Data science has matured into a cross functional discipline. In simple terms, its main purpose is to extract meaningful information from a variety of data sources. This definition is very general and must be explored in more detail to understand the building blocks needed for success. Related workgroups must understand each other and work together to make meaningful impact. Effective data science is a critical enabler for companies to become “data-driven” and to “compete on analytics”. To give shape to data science as a discipline, this course introduces core principles and concepts to provide a solid foundation of understanding. Data science is described in terms of its, purpose, capabilities, techniques, approaches and skills. It’s dependencies on other disciplines and how it enables value creation within the broader “data-driven” ecosystem is also provided.

This course introduces data science and sets the stage for understanding how process, data, skills, culture, methodology and technical building blocks collectively drive results.

• Key concepts in data science
• How data science relates to other related disciplines
• Practical data science process lifecycle steps
• Common data science tools, techniques and modeling categories
• Recommended data science approaches, methods and processes
• The data science process
• Critical success factors for data science
• Why organizational culture and data literacy are challenges that must be managed

• Business managers and executives
• Technology managers and executives
• Data science and data engineering team members
• Business analysts, statisticians and modelers
• Process managers and decision makers
• Business measurement and performance analysts
• IT analysts and developers
• Data management analysts
• Technology and business architects
• Analytics, business intelligence, data science and data engineering program leaders
• Anyone with an interest in understanding the capabilities, opportunities and challenges offered by data science