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UCI's Master’s of Analytics Program + Alteryx Gives Students an Advantage

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The University of Irvine's new Master’s of Science in Business Analytics (MSBA) is a cutting-edge curriculum of data science and analytics. Within this program is BANA 290: The Art and Science of Applied Forecast Modeling, a class in which students are taught how to build robust predictive analytics using Alteryx. Attend to see how Alteryx is helping students develop complex forecasting models that would have been nearly unimaginable without Alteryx!

David Savlowitz - Professor, University of California, Irvine
Michael Ponton - Professor, University of California, Irvine

Published in: Data & Analytics
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UCI's Master’s of Analytics Program + Alteryx Gives Students an Advantage

  1. 1. # A L T E R Y X 1 9 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX GIVES STUDENTS AN ADVANTAGE: PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS PRESENTED BY David Savlowitz CEO & Founder, Competitive Analytics, Professor of Predictive Analytics, UC Irvine dss@competitiveanalytics.com Michael Ponton Director of Analytics, Competitive Analytics, Professor of Predictive Analytics, UC Irvine mp@competitiveanalytics.com UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  2. 2. COMPLETE SESSION SURVEYS ATTENTION 2 • You were handed a survey as you entered the room. It should take less than 2 minutes to complete • Please return your completed surveys B E F O R E YO U L E AV E the room • Surveys are anonymous, and we rely on your opinion for improvement
  3. 3. Fact: Alteryx & More Coffee Correlates to Better Analytics!
  4. 4. # A L T E R Y X 1 9 4 INTRODUCTIONS UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  5. 5. # A L T E R Y X 1 9 5 DAVID SAVLOWITZ A L T E R Y X U S E R S I N C E 2 0 1 0 When I use Alteryx, I feel EMPOWERED With Alteryx, I can DECIPHER any business, the economy, the world. CEO & Founder, Competitive Analytics Professor of Predictive Analytics, UC Irvine UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  6. 6. # A L T E R Y X 1 9 6 MICHAEL PONTON A L T E R Y X U S E R S I N C E 2 0 1 0 With Alteryx, I can DECIPHER any business, the economy, the world. When I use Alteryx, I feel ENTITLED Director of Analytics, Competitive Analytics Professor of Predictive Analytics, UC Irvine UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  7. 7. # A L T E R Y X 1 9 7 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Professors of Predictive Analytics Master of Science in Business Analytics UCI Paul Merage School of Business
  8. 8. # A L T E R Y X 1 9 8 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS WhydidUCIhireus asprofessors?
  9. 9. Irvine Asset Group BERGSTRO M CAPITAL ADVISORS Uunu William Fox Homes JMB Realty Corporation © 2019 Competitive Analytics. © 2019 DECIPHER. All rights reserved. | Competitive Analytics 620 Newport Center Drive, Suite 1100, Newport Beach, California 92660 USA | 714 660 2799  | www.CompetitiveAnalytics.com | info@CompetitiveAnalytics.com
  10. 10. Competitive Analytics transform data | optimize decisions | maximize profits Competitive Analytics transform data | optimize decisions | maximize profits Price Optimization Are the prices, premiums, and discounts you set for your products and services optimized to generate maximum profit for your business? Predictive Analytics Do your predictive models deliver accurate macroeconomic outlooks, precision industry trends, and actionable company KPI forecasts? Performance Analytics Are your BI dashboards accurately and quickly measuring your organization’s performance . . . and deciphering which market drivers you should track? Logistics Is your company maximizing the effectiveness and efficiencies of assets, operations, human resources, sales, distribution, supply, and technology? Cost Minimization Is your organization a lean mean machine? Are you wringing-out the maximum efficiency from all your resources and assets? Sales & Marketing Analytics Are your sales and marketing functions driven by big data, advanced social media analytics, and customer analytics in order to maximize sales revenue? Demand Forecasting Do you know the precise level of demand for all your products and services, now and in the future, so you can budget and plan ahead? Geospatial Analytics Can you visualize your business at the global, national, state, county, city, MSA, ZIP, and custom polygon levels? And decipher which CMAs to target? Product Segmentation Do you know which products, services, features, amenities, and value dimensions your current and future customers will buy and why? Risk Analytics Are you deploying advanced analytics to identify and minimize your unsystematic risk as well as optimize your risk management activities? Govt. Planning/Econ. Dev. For government decision makers at the state, county, or city level, are you deploying advanced analytics in order to significantly enhance quality of life? Employee Analytics Do you get the best from your team? Who are your top performers? How can you recruit, empower, and motivate other employees to excel? AdvancedAnalytics Quick Proof of Concept Do you have challenging strategic initiatives and complex analytical projects? We conduct lightning fast QPOCs to help you realize next steps. BI & Analytics Concierge Do you have a go-to resource that will accurately answer your data-driven questions within minutes or hours instead of days, weeks, or months? Competitive Intelligence How well do you know your primary, secondary, and analogue competitors? Do you acquire vital competitive data to gain competitive advantages? Consumer Intelligence Do you effectively gather and analyze buyer behaviors in order to build deeper customer relationships and maximize customer lifetime value? rvices Price Optimization Are the prices, premiums, and discounts you set for your products and services optimized to generate maximum profit for your business? Predictive