Identify business cases for using data science and Big Data
Understand how predictive analytics and machine learning can be used by businesses to drive commercial outcomes
Understand the pitfalls of working with different types of data, including Big Data
Syllabus: Data Science in Business
This course is designed for managers and executives who want to understand how data analytics can be used to drive business forward. In 1 hour of instructional time, you will learn how other companies have used data analysis to drive their business forward, reduce costs, and target new markets.
By the end of this course, students will be able to:
1. Identify business cases for using data science and Big Data
2. Understand how predictive analytics and machine learning can be used to improve supply chain management
3. Understand the pitfalls of working with different types of data, including Big Data
- Concept reviews: these are comprised of short five question quizzes that cover the most important concepts and ideas in each lesson. They encourage holistic understanding and are multi-faceted question types (i.e. drag and drop, fill-in-the-blanks, matching, etc).
- Accompanying PDFs to use as reference materials
- Printable guidelines that managers can fill out to assess the role of data in their team/company
1. Data in Business Today (24 min)
The leadership problem
The functions of data science in business
How have businesses used data science?
2. What Do Data Look Like? (22 min)
What is the value of data?
The 3 V’s of data
Integrating social media data in business
3. Data Challenges in Business (18 min)
Challenges of using data
Additional data challenges
Total instruction: 1 hr, 6 min
Richard Heimann is the Chief Data Scientist at Cybraics, Inc. He is part of the adjunct Faculty at University of Maryland, Baltimore County and an Instructor of Human Terrain Analysis at George Mason University. Rich serves on the Selection Committee for the AAAS Big Data & Analytics Fellowships Program. He has deep expertise running data science teams and is the co-author of “Social Media Mining with R.”
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