Data Analytics - Associate Degree

Students gain skills that lead to success in college, employment, and life. Students should take this course in their first semester. (Pre-requisite: None.)

Learners develop knowledge/skills in planning, organizing, writing, editing. Students will also analyze audience/purpose, use elements of research, format documents using standard guidelines, and develop critical reading skills. (Prerequisite: High school GPA greater/equal to 2.6; OR ACT Reading score greater/equal to 16 AND English greater/equal to 18; OR Next Gen Reading score greater/equal to 250 AND Writing score greater/equal to 260; OR "preparatory course(s)", contact academic advisor at 920-498-5444)

This course is intended to develop analytic reasoning and the ability to solve quantitative problems. Topics to be covered may include: construction & interpretation of graphs; descriptive statistics; geometry & spatial visualizations; math of finance; functions and modeling; probability; and logic. Appropriate use of units and dimensions, estimates, mathematical notation, and available technology will be emphasized throughout the course. (Prerequisite: Next Gen QRAS score greater/equal to 250 AND Rdg score greater/equal to 250; OR ACT Math score greater/equal to 21 AND ACT Reading score greater/equal to 16-contact an academic advisor 920-498-5444)

Creating worksheets; enter data, make modifications, work with formulas and functions, create and enhance charts, manage data, transform data, conditional formatting, importing/exporting data, introduction to PivotTables and PivotCharts.

This course provides an overview of the interconnectedness among careers within Information Technology. Learn how various fields within the broader industry each play an integral role and how a personal brand fits into preparing you for your career choice.  (Corequisite: 10-890-101, College 101)

Learners will develop an understanding of cybersecurity methods, terms, and key technical concepts to identify and protect assets against attacks.

Covers working with data tools, advanced formatting techniques, advanced functions, evaluating formulas, collaboration tools, data validation, form controls, macros, working with templates, and collaborating with multiple Excel users. (Pre-req: 10-103-131, MS Excel Intro)

Students taking Introductory Statistics display data with graphs, describe distributions with numbers, perform correlation and regression analyses, and design experiments. They use probability and distributions to make predictions, estimate parameters, and test hypotheses. They draw inferences about relationships including ANOVA. (Prerequisite: Next Gen QRAS greater/equal to 250 AND Rdg greater/equal to 250; OR ACT Math greater/equal to 24 AND ACT Rdg greater/equal to 16; OR C or better in Inter Alg or higher-contact academic advisor 920-498-5444)

Power BI (Business Intelligence). This course provides an introduction and exploration of business analysis tools utilized in connecting, aggregating, and visualizing data from multiple sources to quickly summarize data. You will learn how to extract, transform, and load data, build a data model using modeling features, creating proper relationships, and design considerations. (Prerequisite: 10-103-132, MS Excel Part 2)

Database uses, database terminology, analyzing information requirements, data models, database design phases, entity relationships, normalization processes, database management systems, database objects, development environments, creating tables, writing queries using SQL, testing.

Instruction in the multiple approaches used for performing business analysis, planning activities and ongoing communication, defining scopes, process improvements, assumptions, constraints, and dependencies, and the management process.

The role of project management, developing a project proposal, demonstration of relevant software, working with project teams, sequencing tasks, charting progress, dealing with variations, budgets and resources, implementation, and assessment.

Investigate the fundamentals of computer programming using the Python programming language. Students examine data types, variables, conditional statements, looping, array structures, and structured programming techniques. Upon completion of the course, students will be able to use Python to apply problem solving skills to create applications for delivery to various platforms.

