inference. We then move on to a sustained focus on applied regression, starting with bivariate regression and moving on to regression with multiple predictors, up to and including models with interactions. Along the way, students will be exposed to the use of directed acyclic graphs (DAGs) in thinking about causality with observational data. Throughout the course students will carry out numerous analyses of data, learning by doing. Examples are drawn from anthropology, sociology, political science, and related fields.


    • SOAN 220 - A World of Data: Baseball and Statistics
      Credits4
      PrerequisiteOne course selected from CBSC/PSYC 250, ECON 203, INTR 202, SOAN 218, or SOAN 219 or instructor consent
      FacultyEastwood, Kosky

      An introduction to the world of data and data analysis, emphasizing Bayesian methods. Taking the case of contemporary sports, with a particular focus on baseball, it teaches students how to build models of player performance while also asking important questions about the limitations of such approaches to human activities. What is gained and lost in the world made by measuring human actions in reliable ways? How is our experience in the world--in this case as athletes playing and spectators living sports--affected when we see it in terms of statistics and predictive models? What interests and what concerns make up our lives when we engage the world in this way? What interests and concerns may be obscured? The course offers a rare opportunity to acquire some expertise in producing data-driven knowledge and decisions while also reflecting on what it is like to be a non-expert living in the world shaped by such expertise.


    • SOAN 222 - Data Science Tools for Social Policy
      FDRSC
      Credits3
      PrerequisiteOne of the following: SOAN 218, SOAN 219, INTR 202, ECON 202, DCI 202, BIOL 201, CBSC 250, or MATH 310; or instructor consent
      FacultyEastwood

      Students learn about how we think about and estimate causal effects, and practice important contemporary techniques with real data, culminating in reports analyzing the effects of a policy intervention of their choice.  All work will be done in R.  No previous experience with R is required, but some basic previous exposure to linear regression will be helpful.


    • SOAN 264 - States, Data, and Population Policies in the Americas
      FDRSS4
      Credits3
      PrerequisiteOne course selected from SOAN 101, SOAN 102, POV 101, or LACS 101
      FacultyEastwood

      While concentrating on the societies of the Americas, students focus on two of the main domains within which states seek to understand and influence populations: policies governing the collection of information about their residents, such as the census, and those governing migration. The course is made up of two interwoven parts, a traditional seminar portion that examines such policies from the perspective of historical sociology and a data-lab portion in which we perform exploratory visualization of historical and contemporary census and migration data from the region, using the "tidyverse" suite of R packages. We reflect critically on our own work, making use of perspectives afforded by the historical sociology portion of the course.


    • SOAN 265 - Exploring Social Networks
      FDRSS4
      Credits3
      FacultyEastwood

      This course is an introduction to network analysis. Students learn some of the major network analysis literature in sociology and related fields and develop their skills as network analysts in laboratory sessions. Social science, humanities, business, and public health applications are emphasized.


    • SOAN 266 - Neighborhoods, Culture, and Poverty
      FDRSS3
      Credits3
      FacultyEastwood

      This course examines social-scientific research on the determinants of poverty, crime, and ill health by focusing on neighborhoods as the sites where many of the mechanisms impacting these outcomes operate. In addition to engaging with key readings and participating in seminar discussions, students conduct their own exploratory analyses of neighborhood level processes using a variety of spatial data analysis tools in R.


    • SOAN 268 - Migration, Identity, and Conflict
      FDRSS4
      Credits3
      PrerequisiteSOAN 102, POV 101, or POL 105
      FacultyEastwood

      This course focuses on the complex relationship between migration, political institutions, group identities, and inter-group conflict. The course is a hybrid of a seminar and research lab in which students (a) read some of the key social-scientific literature on these subjects, and (b) conduct team-based research making use of existing survey data about the integration of migrant populations into various polities.


    • SOAN 276 - Art & Science of Survey Research
      Credits3
      PrerequisiteSOAN 102 or instructor consent
      FacultyJasiewicz

      This course is designed as a group research project in questionnaire construction and survey data analysis. Students prepare a list of hypotheses, select indicators, construct a questionnaire, collect and analyze data, and write research reports. When appropriate, the course may include service-learning components (community-based research projects).


    • and, when appropriate,

    • BIOL 297 - Topics in Biology
      Credits3 or 4 in fall or winter; 4 in spring
      Prerequisitevary with topic

      Topics vary with instructor and term. Repeatable for credit if topics are different.

       


  9. Capstone:
  10. at least three credits chosen from among the following list of approved courses; or another relevant course, individual study, senior thesis, or honors thesis in the student's major approved in advance by the DS core faculty.

    • BIOL 325 - Ecological Modeling and Conservation Strategies
      Credits4
      PrerequisiteMATH 101 or higher and BIOL 111 and 113, or instructor consent
      FacultyHumston

      This course is an intensive introduction to foundational methods in ecological modeling and their application, with emphasis on the dynamics of exploited or threatened populations and developing strategies for effective conservation. Topics include managing harvested populations, population viability analysis, individual based models, and simulation modeling for systems analyses. Laboratory course.


