Teaching Predictive Analytics at the University of Colorado
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- DataRobot
- Alteryx
Tech Stack
- Python
- R
- SQL
- Excel
Implementation Scale
- Departmental Deployment
Impact Metrics
- Digital Expertise
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Education
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
- Predictive Quality Analytics
Services
- Data Science Services
About The Customer
The customer in this case study is the University of Colorado, specifically the Leeds School of Business. The university is a large educational institution located in the United States. The Leeds School of Business is a department within the university that offers various business-related programs to its students. The school is committed to providing its students with the necessary skills and knowledge to succeed in the business world. This includes teaching them about the latest technologies and trends in the business world, such as predictive analytics. The school recognizes the importance of predictive analytics in reshaping business and society and is therefore keen on integrating it into its curriculum.
The Challenge
Predictive analytics is reshaping business and society, raising serious questions about how colleges and universities should prepare graduates. One answer may be to teach predictive analytics to all business school students. What would it take to implement this important vision and why is it not currently being done? As a business analytics professor, Kai Larsen’s goal is to teach a mixed range of students: those who immediately understand how predictive analytics has reshaped their future jobs (Information Management and Marketing), those for whom different flavors of business analytics have long since infused into the core of their fields (Operations Management and Finance), and those for whom predictive analytics currently is reshaping “only” a small part of their discipline (Accounting). It is becoming clear that all of these students must, at a minimum, understand predictive analytics conceptually to make decisions that will affect the future of their companies as machine learning tools continue to provide business insights and drive change within and outside the enterprise.
The Solution
To be successful in teaching predictive analytics, professor Larsen sees two complexities that have to be addressed: ALGORITHM-SPECIFIC PRE-PROCESSING OF DATA and ALGORITHM EVALUATION AND SELECTION. There are hundreds, if not thousands, of machine learning algorithms to select from. Some are commercially restricted but most are open source and available in specific packages in R and Python, or in special-purpose, cutting-edge packages like Tensorflow from Google. Predictive analytics used to require both an understanding of all of these algorithms and knowledge of how to select between them; the same algorithm will seldom be best for two different problems. As data sizes grow, evaluating all these algorithms brings outsized challenges for the infrastructure required to teach predictive analytics. These two complexities together explain why predictive analytics has remained in the purview of yearlong MS programs in analytics and, so far, outside the core business curriculum.
Operational Impact
Quantitative Benefit
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