Data managers must have the skills and knowledge to align their employer's vision with their data vision, to create interoperable data systems and enterprise-wide data architecture, to enforce data governance, and to manage artificial intelligence and machine learning for decision-making insights.
According to the Bureau of Labor Statistics most recent figures, overall employment of mathematicians and statisticians is projected to grow 30 percent from 2018 to 2028, much faster than the average for all occupations. Businesses will need these workers to analyze the increasing volume of digital and electronic data. With the explosion of data and analysts, it has become increasingly important for companies to hire managers capable of achieving business objectives by leveraging data best practices.
This course equips you with the knowledge and skills to transform your organization into a data-driven organization. By examining industry case studies, lessons learned, and the latest data analytics tools and platforms, you will learn how best to gain actionable insights from big data, as well as to develop data solutions and data transformation road maps for businesses of varying sizes and complexity levels.
- Defining business objectives
- Linking objectives with performance data
- Data integration
- Data standardization
- Data architecture and rationalization
- Process and system engineering requirements
- Change management for data transformation
- Data transformation case studies
- Data governance and Chief Data Officer (CDO)
- Data innovations and digital transformations
- Artificial intelligence and machine learning for data managers
- Current tools for data analytics
- Decision-making and data visualization of uncertainties
Course Learning Outcomes:
- Develop best practices for instituting data transformation
- Learn to manage data talent and investments
- Test state-of-the-art automation tools for data governance
- Align a data vision with you organization’s mission and KPIs
- Establish a road map for data applications and systems rationalization
Course Typically Offered: Online during our Fall academic quarter.
Software: Tutorials of leading automation tools offered by Collibra, Informatica, SAP, and IBM will be available to students during this course; there is no additional charge for these.
Prerequisites: A bachelor’s degree or working experience in software programming, science, business management, or information science.
Contact: For more information about this course, please contact email@example.com.
Course Number: CSE-41296
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence Analysis, Systems Engineering