Why the Diploma in Business Analytics?
This course Business Analytics will give you the skills and expertise that you need to support data-driven decision making in a business environment.
Business Analytics professionals are trained in extracting information and insights from large data sets to understand the past and current performances of a company, make informed decisions, and predict future trends.
Possible career paths
Holders of a Data Science Specialization Diploma will function as a data scientist in the areas of health science, public sector, industry, or entertainment with an emphasis on algorithm creation and refinement to build better predictive models.
Similar career paths exist in the emerging fields of sports analytics and e-commerce that require the gathering, management, and analysis of large and diverse amounts of data to support data-driven strategic decision making.
Overview of the basic concepts and techniques in predictive analytics as well as their applications for solving real-life business problems in marketing, finance, and other areas. Techniques covered in this course include: decision trees, classification rules, association rules, clustering, support vector machines, instance-based learning. Examples and cases are discussed to gain hands-on experience.
Introduces fundamental concepts and modelling approaches to solve problems that are faced by decision makers in today’s fast-paced and data-rich business environment. Different decision alternatives are analyzed and evaluated with the use of computer models. Topics include the most commonly used applied optimization, simulation and decision analysis techniques. Extensive use will be made of appropriate computer software for problem solving, principally with spreadsheets.
Introduction to new tools for data analytics that can be used to discover, collect, organize, and clean the data to make it ready for analysis. Emphasis is placed on software tools used to interact with data sources and provision of user skills to create business applications that encompass a variety of business data sources; such as customers, suppliers, markets, competitors, and regulators. Software packages used to clean and organize the data for analysis will be introduced, as well as software to enable users’ understanding of the data that is collected.
Examination of tools and methods used in data analysis, including basic and advanced analytic tools, as well as machine learning techniques. One or more data analysis packages/programs are used to analyze different types of business data. Statistical and other analytic methods, such as data mining, machine learning and various techniques, and their application to business data analytics are explored.