This course provides an introduction to data management and analytics for business. The course will present a systematic view of analytics with a key focus on using data for business intelligence and descriptive analytics as well as an overview of predictive analytics and how they are used by businesses for creating a competitive advantage. The course emphasizes the managerial aspects of analytics along with applications and implementation challenges, rather than technical issues (e.g. coding). The course will also focus on how to understand and manage data as an asset that is at the core of value creation in today's digital age, with emphasis on technical aspects such as data structure, data models, and data preparation and exploration, as well as management aspects such as data reliability and quality. Students will learn and apply relevant business intelligence and analytics concepts for solving various types of business problems using real data and practical business cases. After completing the course, students will be able to do the following: 1. Organize data for answering business questions through applications involving data modeling, extraction, querying, and transformation. 2. Describe issues that can arise with data and the resulting models and implement techniques to address them effectively. 3. Explain how organizations can use business intelligence and data analytics, AI, and machine learning for operational and strategic purposes. 4. Use technology tools to perform descriptive data analytics, including data preparation, modeling, and visualization. 5. Build, train, test, evaluate, and deploy a machine learning model using graphical interface tools, and choose between competing models. 6. Communicate analytics results to managers and to a general business audience.
This course provides an introduction to data management and analytics for business. The course will present a systematic view of analytics with a key focus on using data for business intelligence and descriptive analytics as well as an overview of predictive analytics and how they are used by businesses for creating a competitive advantage. The course emphasizes the managerial aspects of analytics along with applications and implementation challenges, rather than technical issues (e.g. coding). The course will also focus on how to understand and manage data as an asset that is at the core of value creation in today's digital age, with emphasis on technical aspects such as data structure, data models, and data preparation and exploration, as well as management aspects such as data reliability and quality. Students will learn and apply relevant business intelligence and analytics concepts for solving various types of business problems using real data and practical business cases. After completing the course, students will be able to do the following: 1. Organize data for answering business questions through applications involving data modeling, extraction, querying, and transformation. 2. Describe issues that can arise with data and the resulting models and implement techniques to address them effectively. 3. Explain how organizations can use business intelligence and data analytics, AI, and machine learning for operational and strategic purposes. 4. Use technology tools to perform descriptive data analytics, including data preparation, modeling, and visualization. 5. Build, train, test, evaluate, and deploy a machine learning model using graphical interface tools, and choose between competing models. 6. Communicate analytics results to managers and to a general business audience.
