COURSE NUMBER: EWMBA296.13

COURSE TITLE: Data Science

UNITS OF CREDIT: 2 Units

INSTRUCTOR: Greg La Blanc

E-MAIL ADDRESS: lablanc@haas.berkeley.edu

MEETING DATES: Block Week: Monday 3/27 to Friday 3/31, 9AM-5PM

PREREQUISITE(S): None

Please note this portion of the course description is from Spring 2016 and is for reference only. Spring 2017 course topics may differ.

CLASS FORMAT: The format will be about half lecture and discussion and half guest speakers from industry.

REQUIRED READINGS: We will use a textbook and additional readings and cases.

BASIS FOR FINAL GRADE: Students will be responsible for individual assignments and a group presentation.

CAREER FIELD: General Management

ABSTRACT OF COURSE'S CONTENT AND OBJECTIVES:
It’s the Era of Big Data. Data Scientist is the sexiest profession of the 21st century. Everywhere you turn, we hear that we cannot compete without being on the cutting edge of analytics and that those people and executives and companies and organizations that are not fluent in the art of Big Data will be left behind.

The purpose of this course is NOT to make you into a Data Scientist! (Or, for that matter, a statistician, econometrician, data engineer, computer scientist, mathematician, or any number of other Big Data professions that would take years and years of advanced education to achieve.)
The purpose of this course is twofold:

• To teach you enough basic quantitative analytics so that you will be able to leverage data scientists and analytics experts to make your make your strategy better, more robust, more reliable, more profitable. In order to be able to leverage these experts effectively, you need to speak the language of analytics: you’ll need to know how to ask your data science experts the right questions, how to give the analytics experts the information they need to do their best possible work, and most importantly, you’ll need to know how to tell if you’re being hoodwinked. And in this course you’ll acquire these skills.

• To help you assess and modify a company’s data strategy as well as design new business models around data collection and analytics. The proliferation of massive amounts of data can disrupt traditional business models and this course will help you to manage that disruption.

At the conclusion of this course, you should be able to:

• Assess a company’s (your company’s?) data strategy
• Assess a company’s competitive data advantage: is it in the data or the analytics?
• Design a company’s data strategy.
• Decide for a given challenge whether you need to hire a Data Scientist or a Statistician or both or neither
• Decide for a given challenge whether you need to undertake predictive or experimental analytics or both or neither
• Determine the right analytical framework for approaching strategy problems
• Be fluent in a variety of techniques for determining whether the analytical solution is robust and stable, how well it fit the data, and be able to deploy multiple techniques for determining whether the analytical solution over or under-fit the data
• Be capable of evaluating the analytical work or prospective work of any consultant to determine whether or not he or she should be hired or fired

Topics will include:

• Review of Decision Making: Gut Feeling vs. Data
• Basics of Data Mining
• Logistic Regression
• Decision Trees and Random Forests
• Clustering
• Nearest Neighbor and Collaborate Filtering
• Problems of Overfitting
• Confusing Correlation and Causation

Use Cases for Big Data

• Functional Areas: Marketing, Pricing, Fraud Detection, Lending and Portfolio
• Decisions, Active Investing, Costing, Supply Chain Management, Maintenance, Distribution, and Human Resources.
• Industries: Manufacturing, Retailing, Banking and Investing, Military and
• Policing, Services, Agriculture, Education, Aviation and Trucking,
• Healthcare and Insurance and Sports

BIOGRAPHICAL SKETCH:
At Haas, Greg teaches primarily in the areas of finance and strategy in the MBA and MFE programs and in Executive Education. Greg has also worked in competitive intelligence and litigation consulting and has advised consulting teams in finance, marketing, and strategy. His research interests lie at the intersection of law, finance, and psychology in the area of business strategy and risk management. Greg is the recipient of teaching awards including the Earl F. Cheit Award for Outstanding Teaching, 2009; and the Haas EWMBA Graduate Instructor of the year, 2004-2005.

Greg received a B.A. (History, Politics, Philosophy, and Economics) and a B.S. Economics (Business Administration) from the University of Pennsylvania, where he continued his education as a University Scholar and graduate fellow, studying in the schools of Arts and Sciences, Business, and Law. He later pursued a J.D. at the George Mason University and an L.L.M at Berkeley’s Boalt Hall. Greg has taught undergraduate and graduate courses in all areas of business. Prior to arriving at the Haas School in 2005, Greg taught at Wharton, Duke, and the University of Virginia.