Analytics Do your predictive models deliver accurate macroeconomic outlooks, precision industry trends, and actionable company KPI forecasts? Performance Analytics Are your BI dashboards accurately and quickly measuring your organization’s performance . . . and deciphering which market drivers you should track? Logistics Is your company maximizing the effectiveness and efficiencies of assets, operations, human resources, sales, distribution, supply, and technology? Cost Minimization Is your organization a lean mean machine? Are you wringing-out the maximum efficiency from all your resources and assets? Sales & Marketing Analytics Are your sales and marketing functions driven by big data, advanced social media analytics, and customer analytics in order to maximize sales revenue? Demand Forecasting Do you know the precise level of demand for all your products and services, now and in the future, so you can budget and plan ahead? Geospatial Analytics Can you visualize your business at the global, national, state, county, city, MSA, ZIP, and custom polygon levels? And decipher which CMAs to target? Product Segmentation Do you know which products, services, features, amenities, and value dimensions your current and future customers will buy and why? Risk Analytics Are you deploying advanced analytics to identify and minimize your unsystematic risk as well as optimize your risk management activities? Govt. Planning/Econ. Dev. For government decision makers at the state, county, or city level, are you deploying advanced analytics in order to significantly enhance quality of life? Employee Analytics Do you get the best from your team? Who are your top performers? How can you recruit, empower, and motivate other employees to excel? 3195 Red Hill Avenue, Suite C, Costa Mesa, California 92626 | 714 545 2555 | CompetitiveAnalytics.com | info@CompetitiveAnalytics.com AdvancedAnalytic Quick Proof of Concept Do you have challenging strategic initiatives and complex analytical projects? We conduct lightning fast QPOCs to help you realize next steps. BI & Analytics Concierge Do you have a go-to resource that will accurately answer your data-driven questions within minutes or hours instead of days, weeks, or months? Competitive Intelligence How well do you know your primary, secondary, and analogue competitors? Do you acquire vital competitive data to gain competitive advantages? Consumer Intelligence Do you effectively gather and analyze buyer behaviors in order to build deeper customer relationships and maximize customer lifetime value? Full Service BI Solutions Do you have all the vital business intelligence tools to transform your data into actionable intelligence so you can make better and faster decisions? IT Services Is your “info technology” empowering C-Suite with actionable, easy-to-read dashboards and delivering effective software tools for your power users? Data Cleansing Raw data is dirty, error-prone, and misleading. Are you perpetually cleaning, wrangling, blending, parsing, interpolating, and extrapolating data? Web Data Extraction Valuable data resides on the open web. Are you perpetually extracting and analyzing vital competitive data to drive better and faster decisions? Strategic Planning Do you need to develop a data driven strategic business plan that evolves your company’s vision and also drives your day-to-day operations? Speaking Do you need an expert speaker on economic trends, industry outlooks, BI, big data, analytics, consumer trends, innovation, or competitive strategy? Training & User Groups Does your company want self-serve analytics? We are the “Center for Analytics & BI Excellence” and will help your firm gain analytical self sufficiency. Reporting & Dashboarding All orgs have vital data that is not used in decision making. Are your reports, dashboards and auto-alerts timely, accurate, interactive, and actionable? Alteryx User Group Southern California Tableau User Group Orange County, California Predictive Analytics User Group Southern California NEXT•24 C-Suite Roundtable Southern California StrategicServices Hosted User Groups Founded January 21, 2000 • 100+ clients • 2000+ projects • Full Service Big Data & Advanced Analytics
  11. 11. # A L T E R Y X 1 9 11 TODAY’S AGENDA UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  12. 12. # A L T E R Y X 1 9 12 TODAY’S AGENDA 1. About UCI 2. Metaphors & Archetypes 3. Real-World Syllabus 4. Core Teaching Vision 5. Mastering Predictive Analytics 6. Capstone Analytics 7. Q&A UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  13. 13. # A L T E R Y X 1 9 13 ABOUT UCI UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  14. 14. # A L T E R Y X 1 9 14 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Master of Science in Business Analytics (MSBA)
  15. 15. # A L T E R Y X 1 9 15 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS mSBAPROGRAM
  16. 16. # A L T E R Y X 1 9 16 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Concept
  17. 17. # A L T E R Y X 1 9 17 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS One-year full-time program Target mix of students with and without work experience Very global program, drawing students from over 15 countries ▪ Stem-Certified program 50 units (30 core + 20 electives), including capstone project Three flexible curricular tracks ▪ Data Analytics ▪ Marketing Analytics ▪ Operations Analytics Key Features
  18. 18. # A L T E R Y X 1 9 18 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Sample MSBA Program Summer Quarter •Foundations of Business Analytics •Statistics for Data Science Winter Quarter •Management Science for Analytics •Customer and Social Analytics •Capstone Prep •Two Electives Fall Quarter •Foundations of Marketing •Data and Programming for Analytics •Machine Learning for Business Analytics •One Elective Spring Quarter •Capstone Project •Two Electives
  19. 19. # A L T E R Y X 1 9 19 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Business Analytics Electives Operations Analytics Marketing Analytics Natural Language Processing Analytics & Technology Consulting Supply Chain Analytics Predictive Analytics Forecasting Models Deep Learning Applications Big Data Management
