Tools and Analysis for Business Strategy. Course Philosophy. Background on the Analytics Revolution

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1 Tools and Analysis for Business Strategy Professor Jason Snyder Mondays: 1:00-3:50 & 7:00-10:00 (C315) Office Hours: By appointment Course Philosophy In this class, students will gain experience in using data, discussing data, and presenting data within the context of a variety of real world settings. This course is aimed at giving students a competitive advantage on the job market, and therefore is not a pre-packaged class. It is, to my knowledge, unique in the business school world. Background on the Analytics Revolution The process of making business decisions is undergoing a major shift in light of the exponential growth in data collection and computational power. Prior to this shift, the best business decision-makers, be they leaders within firms or external consultants, combined three different sets of skills. They had a basic disciplinary knowledge of subjects relevant to business such as economics, organizational behavior, finance, and marketing. This understanding was not necessarily formal. Many successful business decision makers intuitively understood much of the content of these disciplines without formal training. Great business decision-makers also had a depth of specific industry understanding. 1 With this deep industry knowledge they were able to inform their decisions with anecdotal evidence and useful comparisons. Finally these decisionmakers had effective communication skills. They were able to present their information in a clear manner that showed the logic behind their reasoning in a way that convinced others to buy into their way of thinking. The most important business leaders didn t outsource any of these fundamental skills, they integrated them. The reason economists don t run corporations is that they lack depth of specific industry knowledge and often effective communication skills. The challenge of the information age is what to do with it 2 1 Mark Cuban s formative experience in the software industry is on point: Every night I would take home a different software manual, and I would read them... It worked. Turns out not a lot of people ever bothered to RTFM (read the f****** manual), so people started really thinking I knew my stuff. As more people came in, because I knew all the different software packages we offered, I could offer honest comparisons and customers respected that. Quote taken from his website 2 Chris Cox, Facebook s vice president of product, in a recent New York Times interview.

2 The analytical revolution is changing the process of business decision-making in profound ways. 3 The classic skill sets aren t being replaced, but need to be enhanced to navigate the data revolution. Basic disciplinary knowledge, depth of specific industry, and effective communication skills are still the hallmarks of effective business decision-making. Quantitative reasoning and statistical analysis are rapidly becoming indispensable business disciplinary knowledge. Likewise, knowing for your firm and industry what data is generated, how to access it, and what it means is crucial. Visual design skills are also becoming an increasingly large part of the toolkit of the effective business communicator. Creating information displays that spark creative thinking is not just the domain of the graphic artist, but is rapidly becoming part of the business analyst s toolkit. If data analytics is going to become a competitive advantage for firms and individual careers, it can not be outsourced. Course Goals To become comfortable using quantatitive data to answer structured questions and to spark creative insight. To become more capable in communicating your ideas in a persusive manner. To reinforce learning from your data and decisions class and also introduce new approaches and techniques where necessary. To gain familiarity with all stages of the workflow associated with analyzing and presenting quantatitive results. 4 To provide an environment where students with varying degrees of quantitative comfort can thrive and benefit. Quants and non-quants are welcome. To become more comfortable with the consumption of data analysis. Even if you don t go onto a career in data analysis you need to know when others are adding value and when they are shoveling garbage at you. 3 The amount of data in our world has been exploding and analyzing large data sets socalled big data will become a key basis of competition Leaders in every sector will have to grapple with the implications of big data, not just a few dataoriented managers... Companies must tackle significant hurdles to fully capture big data s potential - including a shortage of skilled analysts and managers. The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data. Big Data: The Next Frontier for Innovation, Competition, and Productivity by The McKinsey Global Institute 4 We will be using Excel, R (statistics software) and QGIS (open source mapping software)

3 Course Structure This is a clinical course. A famous dictum in medical education is see one, do one, teach one. You will spend the vast majority of your time in this class performing data analysis and learning how to use tools. Learning by doing is the best way to become an accomplished analyst. There will be a limited number of readings that accompany the lectures which will be made available on the web. Reading Much of the course instruction will be done using videos to introduce students to software and techniques. Some of the videos will be from lynda.com (use as a gateway to lynda.com) and some will be created by me. Recommended readings are for those wishing to explore the topics in further depth and will be scanned and put on the web. For the curious individual I would recommend the following books & blogs: Any book written by Edward Tufte Visualize This by Nathan Yau [for fun] Company Engagements This class will have several company engagements. This is a unique opportunity that many students have enjoyed immensely.

