Data Analytics (B.A., B.S.)

This course will introduce students to the power of effective project management through two primary frameworks: waterfall and agile. Students will also learn vital project-management concepts applicable to a wider range of industries and occupations. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

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.)

Any successful project starts with a plan. This course provides students with a deep understanding of project planning. Projects are a series of tradeoffs between scope, cost, and time, so students will need to learn how to balance them to create a realistic and achievable plan. Students will also learn to leverage resources and manage risk, quality, and stakeholder expectations to ensure project success. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Prerequisite(s): Grade of "C-" or better in BUSAD 1650 Introduction to Project Management.

This course will provide an introduction and overview to the managing of information systems (MIS) in today's organizations. The focus is on the use of strategic information systems related to decision making processes and activities in the functional areas of organizations such as operations, management, and marketing.
Prerequisite(s): BUSAD 2500 Principles of Management or permission of the instructor.

This course will review modern quantitative methods used in decision making. The intent is to expose the student to various modeling techniques and to apply these techniques using Excel. Topics include productivity and capacity analysis, forecasting, regression analysis, linear programming, PERT/CPM, and statistical process control.

Prerequisite(s): Grade of "C-" or better in BUSAD 2100 Business and Economic Statistics, ECON 2100 Business and Economic Statistics, MATH 1300 Statistics, or MATH 3300 Mathematical Statistics I, and one of MATH 1100 College Algebra or MATH 1600 Calculus I, or department chair permission.

(Normally offered each semester.)

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 project-based continuation of the techniques developed in CMPSC 1100 Python Programming I. Topics include object-oriented programming, algorithm design and analysis, data structures, and general problem-solving techniques (such as recursion) while following industry-standard software development principles.

Prerequisite(s): Grade of "C" or better in CMPSC 1100 Python Programming I or permission of instructor.

This course, built in collaboration with Google, will teach you how to understand and use data structures. Data structures are used by almost every program and application to store, access and modify the vast quantities of data that are needed by modern software. By the end of this course you'll learn what data structures are and learn how to use them in the applications you build. This online class has optional live sessions. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions. Prerequisite(s): CMPSC 2100 Python Programming II.

This course explores algorithms from a coding-focused perspective, using Python. Students will learn about the issues that arise in the design of algorithms for solving computational problems and will explore a number of standard algorithm design paradigms and their applicability. Students will also become familiar with concepts of runtime, recursion, implementation and evaluation. This course features a heavy emphasis on practical application of algorithms to common development and engineering challenges. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Prerequisite(s): CMPSC 3000 Data Structures and MATH 1600 Calculus I.

A study of theories and practices of persuasion within a variety of communication contexts. Students will be expected to apply these concepts to out-of-class persuasive situations.
Prerequisite(s): Junior standing.
(Normally offered each semester.)

This course is designed to help students develop theoretical and practical understandings of dialogic communication. Students will develop the skills necessary to effectively participate in and facilitate transformational dialogue. In addition to developing a comprehensive understanding of current dialogic research, students will have several opportunities to practice their facilitating skills by helping NWU and Lincoln community groups engage impasse through dialogue.
Prerequisite(s): Sophomore standing and permission of the instructor.

Students will create and deliver presentations for a variety of communication contexts and audiences. Skills in interviewing and group problem solving will be also be developed.
Prerequisite(s): Junior standing and instructor permission.
(Normally offered each semester.)

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.

In today's world, no one is safe from cyber-attacks, but everyone can be prepared. This course will teach you how malicious actors use social skills and technology to facilitate cyber attacks and provide you with the tools and information you need to defend against those attacks. Whether you pursue one of the many available jobs in cybersecurity or just want to secure your own privacy, you'll learn how to make the Internet safer.This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Supply chain management is the process by which organizations get us the products we consume, and companies need talented employees to help optimize their supply chain. This course will teach you how to use forecasting techniques to match supply and demand, and how to develop logistics networks that help minimize costs and deliver top customer service. This online class has optional live sessions. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

In today's modern economy, something as simple as a razor might be manufactured in multiple countries with each part coming from a different supplier. This course will teach you how businesses manage this increasing complexity behind the scenes through efficient sourcing of suppliers and operations. You will have the opportunity to apply this knowledge by conducting a real-world case study of a product of your choosing. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Prerequisite(s): DATA 2200 Forecasting And Logistics.

Cybercrime is one of the biggest threats companies face on a daily basis, and they are constantly looking for new hires to help protect them. In this course, you will get a firsthand look at the methods used to commit cybercrimes. You will also learn how governments detect, investigate, and stop these crimes, and become familiar with the laws and policies in place to deter cybercriminals. This online class has optional live sessions. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Prerequisite(s): DATA 1700 Introduction to Cybersecurity.

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.

This course is based heavily on UC Berkeley's Data 100 class. Data Analytics combines data, computation and inferential thinking to solve challenging problems and understand their intricacies. This class explores key principles and techniques of data science, and teaches students how to create informative data visualizations. It also explores particular concepts of Linear Algebra which are central to Data Science. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Prerequisite(s): CMPSC 2100 Python Programming II and DATA 1400 Foundations of Data Analytics II.

This course builds on DATA 3200 to provide students with a more robust understanding of the tools of a Data Scientist. Data Analytics combines data, computation and inferential thinking to solve challenging problems to thereby better understand the world. This class explores key principles and techniques of data science, including quantitative critical thinking and algorithms for machine learning methods. It will also introduce students to the ways in which data analytics is deployed in healthcare, marketing, political science, criminal justice, and other fields. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.

Prerequisite(s): DATA 3200 Principles and Techniques of Data Analytics I and MATH 1600 Calculus I.

On-the-job training in data analytics in situations that satisfy the mutual interests of the student, the supervisor, and the instructor. The student will arrange for the position in accordance with the guidelines established by the department. Pass/Fail only.

Prerequisite(s): Permission of the instructor and approval of the department chair.

A student-driven collaborative project synthesizing skills developed in the data analytics major.

Prerequisite(s): At least Junior standing and grades of "C" or better in CMPSC 2100 Python Programming II and DATA 3100 Data Visualization With R.

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.)