  20. 20. # A L T E R Y X 1 9 20 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Capstone Project 6-month data-analytic projects, sourced from companies in Southern California Amazon, Experian, Pacific Life, Ingram Micro, Disney, Coca Cola, Wells Fargo, Competitive Analytics, Eaton, Edwards Life Sciences, LA Rams, City of LA, Kaiser Permanente, Paciolan, Cerius, Niagara Bottling, Vizio/Inscape Project supervised by mix of ladder faculty and lecturers (including alumni and industry experts)
  21. 21. # A L T E R Y X 1 9 21 METAPHORS & ARCHETYPES UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  22. 22. # A L T E R Y X 1 9 22 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Dual Brain Metaphor Quant v. Strategist
  23. 23. # A L T E R Y X 1 9 23 Pulling Strategies & Analytics Together Dual Brain Metaphor
  24. 24. # A L T E R Y X 1 9 24 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  25. 25. # A L T E R Y X 1 9 25 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS bricoleurA person who engages in bricolage, which is the construction or creation of a work from a diverse range of things that happen to be available. Bricolage is a French that means the process of improvisation in a human endeavor. The word is derived from the French verb bricoler ("to tinker"), with the English term DIY ("Do-it-yourself") being the closest equivalent of the contemporary French usage. In both languages, bricolage also denotes any works or products of DIY endeavors
  26. 26. # A L T E R Y X 1 9 26 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  27. 27. QUANT (science) QUAL (art) Holistic Analytics
  28. 28. QUANT (science) QUAL (art) HYBRID (bricoleur) Holistic Analytics
  29. 29. # A L T E R Y X 1 9 29 REAL-WORLD SYLLABUS UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  30. 30. # A L T E R Y X 1 9 30 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Everydecision,bydefinition,isaprediction
  31. 31. # A L T E R Y X 1 9 31 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING Course Objectives I. Strategy
 • Understanding different philosophies of forecasting
 • Identifying and learning from master forecasters
 • Formulating the forecast question via an organization’s strategic and tactical objectives • Planning your forecast while understanding your audience II. Data
 • Finding reliable external data sources
 • Connecting to internal data sources and leveraging dark data • Preparing and cleaning time series data
 • Blending different data sources III. Method
 • Selecting forecast methods
 • Building modeling tables
 • Conducting quick forecasts
 • Utilizing statistical, mathematical, econometric, and analytical tools IV. Workflow
 • Building analytic work flows
 • Developing comprehensive what-if scenario-based forecasts • Evaluating and contextualizing forecast output
 • Designing dashboards and the art of data visualization V. Output
 • Presenting and reporting forecasts
 • Using forecasts and the decision-making process • Monitoring and benchmarking forecasts
 • Revising and evolving forecasts
  32. 32. # A L T E R Y X 1 9 32 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS To Our Prospective & Current Students . . . FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING
  33. 33. # A L T E R Y X 1 9 33 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS This course will integrate many of the actual techniques, approaches, and methodologies of custom predictive models developed for companies across a wide array of industries, such as Toyota, Honda, Yamaha, Boeing, AvalonBay Communities, Irvine Company, Western National Group, Cetera Financial, and other Fortune 100 and SMBs. FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING
  34. 34. # A L T E R Y X 1 9 34 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS The goal is to “go beyond the text book” - by not only covering core techniques of forecasting, but also revealing the unwritten guideposts of robust forecasting by: 1. Emphasizing the need for perpetual innovation required to develop accurate models 2. Inspiring the need to break rules 3. Leveraging imagination and ideation often needed to discover new data sources and engineer new variables 4. Perpetually seeking new ways to build new models never before built FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING
  35. 35. # A L T E R Y X 1 9 35 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Moreover, we emphasize a fusing of left brain thinking with right brain creativity – a coupling of differing yet synergistic skills often ignored in the realm of mathematics, statistics, business, and especially, predictive analytics. FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING
  36. 36. # A L T E R Y X 1 9 36 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Students who complete BANA 290 will become “bricoleurs” – learning that a single model or technique rarely if ever works – and contrastingly, learning to leverage and combine various techniques in order to create new predictive analytics models based on the contextual situation by interweaving four vital ingredients . . . FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING
  37. 37. # A L T E R Y X 1 9 37 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS 1. Understanding the strengths and weaknesses of traditional and proven modeling techniques 2. Applying best-in-class software that leverage machine learning 3. Developing powerful data preparation work flows, modeling tables, and interactive data visualization dashboards 4. Harnessing the unlimited power of a student’s imagination and ideas . . . All four synchronized to foster a truly unique and competitive expertise . . . to enhance competitive advantages and maximize sustainable growth for their organization. FROM BANA 290 SYLLABUS THE ART AND SCIENCE OF APPLIED FORECAST MODELING
  38. 38. # A L T E R Y X 1 9 38 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Rigoroussyllabus!