4 Grading Quizzes (5%): Quizzes will be given on a pass / not pass basis. The quizzes will be very easy designed to test basic knowledge. Team assignments (40%) Individual assignments (15%) Take home individual final exam (40%) Thoughtful questions and participation during the class will help with borderline grades.

5 Class 1, March 31 st : Introduction & Principles of Analytical Design (1) Introduction to the course (2) The principles of effective analytical design (3) Using data to answer strategy questions Background Readings: Tufte, Edward Beautiful Evidence Chapter 6-8; Graphics Press 2001 Tufte, Edward Envisioning Information Chapter 2; Graphics Press 1990 Quiz next week: Excel 2013 essential training. This video is 7 hours so please schedule your time accordingly. If you are confident that you know the material please skim the video. Training/ html?org=ucla.edu Class 2, April 7 th : Comparing Distributions & Creating Summary Statistics Quiz: Excel basic numerical operations, vlookup, countif, and pivot tables (1) Comparing distributions (2) Interpretting non-normal distributions (3) Effective display of distributions (4) Creating summary statistics Background Readings: Savage, Sam The Flaw of Averages Chapters 6-12; Wiley 2009 Lowes & Home Depot assignment due at 11:59 pm on April 13 th [Team assignment worth 10% of total grade]

6 Class 3, April 14 th : Testing differences Quiz: Basics of QGIS (1) Discuss Home Depot / Lowe s assignments (2) Recap of drawing distributions (3) Testing for differences across samples (4) Conducting statistical tests without data Background Readings: Chapters 3 & 9 of Testing for differences in excel 2010 Quiz next week: Watch up and running with R html?org=ucla.edu Class 4, April 21 st : Multi-variate regression analysis Quiz: R basics (1) From t-tests to regression (2) Conditional differences (3) Dummy variables (4) Creating graphics from regression results Background readings: Sykes An Introduction to Regression Analysis Quiz next week: Introduction to relational databases video Begin working on campaign spending and elections outcomes assignment

7 Class 5, April 28 th : Regressions and Prediction Part I (1) Regression and causation (2) Creating predictive models (3) The use of R 2 in predictive models (4) Testing the validity of a predictive model Background readings: Dranove, David Interpretting dummy variables when you run interactions Campaign spending and election outcomes assignment due at 12:00 pm on May 5 th [Individual assignment worth 15% of total grade] Class 6, May 5 th : Civic Science Guest Speaker & Regression and Prediction Part II (1) Guest Speaker: Zachariah Sharek: Director of Strategy and Innovation at CivicScience.com (2) Testing the sensibility of predictive models CivicScience.com assignment due at 12:00pm on Friday May 23 rd [Team assignment worth 15% of total grade]

8 Class 7, May 12 th : Benchmarking performance (1) Different measures of performance (2) Benchmarking productivity (3) Creating graphics from regression results Background readings: Dranove, David Benchmarking and Regression Continue working on Civic Science Assignment due at 12:00pm on Friday May 23 rd Class 8, May 19 th : Analysis of the Essential Sources of Variation in a Data (1) Analysis of Variance (ANOVA) (2) Multilevel Models Background readings: Chapters 10, 11, 13, & 14 on Analysis of Variance in excel 2010 Begin PWC airline assignment

9 Class 9, May 23 rd [Memorial Day Make-up]: Forecasting (1) Mean, Variance, and Auto-correlation in Time Series (2) Trend, Seasonal, Cycle, and Irregular Components Assignment due at 12:00pm the day on June 2 nd : PWC airline assignment [Team assignment worth 15% of total grade] Class 10, June 2 nd : PWC in class exercise (1) Estimating demand curves in the airline industry (2) Course wrap-up