This course will introduce the data analytics process and how to apply statistical methods in different phases of the process. Students will learn statistical methods of creation, collection, analysis, validation and visualization of quantitative data to make recommendations for business decisions.

This course introduces students to the opportunities and challenges related to data in a business environment in terms of identifying data for decision making, collecting, storing, and processing data. This course helps students understand structured and unstructured big data that is available from a variety of sources. The course will cover techniques to obtain data from the web, APIs, and databases in various formats. Students will learn the basics to store, clean, prepare, share, process, and report on this data.

This course helps students apply data analytics process in real business environments. Modern businesses collect vast amounts of data that are critical for managing the business today and driving innovations for tomorrow. Case studies will be used to prepare students for real world problem solving by developing their ability to recognize the different types of problems, the analytics methodology used, how the analysis was performed using statistical analysis software tools, how the results were interpreted, and how the analysis was translated into a presentation to inform business decisions.

This course introduces students to programming languages for effective data analysis. Students will learn to design, code and test programs necessary for a statistical programming environment. Data types and control flow will be applied to manipulate data, interpret it and model it to present meaningful output.

This course uses practical case analysis to provide an overview of methods, processes, architectures, applications and technologies that support data analytics with a focus on data visualization for presenting data in a meaningful way that will help businesses make decisions and take actions.

This course provides students with leadership and communications skills through the lens of big data and business intelligence to help students solve problems, manage risks and enhance competitive advantage for organizations. Students will gain understanding on how to communicate with other stakeholders to evaluate business needs, and how to communicate the outcomes of the analytics process to guarantee the effective application of its outcomes. Students will also gain people-management, negotiation, and conflict management skills for implementing useful business intelligence solutions, providing guidance for the organization and its partners, and coaching the team on the different kinds of analytics or data modeling. Students will apply strategic frameworks and personal and organizational leadership models to real-life business issues through case studies.

This course introduces students to techniques used to collect data from different sources, i.e., social media sources. Students will apply statistical methods to process text data in any natural language with minimum human effort. Students will also apply algorithms in text mining involving correlation, regression, pattern recognition, and knowledge extraction to derive insights about data sources and their potential applications.

This course will introduce students to recent developments in advanced analytics techniques. Predictive analytics encompasses a variety of machine learning techniques that analyze current and historical facts to make predictions about unknown future events. In this course, students will learn how to build a predictor through supervised or unsupervised techniques in a training stage, and will evaluate the predictor performance through a tester stage.

Students explore the ethical and social considerations of privacy in a globally connected work. This course examines the effect of the internet and biometrics on privacy laws, policies, and technology, and the impact to business sectors and individual and corporate rights. Students evaluate the cost of new personal data privacy initiatives and the impact to application domains in the future.

This course is structured along the 4-stage process of data mining, i.e.: (i) Problem Identification, (ii) Creating the Analytical File, (iii) Conducting the Analytics, and (iv) Measurement and Implementation. Students will learn how to both identify and prioritize business problems, while appreciating the importance of data and the data audit process. In addition to exploring the key principles in designing analytical reports, advanced data mining and modelling are discussed. Moreover, methods for measuring and deploying solutions are explored.

In this course, students apply knowledge acquired during the program to a project involving actual data in a realistic setting. They will go through different steps of the business analytics process to create business value. Using various techniques like, ways of cleaning-up data, multiple algorithms for analysis and modeling, students will make data-driven decisions in response to a real business challenge.