  39. 39. # A L T E R Y X 1 9 39 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS 6 Course Overview Week 1: Introduction to Predictive Analytics and Data Preparation via Alteryx Wednesday, January 9, 2019 A. Overview of Predictive Analytics and why this class will be VERY unique! B. Introduction to “Real World” Forecasting (How Fortune 500 companies use predictive analytics: Forecasting yogurt sales, cigarettes sales, motorcycle sales, home prices, apartment rents, and more!) C. How Honda & Yamaha use Predictive Analytics D. Understanding Different Philosophies and Methods of Forecasting and Predictive Models E. The Art and Science of Predictive Analytics F. Formulating the Forecast Question via Business Understanding and Setting up the Problem G. Data Sources, Data Collection, Data Joining, and Time Series Data H. Deciphering Signal versus Noise I. Learning from Master Forecasters and the Philosophy of Superforecasting J. Data Preparation via Alteryx K. PROJECT 1 ASSIGNED: Investigating Data and Preparing Data via Alteryx L. PROJECT 2 ASSIGNED: Superforecasting and Why Predictions Fail and Some Don’t Week 2: Predictive Modeling Approaches and Introduction to Data Visualization via Tableau Wednesday, January 16, 2019 A. Orientation about ML, AI, and Ensemble Models B. How Toyota uses Predictive Analytics C. PROJECT 1 DUE & REVIEW: Investigating Data and Preparing Data via Alteryx D. PROJECT 2 DUE & REVIEW: Superforecasting and Why Predictions Fail and Some Don’t E. Supervised Versus Unsupervised Models F. Linear Regression Part I G. ARIMA Models: Strengths and Weaknesses H. Seasonality, Special Events, Unexpected Events I. FRED Time Series J. Growth Rate Calculations K. Pattern Recognition L. Feature Engineering AKA Variable Transformations M. Dynamic What-if Time Series Forecast Visualization via Tableau: A Deep Dive Introduction to Tableau N. PROJECT 3 ASSIGNED: Architecting Advanced Analytics Work Flows via Alteryx O. PROJECT 4 ASSIGNED: Building Interactive What-if Forecast Scenarios via Tableau 7 Week 3: Diagnosing Time Series Data and Designing Advanced Work Flows Wednesday, January 23, 2019 A. How Cetera Financial uses Predictive Analytics B. PROJECT 3 DUE & REVIEW: Architecting Advanced Analytics Work Flows via Alteryx C. PROJECT 4 DUE & REVIEW: Building Interactive What-if Forecast Scenarios via Tableau D. NFL Data Set as analogue to predicting future performance E. The Art & Science of Cleaning, Understanding, and Organizing Data F. Review of Time Series PA Methods (e.g. Moving Average, Moving average, Weighted moving average, Kalman filtering, Exponential smoothing, Autoregressive moving average (ARMA) (forecasts depend on past values of the variable being forecasted and on past prediction errors), Autoregressive integrated moving average (ARIMA) (ARMA on the period-to-period change in the forecasted variable) e.g. Box–Jenkins, Seasonal ARIMA or SARIMA or ARIMARCH, Extrapolation) G. Focus on Linear Prediction H. Trend estimation (predicting the variable as a linear or polynomial function of time) I. Growth curve statistics J. Single Variable Summaries K. Multiple Variable Summaries L. Variable Cleaning M. Connecting to External Data Sources N. Preparing and Cleaning Time Series Data O. Linear Regression Part II P. PROJECT 5 ASSIGNED: Predicting Super Bowl 53 on February 3, 2019 Week 4: Creative Analytics Wednesday, January 30, 2019 A. How the Apartment Industry uses Predictive Analytics B. PROJECT 5 DUE: Predicting Super Bowl 53 on February 3, 2019 C. Creative Analytics: The missing ingredient D. Developing What-if Scenario-Based Forecasts E. Qualitative Drivers: How to integrate intuition, subjectivity, and experiential understanding into predictive analytics models F. Judgmental Methods: Composite Forecasts, Cooke's Method, Delphi Method, Forecast by Analogy, Scenario Building, Statistical Surveys, Technology Forecasting G. Demand & Supply Forecasting H. Feature Creation and Building a Modeling Table I. Variable Selection J. PROJECT 6 ASSIGNED: Developing a Yogurt Predictive Sales Model
  40. 40. # A L T E R Y X 1 9 40 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS 8 Week 5: Logistics Regression/Scoring Models Wednesday, February 6, 2019 A. How SMBs use Predictive Analytics B. PROJECT 5 REVIEW: Predicting Super Bowl 53 on February 3, 2019 C. PROJECT 6 DUE and REVIEW: Developing a Yogurt Predictive Sales Model D. Logistics Regression/Scoring Models E. Data Blending F. Data Scaffolding G. Metaphors of Predictive Analytics H. Advanced Data Visualization Week 6: Mid Term In-Class Exercise Wednesday, February 13, 2019 A. PROJECT 7: MID-TERM IN-CLASS EXERCISE: Delivering a Robust Sales Prediction Model in Under 3 Hours Week 7: Machine Learning Predictive Modeling Wednesday, February 20, 2019 A. REVIEW PROJECT 7: MID-TERM IN-CLASS EXERCISE - Delivering a Robust Sales Prediction Model in Under 3 Hours B. How Healthcare uses Predictive Analytics C. Conducting Quick Forecasts D. Using Machine Learning in Predictive Analytics via DataRobot E. Predicting Customer Response Week 8: Advanced and Custom Modeling Wednesday, February 27, 2019 A. How the Motorcycle Industry uses Predictive Analytics B. PROJECT 8 ASSIGNED: Benchmarking Predictive Model Accuracy (Assessment & Comparison) C. Evaluating and Contextualizing Forecast Output D. Designing Custom Dashboards and the Art of Data Visualization E. Boosted Decision Tree Models F. Forest Models G. Bayesian/Probabilistic Models H. AI/Neural Networks Week 9: Navigating the Political Minefield of Communicating Predictive Analytics Wednesday, March 6, 2019 A. How the Housing Industry uses Predictive Analytics B. PROJECT 8 DUE and REVIEW: Benchmarking Predictive Model Accuracy (Assessment & Comparison) C. Politics of Predictive Analytics D. Presenting and Reporting Forecasts E. Using Forecasts and the Decision-Making Process F. Output & Model Deployment G. Monitoring and Benchmarking Forecasts H. Revising and Evolving Forecasts I. PROJECT 9 ASSIGNED: Designing & Developing an Ensemble Predictive Model + Dashboard Week 10: Guest Speaker and In-Class Working Session Wednesday, March 13, 2019 A. How Competitive Analytics uses Predictive Analytics B. Possible Guest Speaker C. In-Class Working Session for guidance on final exam Week 11: FINAL PROJECT DUE AND FINAL PROJECT PRESENTATION Wednesday, March 20, 2019 A. PROJECT 9 DUE: Designing and Developing an Ensemble Predictive Model + Dashboard B. In-Class Presentation and Defense of Ensemble Predictive Model + Dashboard Note: This week-by-week course overview outlines our optimistic plan to cover a wide and deep array of topics. Based on the profile of students enrolled in this class, specific topics in the aforementioned course outline may not be covered, replaced with other topics, and/or expanded upon. Also, for students who absorb the content, concepts, and topics at an accelerated pace and wish to dive into deeper and/or wider areas of predictive analytics, we can accommodate by providing complementary areas of advanced content, concepts, topics, and assignments.
