This course explores the fundamental concepts of modern business analytics. Students will gain an understanding of how data analysis works in today’s business organizations. This course provide the foundations needed to understand business analytics and show students how to manipulate data and develop simple data models. The course will explore the fundamental tools and methods of data analysis and statistics with a focus on visual representations of data, descriptive statistical measures, probability distributions and data modeling, sampling and estimation, and statistical inference. 

This course continues the exploration of business analytics from the point of view of predictive and prescriptive analytics. Students will learn to develop approaches for applying trendlines and regression analysis, forecasting and introductory data mining techniques, building and analyzing models on spreadsheets, and simulation and risk analysis. The course also explores linear, integer and nonlinear optimization models

In this course, students will learn how business analytics, Data Science, artificial intelligence and decision support systems are interrelated and how they work together to provide businesses with the information needed to support decision-making. The course will review Predictive and prescriptive analytics with a focus on Artificial Intelligence, Machine Learning, Big Data, Robotics, social Networks and the Internet of Things and their roles in data science. 

This course explores the tools and techniques used to solve a variety of business and data science problems. Students will start by installing and configuring the tools needed to solve professional-level data science problems. The course will explore the use of the widely used R language and Git versioncontrol system within the context of data science and analytics. They explain how to wrangle data into a form where it can be easily used, analyzed, and visualized to support business decisions. Students will master the powerful R programming techniques and learn troubleshooting skills for probing data at larger scales. 

This course explores topics on data visualization within the context of the tools used to present data in various visual formats. Data visualizations are a powerful method of making data accessible and understandable to non-specialists. Students develop their skills in data presentation through the use of Tableau, a popular data visualization tool utilized by analysts, marketers, statisticians, and any business leadership role that depends on data. Students learn how to use graphs and charts, calculated fields, maps, and interactive dashboards to visualize data and refine the decision-making process.

This course focuses on the visual presentation of data to better support business decision making. Students will learn how to create world-class Power BI business analysis dashboards that transform generic data into visual stories that provide a deeper insight to the information that is being presented. Students will learn to create dashboards from Excel, SharePoint, SQL, Azure Oracle and Dynamics 365. 

This course will introduce the Python programming language within the context of data science and analytics. The course will start with the introduction of the Python programming environment, the language, and its syntax, control statements, functions and libraries. Students will learn to work with large datasets and data frames with Python.

This course will continue the exploration of the Python programming language within the context advanced topics in data science including Artificial Intelligence, Big data and Natural Language Processing, Data Mining, Machine Learning and cognitive computing. The course will conclude with an overview of Big Data: Hadoop®, Spark™, NoSQL and IoT.

This course focuses on the application of data science with Hadoop. Students will learn practical information about applying data science and Big Data in Hadoop environments. This course will explore practical business applications combined with technical details to offer insight into the Hadoop environment. The course will review the core topics of data science and then explore the Hadoop tools, the Hadoop Distributed File System, importing data, and working with data in the Hadoop environment. Topics include Hadoop tools and utilities, preparing data with Hadoop, Managing work and data flowsBatch Analytics and real-time analytics, visualizing big data and cloud computing. 

This course will introduce what is known about how humans and machines learn. Students will be introduced to the most important classes of machine learning algorithms, and what each of them can do. The course will review key concepts ranging from neural networks to supervised and unsupervised learning. Students will learn the key steps needed to build a successful machine learning solution, from collecting and finetuning source data to building and testing the solution. The course will guide students through the constructing of complete solutions with ML.NET, Microsoft’s powerful open source and cross-platform machine learning framework. 

This course builds on the skills learned in the Student Success Strategies course or its equivalent. It provides information on how to use communication skills to make 
successful presentations to prospective employers, uncover the hidden job market, and identify employment opportunities. Students engage in self-assessment to identify transferable skills and learn how to effectively communicate these to employers. The course also introduces the use of artificial intelligence (AI) as a career support tool. Students apply AI to assist with resume and cover letter development, job searching, and interview preparation, while maintaining their own authentic voice and critically evaluating AI-generated content. Students may be videotaped during a mock interview and will participate in the analysis of their performance in the interview.

This field placement will prepare students for their transition to the workforce. The practicum will be completed in a business environment working under the supervision of experienced personnel. Students are provided with a description of duties he/she will perform on the job. At the end of the placement, the practicum host will provide an evaluation on the student and the student will provide the college with an evaluation on the placement.