    • BIOL 385 - Molecular Mechanics of Life
      Credits4
      PrerequisiteBIOL 220
      FacultyWhitworth

      How do we study complex networks of interactions between molecules in cells? How do we discover what roles different molecular machines play in the development and behavior of cells and animals? How can we identify the ways in which medical illness is caused by the misregulation of biological complexes because of a pathogenic infection or genetic disease? Our approach to answering these questions reflects the same interdisciplinary strategy being used at the forefront of current biomedical research. We consider the ways in which traditional approaches in biochemistry, genetics and cell biology can be merged with new systems-level approaches such as genomics and proteomics, to allow us to probe the underlying molecular mechanics of life. In the classroom, we examine different molecular networks, while readings include selections from the primary literature. The laboratory is based on an investigation of a novel research question, designed and addressed by student participants. Laboratory course


    • BUS 317 - Data Mining for Business Analytics
      Credits3
      PrerequisiteBUS 316, BIOL 185, CBSC 240, or instructor consent; and at least junior standing. Preference to BSADM majors or DS, DSBA, ENTR minors during initial registration
      FacultyBallenger

      A prerequisite for this course is the successful completion of an R tidyverse centric data analytics course. Data mining is the science of discovering structure and making predictions in large, complex data sets. In the era of e-commerce and information economy, enormous amounts of data are generated daily from business transactions, networked sensors, social networking activities, website traffic, GPS systems, etc. Data-driven decision-making has become essential across a wide variety of functional areas in businesses such as targeted advertising, market segmentation, personalized recommendation, supplier/customer relationship management, product design, credit scoring, fraud detection and workforce management. This course serves as an introduction to Data Mining for students interested in Business Analytics. Students will learn about many commonly-used methods for predictive and descriptive analytics tasks. They will also learn to assess the methods' predictive and practical utility. 


    • CBSC 359 - Advanced Methods in Cognition and Emotion Research
      Credits3
      PrerequisiteInstructor consent
      FacultyJohnson

      Directed research on a variety of topics in cognition and emotion. May be repeated for degree credit.


    • SOAN 395 - Senior Seminar in Social Analysis
      Credits3
      PrerequisiteSOAN 102 as well as completion of Group 3 Methods Requirements for the SOAN major
      FacultyJasiewicz

      This course is designed as a capstone experience for majors with the sociology emphasis. Students, utilizing their knowledge of sociological theory and research methods, design and execute independent research projects, typically involving secondary analysis of survey data. Working on a subject of their choice, students learn how to present research questions and arguments, formulate research hypotheses, test hypotheses through univariate, bivariate, and multivariate analyses (utilizing appropriate statistical packages such as SPSS), and write research reports.


    • DS 395 - Special Topics in Data Science
      Credits3
      PrerequisiteInstructor consent
      FacultyData Science faculty

      Exploration of a topic in data science, where students craft testable research questions and retrieve an appropriate data set to answer those research questions. Includes training in some aspect of data science and incorporates extensive independent student work, with individual projects being the key product of that work, synthesizing what you have learned in the data science minor and demonstrating mastery of core data-science skills. Topics may include causal inference for data science with directed acyclic graphs (DAGs); analysis of spatial data; or multilevel models. Data Science faculty. Offered periodically depending on faculty availability and expertise and student interest.

      Winter 2022, DS 395A-01: Special Topics in Data Science: Statistics & Medicine, How a Vaccine is Born (3). Covid-19 has changed life as we know it for everyone on the planet. While we are still in the clutches of this deadly, global pandemic, we recently received a ray of medical hope in the form of several vaccines. How are vaccines developed? What are the statistical methods used to analyze their effectiveness? What can history tell us about the ethics, efficacy and quality of vaccines and their distribution? How do the current available Covid-19 vaccines compare to one another? This course will attempt to answer these questions using data science skills. Prince Nelson.


    • DS 399 - Data Science Capstone
      Credits3
      PrerequisiteInstructor consent
      FacultyData Science faculty

      In this course, students have the opportunity to identify a topic of interest, craft testable research questions, and retrieve an appropriate data set to answer those research questions. Students' primary goal is to synthesize what you have learned in the data science minor and demonstrate mastery of core data-science skills.


  11. Portfolio:
  12. DS 401, completed during the fall or winter term of the senior year, including at least three projects or assignments from courses in the minor in addition to the capstone project in which students reflect on data-science competencies
    Additional prerequisites may be required depending on course choices above.

    • DS 401 - Directed Individual Study
      Credits1
      FacultyData Science faculty

      To be taken during the fall or winter term of the senior year. Under the guidance of faculty in the data-science program, the student produces a digital portfolio (required for the minor) of coursework, research, and non-curricular activities in data science that demonstrates the student's mastery of data science. Students receive instruction in best practices and expected requirements but have considerable freedom in designing their portfolios. It is expected that the student works independently each week on producing the portfolio.