  41. 41. # A L T E R Y X 1 9 41 CORE TEACHING VISION UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  42. 42. # A L T E R Y X 1 9 42 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Legacytools
  43. 43. •95% of firms using Excel for financial reporting •9 in 10 spreadsheets contain errors •89 hours to create a management report •8 people to produce a typical report •92% of Excel users claim it too time-consuming •$6 Billion loss due to JP Morgan's “cut and paste” error in Excel in 2013 https://www.clearpointstrategy.com/road-excel-hell-paved-good-intentions-2/
  44. 44. # A L T E R Y X 1 9 44 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS TedWilliams performing “dataprep” backin1971!
  45. 45. Ted Williams 1939 (20) 1960 (41) .344 #6 521 HR #20 .634 SLG #2 Published: “The Science of Hitting: 1971
  46. 46. # A L T E R Y X 1 9 46 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Newtools
  47. 47. Browse Date Time Now Directory Tool Input MapInput Output Text Input XDF Input XDF Output Auto Field Strings Filter Formula Generate Rows Multi Field Formula Multi Row Formula Random [n%] of Records Record ID Sample Select Sort ` Tile Unique Date Filter Impute Values Multi-Field Binning Select Records Business Listing Matching Household Matching Append Fields Find and Replace Fuzzy Match Join Join Multiple Make Group Union Date Time Regular Expression XML Parse Tool Text to Columns Running Total Count Records Arrange Total Transpose Cross Tab Weighted Average Summarize Overlay Charting Tool E-Mail Tool Image Layout Map Render Table Text Legend Builder Legend Splitter Report Footer Report Header Explorer Box Text Comment Tool Container Buffer Create Points Heat Map Distance Find Nearest Generalize Make Grid Non Overlapping Drivetime Poly Build Poly Split Smooth Spatial Info Spatial Match Spatial Process Trade Area Distributed Analysis Heat Plot Histogram Contingency Table Frequency Table Pearson Correlation Coefficient Spearman Rank Correlation Coefficient Association Analysis Create Samples Oversample Field Plot of Means Scatterplot Field Summary Report Violin Plot AB Analysis AB Controls AB Treatment AB Trend Boosted Model Count Regression Decision Tree Forest Model Lift Chart Linear Regression Gamma Regression Naives Bayes Neural Networks Support Vector Machine Spline Model Logistic Regression Market Basket Rules Market Basket Inspect Nested Test Score Stepwise Test of Means TS Filter TS Covariant Forecast TS ARIMA TS Compare TS ETS TS Forecast TS Plot Append Cluster K- Nearest Neighbors K-Centroids Cluster Analysis K-Centroids Diagnostics Principal Components Amazon S3 Download Tool Amazon S3 Upload Tool Download Tool Google Analytics HDFS Input HDFS Output Marketo Input Marketo Append Marketo Output Mongo DB Output MongoDB Input Salesforce Input Salesforce Output SharePoint List Input SharePoint List Output CASS CASS Address Parse Tool Geocoder Suite US Street Geocode US Zip9 Coder Canada Geocoder Allocate Input Allocate Append Data Allocate MetaInfo Tool Allocate Report Read Behavior Profile Set Compare Behavior Behavior Detail Fields Behavior MetaInfo Cluster Code Create Behavior Profile Write Behavior Profile Set Profile Detail Report Profile Comparison Report Profile Rank Report Profile Input Profile Output Calgary Loader Calgary Input Calgary Join Calgary Cross Count Calgary Cross Count Append API Output Block Until Done Detour Detour End Dynamic Input Dynamic Rename Tool Dynamic Replace Dynamic Select Field Info Message Tool Run Command R Test Base64 Encoder JSON Parse Foursquare Gnip Input Twitter Search Datasift Generic Tool Blob Convert Blob Input Blob Output JSON Build Make Columns Throttle Action Check Box Condition Control Parameter Date Drop Down Error Message File Browse Folder Browse List Box Macro Input Macro Output Map Numeric Up Down Radio Button Test Box Tree Browse Data In-DB Connect In-DB Filter In-DB Formula In-DB Join In-DB Sample In-DB Select In-DB Data Stream In Data Stream Out Summarize In-DB Union In-DB Write In-DB Alteryx Tools!