In this course, students will learn to organize, manage, and analyze very large data sets from various sources. Students will use software tools to present complex data in visually meaningful representations that can be communicated to business stakeholders. Upon completion, students will learn how to transform raw data into meaningful information that will be utilized for data-driven decision making. (Corequisite needed for Data Analytics program: 10-103-175, Power BI)

Create professional resume and cover letter, prepare for interviews, understand interviewing techniques, participate in a mock interview, evaluate outcomes of interview, and prepare for Career Experience. (Prerequisites: 10-105-100, Careers in Business OR 10-107-117, Careers in IT OR 10-201-100, Careers In Digital Arts)

Write queries in a relational database: creating tables, setting primary/foreign keys, populating tables, manipulating data and reporting. Explore join types, stored procedures, functions, and database administration SQL statements. (Prerequisite: 10-152-125, Database Design)

In this course, students will learn to use the R programming language to analyze data. Students will learn the syntax and data structures of the R language, and how to apply the language to perform traditional statistical analysis, such as means testing, variable correlations, and linear regressions. Upon completion, students will be able to create R programs to process data and create meaningful output rooted in sound statistical techniques. (Prerequisite: 10-804-189, Introductory Statistics)

Will provide business analysis and make focused organizational decisions by mining, aggregating, and conducting descriptive, prescriptive, and predictive analytics on big data using a variety of analytic tools. (For students in the Data Analyst program only - Prerequisite: 10-156-112, Data Visualization)

This science of psychology course is a survey of multiple aspects of behavior and mental processes. It provides an overview of topics such as research methods, theoretical perspectives, learning, cognition, memory, motivation, emotions, personality, abnormal psychology, physiological factors, social influences, and development.. (Prerequisite: Cumulative GPA of 2.6 or greater OR satisfactory reading and writing assessment scores OR 10-831-107, College Reading and Writing 1 with "B" or better OR Corequisite: 10-831-102, English Comp Prep).

Benefits and challenges of group work, necessary roles in a team, stages of team development, meeting facilitation, different approaches to problem solving, consensus, data acquisition, analysis, developing alternative solutions, implementation and evaluation.

Develop knowledge skills, process of lean operations management/JIT, quality management, quality control, continuous improvement methodologies, statistical process control chart interpretation, material handling and production logistics.

Develop knowledge skills process of lean manufacturing/JIT environment, systems approach, productivity attainment, line balancing, kaizen, Kanban, 5S system, value stream mapping, human resource development and lean operations management. (Corequisite: 10-182-111, Lean Operations)

This course introduces the study of diversity from a local to a global perspective using a holistic, interdisciplinary approach that encourages exploration and prepares students to work in a diverse environment. The course introduces basic diversity concepts, examines the impact of bias and power differentials among groups, explores the use of culturally responsive communication strategies, and compares forces that shape diversity in an international context. (Prerequisite: Next Gen Rdg score greater/equal to 250 AND Writing score greater/equal to 237; OR ACT Rdg score greater/equal to 16 AND English greater/equal to 15; OR 10-831-107, College Reading and Writing with a B or better.)

Please see the curriculum note if such note references elective options.

Develop a general awareness of Lean Six Sigma: what it is, why it matters, what makes it successful. This course provides an overview of Six Sigma concepts and language and an introduction to the Define-Measure-Analyze-Improve-Control (DMAIC) process improvement cycle. This course will prepare you to take the green belt six sigma certification. (Pre-requisite: 10-182-112, Lean Manufacturing)

This course focuses on developing effective listening techniques and verbal and nonverbal communication skills through oral presentation, group activity, and other projects. The study of self, conflict, and cultural contexts will be explored, as well as their impact on communication. (Prerequisite: Next Gen Rdg score greater/equal to 250 AND Writing score greater/equal to 237; OR ACT Rdg score greater/equal to 16 AND English score greater/equal to 16; OR preparatory course-contact an academic advisor at 920-498-5444)

In this course, students will build upon the skills learned in Data Analytics 1. Students will learn to work with large data sets and organize that information for effective data analysis. Students utilize commercial data analysis software packages and create custom computer programs to analyze data. Upon completion of the course, students will be able to perform analysis of relevant data with various software tools and use the generated information to help make informed business decisions. (Prerequisite: 10-156-114, Data Analytics)

Students can select any hands-on experience related to: Internship, Field Research, Service Learning, Simulations, Capstone, Industry Related, or International. Instructor approval required. (Prerequisite:10-105-103, Career Fundamentals)