  48. 48. # A L T E R Y X 1 9 48 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Correlationv.causation
  49. 49. Causation Correlation Rabbit Holes bizarre, confusing, or nonsensical situations from which it is difficult to extricate oneself. Misleading • False Positives • False Negatives Random Walks Movements or changes in a variable following no discernible pattern or trend. Nothing to see here! Blind Spots Areas where a person's view is obstructed. Wrong Hypothesis • Hidden Relationships Missing Data • Know your blind spots! Relationship Matrix of Dependent Variables (Targets) VERSUS Independent Variables (Drivers) Gold! Related Context. Meaningful relationships. Targeted objective of analytics Lead Lag Concurrent
  50. 50. # A L T E R Y X 1 9 50 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS AIandML
  51. 51. # A L T E R Y X 1 9 54 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS CycleofAdvancedAnalytics
  52. 52. Evolve Benchmark Feedback Monitor Decide Contextualize Report Decipher Analyze Model Data Prep Data Collect Data Tacticalize Strategize Inquire Envision 16 15 02 01 0314 0413 0512 0611 10 07 09 08 I. Planning II. AnalyticsIII. Decision-Making IV. Learning Competitive Analytics Cycle of Advanced Analytics 16 Functional Areas of Expertise (evolved from CRISP-DM) CRISP-DM
  53. 53. Evolve Benchmark Feedback Monitor Decide Contextualize Report Decipher Analyze Model Data Prep Data Collect Data Tacticalize Strategize Inquire Envision 16 15 02 01 0314 0413 0512 0611 10 07 09 08 I. Planning II. AnalyticsIII. Decision-Making IV. Learning Competitive Analytics Cycle of Advanced Analytics 16 Functional Areas of Expertise (evolved from CRISP-DM) CRISP-DM
  54. 54. # A L T E R Y X 1 9 57 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Data-AnalyticsTriangle
  55. 55. Data-Analytics Triangle Global Macro Region Micro CMA Project Nano Components
  56. 56. # A L T E R Y X 1 9 59 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS 3PhasesofAnalytics
  57. 57. analyze take apart synthesize put together actualize make reality
  58. 58. The five V’s of big data
  59. 59. # A L T E R Y X 1 9 63 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS 4typesofAnalytics
  60. 60. 4 Types of Analytics What will happen? And when? How can we make it happen? Why did it happen? What happened? Descriptive Analytics01 Diagnostic Analytics02 Predictive Analytics03 Prescriptive Analytics04
  61. 61. # A L T E R Y X 1 9 65 MASTERING PREDICTIVE ANALYTICS UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  62. 62. # A L T E R Y X 1 9 66 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS readings
  63. 63. MSBA 290: Mastering Predictive Analytics
  64. 64. # A L T E R Y X 1 9 69 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Quickquiz!
  65. 65. # A L T E R Y X 1 9 70 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS What makes a good forecast? 1. Unambiguous forecast statement 2. Concrete expiry date 3. Probabilistic (specific level of confidence) 4. Accurate Discrete Forecast (Yes or No) 5. Accurate Direction 6. Low Variance or Low MAPE (Mean Absolute Percent Error) 7. Relative score & rank among other forecasters 8. Insight to decision makers 9. Timing: Early forecast versus late forecast
  66. 66. # A L T E R Y X 1 9 71 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Which economist made the better forecast? Metric Bill Sue Consensus Dec 2017 Revenues $100,000,000 $100,000,000 $100,000,000 Dec 2018 Revenue Forecast $160,000,000 $98,000,000 $129,000,000 Dec 2018 Revenue Actual $115,000,000 $115,000,000 $115,000,000 $ Variance $45,000,000 -$17,000,000 $14,000,000 % Variance 39.13% -14.78% 12.17% Correct Direction Yes No Yes
  67. 67. # A L T E R Y X 1 9 72 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Ensemblemodeling
  68. 68. 15% Qualitative (Experience & Intuition) Operational knowledge and broad understanding of how all key dimensions and networks of organization, industry, competitors operate. 50% Hickam's Dictum Complex solutions are more likely to be correct than simpler ones. Advanced Analytics Machine Learning The Diversified Approach to Predictive Analytics by Competitive Analytics There is no singular-best approach to predictive analytics - which is why so many “experts” focusing on one approach will get “out-forecasted” by other approaches. The optimized approach is to combine all 4 meta-approaches: Qualitative, SME, Basic Modeling, and Advanced Analytics/Machine Learning. Essentially, leveraging a series of approaches, methodologies, and models (i.e. ensemble modeling) assures all possible inputs, influencing factors, assumptions, drivers, possibilities, parameters, technologies, weights, data sources, algorithms are enjoined and synergized into a Holistic Optimized Approach - which will produce the lowest absolute margin of error of any single approach. 15% SME (Subject Matter Experts) Deep and detailed knowledge of organization, industry, products, services, suppliers, value chain, customers, competitors. Complexity-Quantitative FocusedAttention (TheTrees) DiffuseAwareness (TheForest) 20% Occam's Razor Simpler solutions are more likely to be correct than complex ones. Basic Modeling • What-If Linear Regression • Multi-Linear Regression • Logistic Regression • Decision Tree • Random Forrest • Boosted Decision Trees • Support Vector Machines • Naive Bayes • K-Means Clustering • K-Nearest Neighbors • Hierarchical Clustering • Deep Neural Networks • Box-Jenkins • Econometrics • Extrapolation • Rule-based Forecasting • Expert Systems • Analogies • Role Playing • Intention Analysis • Conjoint Analysis • Expert Forecasting • Loglinear Models • PCA Simplicity-Qualitative Domain Knowledge Relationship Knowledge Applied Predictive Analytics Applied Mathematics Applied Economics Applied Statistics Data Science Driver/Variable Knowledge Anecdotal Knowledge Contextual Knowledge Nuanced Knowledge • Descriptive Analytics • Diagnostic Analytics • Interpretive Analytics • Inquisitive Analytics • Prescriptive Analytics • Optimization Analytics • Max/Min Analytics • Competitive Analytics • Goal Analytics Holistic Optimized Approach © 2019 Competitive Analytics. © 2019 DECIPHER. All rights reserved. • 620 Newport Center Drive, Suite 1100, Newport Beach, California 92660 • 714 660 2799  Office • www.CompetitiveAnalytics.com • Questions? Comments? Requests? Email us at info@CompetitiveAnalytics.com Examples • Delphi Method • Market Research • Panel Consensus • Visionary Forecast • Historical Analogy Examples • Linear Regression • Variable Trending • Descriptive Statistics • Moving Average • Extrapolation • Interpolation Examples • Experiential Forecasting • Leadership Forecasting • Consensus Forecasting • Judgmental Bootstrapping Example of 24 Models From Thousands of Potential Models
  69. 69. # A L T E R Y X 1 9 74 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS ForecastMethodologies
  70. 70. Qualitative • Human Judgement: Panel of Experts with Multipart Survey • Human Judgement: Systematic, Formal, Procedural Tests • Human Judgement: Panel of Experts Compiled Assumption • Human Judgement: Insight, Intuition, Prophecy, Imagination • Human Judgement: Basis on Historical Patterns Time Series • Moving average smoothing • Weighted moving averages • Simple Exponential Smoothing • Trend Methods • Holt-Winters' Seasonal Method • Innovations state space models for exponential smoothing • ETS Models (Error Trend Seasonality) • Exponential Smoothing - Horizontal • Exponential Smoothing - Trend • Exponential Smoothing - Trend / Seasonal • Random Walk Model • Autoregressive Models • Moving Average Models • Non-Seasonal ARIMA Models (AutoRegressive Integrated Moving Average) • Seasonal ARIMA Models (AutoRegressive Integrated Moving Average) • Automatic ARMA • Theoretical ARMA • X-11 Decomposition • Slope-Characteristic Method • Polynomial Method • Logarithmic Method Causal-Statistical Models, Regression Model • Linear Regression • Simple Linear Regression • Simple Linear Regression: Linear Regression and Pearson Correlation • Simple Linear Regression: Box-Cox Transformation for Simple Linear Regression • Simple Linear Regression: Robust Linear Regression (Passing-Bablok Median-Slope) • Multiple Regression • Multiple Regression: Principal Components Regression • Multiple Regression: Nondetects-Data Regression • Multiple Regression: Response Surface Regression • Multiple Regression: Ridge Regression • Multiple Regression: Robust Regression • Multiple Regression: Multiple Regression for Appraisal • Multiple Regression: Multiple Regression with Serial Correlation • Multiple Regression: Analysis of Covariance (ANCOVA) with Two Groups • Multiple Regression: One-Way Analysis of Covariance (ANCOVA) • Multiple Regression: General Linear Models (GLM) for Fixed Factors • Multiple Regression: Multiple Regression (Old Version) • Logistic Regression • Logistic Regression: Conditional Logistic Regression • Logistic Regression: Discriminant Analysis • Logistic Regression: Probit Analysis • Cox Regression • Cox Regression: Parametric Survival (Weibull) Regression • Cox Regression: Two-Sample Non- Inferiority Tests for Survival Data using Cox Regression • Cox Regression: Two-Sample Superiority by a Margin Tests for Survival Data using Cox Regression • Cox Regression: Two-Sample Equivalence Tests for Survival Data using Cox Regression • Cox Regression: Probit Analysis • Count Data: Poisson Regression • Count Data: Zero-Inflated Poisson Regression • Count Data: Negative Binomial Regression • Count Data: Zero-Inflated Negative Binomial Regression • Count Data: Geometric Regression • Method Comparison: Deming Regression • Method Comparison: Passing-Bablok Regression for Method Comparison • Nondetects-Data Regression • Nonlinear Regression • Nonlinear Regression: Curve Fitting - General • Nonlinear Regression: Michaelis- Menten Equation • Nonlinear Regression: Sum of Functions Models • Nonlinear Regression: Fractional Polynomial Regression • Nonlinear Regression: Ratio of Polynomials Fit - One Variable • Nonlinear Regression: Ratio of Polynomials Search - One Variable • Nonlinear Regression: Harmonic Regression • Nonlinear Regression: Ratio of Polynomials Fit - Many Variables • Nonlinear Regression: Ratio of Polynomials Search - Many Variables • Time Series Multiple Regression with Serial Correlation • Time Series Harmonic Regression Causal-Statistical Models, Econometric Models • Intention-to-Buy & Anticipations Surveys • Input-Output Model • Economic Input-Output Model • Diffusion Index • Leading Indicator • Life-Cycle Analysis • Vector Autoregression • Cointegration Causal-Statistical Models, Cluster Analysis • Fuzzy Clustering • Heirarchical Clustering / Dendrograms • K-Means Clustering • Medoid Partitioning • Regression Clustering • Clustered Heat Maps (Double Dendrograms) Causal-Statistical Models, Descriptive Statistics • Descriptive Statistics - Summary Tables • Descriptive Statistics - Summary Lists • Cluster Randomization - Create Cluster Means Dataset • Contingency Tables (Crosstabs / Chi- Square Test) • Frequency Tables • Box-Cox Transformation • Data Screening • Data Simulation • Grubbs' Outlier Test • Normality Tests • Area Under Curve • Circular Data Analysis • Tolerance Intervals Causal-Statistical Models, Distribution Fitting • Beta Distribution Fitting • Distribution (Weibull) Fitting • Gamma Distribution Fitting • Grubbs' Outlier Test • Normality Tests • Normal Probability Plots • Weibull Probability Plots • Chi-Square Probability Plots • Exponential Probability Plots • Gamma Probability Plots • Half-Normal Probability Plots • Log-Normal Probability Plots • Uniform Probability Plots • Probability Plot Comparison Causal-Statistical Models, Item/Meta Analysis • Item Analysis • Item Response Analysis • Meta-Analysis of Correlated Proportions • Meta-Analysis of Hazard Ratios • Meta-Analysis of Means • Meta-Analysis of Proportions Causal-Statistical Models, Multivariate Analysis • Factor Analysis • Principal Components Analysis • Canonical Correlation • Equality of Covariance • Discriminant Analysis • Hotelling's One-Sample T2 • Hotelling's Two-Sample T2 • Multivariate Analysis of Variance (MANOVA) • Correspondence Analysis • Loglinear Models • Multidimensional Scaling Causal-Statistical Models, Survey Data • Contingency Tables (Crosstabs / Chi- Square Test) • Frequency Tables • Cochran's Q Test • Descriptive Statistics - Summary Tables • Descriptive Statistics - Summary Lists • Cluster Randomization - Create Cluster Means Dataset • Cluster Randomization - Create Cluster Proportions Dataset • Cluster Randomization - Create Cluster Rates Dataset • Loglinear Models • Item Analysis • Data Screening Causal-Statistical Models, Survival Analysis • Cumulative Incidence • Kaplan-Meier Curves (Logrank Tests) • Life-Table Analysis • Cox Regression • Parametric Survival (Weibull) Regression • Two-Sample Non-Inferiority Tests for Survival Data using Cox Regression • Two-Sample Superiority by a Margin Tests for Survival Data using Cox Regression • Two-Sample Equivalence Tests for Survival Data using Cox Regression • Beta Distribution Fitting • Distribution (Weibull) Fitting • Gamma Distribution Fitting • Mantel-Haenszel Test • Probit Analysis • Tolerance Intervals • Cluster Randomization - Create Cluster Rates Dataset • Survival Parameter Conversion Tool • Time Calculator Causal Models-Machine Learning- Dimensionality Reduction • Canonical correlation analysis (CCA) • Factor analysis • Feature extraction • Feature selection • Independent component analysis (ICA) • Linear discriminant analysis (LDA) • Multidimensional scaling (MDS) • Non-negative matrix factorization (NMF) • Partial least squares regression (PLSR) • Principal component analysis (PCA) • Principal component regression (PCR) • Projection pursuit • Sammon mapping • t-distributed stochastic neighbor embedding (t-SNE) Causal Models, Machine Learning, Ensemble Learning • Bayes Optimal Classifier • Bayesian Parameter Averaging • Bayesian Model Combination • AdaBoost • Boosting • Bootstrap aggregating (Bagging) • Ensemble averaging • Gradient boosted decision tree (GBDT) • Gradient boosting machine (GBM) • Random Forest • Stacked Generalization (blending) • XGBoost • Bucket of Models • Stacking Causal Models, Machine Learning, Reinforcement Learning • Monte Carlo • State–action–reward–state– action (SARSA) • SARSA - Lambda • Temporal difference learning (TD) • Learning Automata • Q-learning • Q-learning - Lambda • Deep Q Network • Deep Deterministic Policy Gradient • Asynchronous Actor-Critic Algorithm • Q-Learning with Normalized Advantage Functions • Trust Region Policy Optimization • Proximal Policy Optimization Causal Models, Machine Learning, Supervised Learning • Averaged one-dependence estimators (AODE) • Artificial neural network • Apriori algorithm • Eclat algorithm • Case-based reasoning • Gaussian process regression • Gene expression programming • Group method of data handling (GMDH) • Inductive logic programming • Instance-based learning • Lazy learning • Learning Automata • Learning Vector Quantization • Logistic Model Tree • K Nearest Neighbor Algorithm • Analogical modeling • Probably approximately correct learning (PAC) learning • Ripple down rules, a knowledge acquisition methodology • Symbolic machine learning algorithms • Support vector machines • Random Forests • Ordinal classification • Information fuzzy networks (IFN) • Conditional Random Field • ANOVA • Quadratic classifiers • Boosting - SPRINT • Hidden Markov models • Hierarchical hidden Markov model • Bayesian knowledge base • Naive Bayes • Gaussian Naive Bayes • Multinomial Naive Bayes • Averaged One-Dependence Estimators (AODE) • Bayesian Belief Network (BBN) • Bayesian Network (BN) • Decision tree • Classification and regression tree (CART) • Iterative Dichotomiser 3 (ID3) • C4.5 algorithm • C5.0 algorithm • Chi-squared Automatic Interaction Detection (CHAID) • Decision stump • Conditional decision tree • SLIQ • Linear Classifier • Fisher's linear discriminant • Linear regression • Logistic regression • Multinomial logistic regression • Perceptron • Support vector machine Causal Models, Machine Learning, Unsupervised Learning • Expectation-maximization algorithm • Vector Quantization • Generative topographic map • Information bottleneck method • Feedforward neural network • Extreme learning machine • Convolutional neural network • Recurrent neural network • Long short-term memory (LSTM) • Logic learning machine • Self-organizing map • Association rule learning • FP-growth algorithm • Hierarchical clustering • Single-linkage clustering • Conceptual clustering • Cluster analysis • BIRCH • DBSCAN • Expectation-maximization (EM) • K-medians • Locally Weighted Learning (LWL) • Mean-shift • OPTICS algorithm • Anomaly detection • Local outlier factor Causal Models, Machine Learning, Semi-Supervised Learning • Active learning • Generative models • Low-density separation • Graph-based methods • Co-training • Transduction Causal Models, Machine Learning, Deep Learning • Deep belief networks • Deep Convolutional neural networks • Deep Recurrent neural networks • Hierarchical temporal memory • Generative Adversarial Networks • Deep Boltzmann Machine (DBM) • Stacked Auto-Encoders © 2019 Competitive Analytics. © 2019 DECIPHER. All rights reserved. • 620 Newport Center Drive, Suite 1100, Newport Beach, California 92660 • 714 660 2799  Office • www.CompetitiveAnalytics.com • Questions? Comments? Requests? Email us at info@CompetitiveAnalytics.com Non-Inclusive List of Predictive Analytics Methodologies
  71. 71. Participants Sufficient objective data Good knowledge of relationships Large changes expected Expertise expensive or repetitive forecasts Conflict among a few decision makers Policy analysis Best source Similar cases exist Type of dataLarge changes expected Policy analysis Good domain knowledge Expert Forecasting Judgmental Bootstrapping Conjoint Analysis IntentionsRole Playing Analogies Expert Systems Rule-based Forecasting Extrapolation Econometric Method Different methods provide useful forecasts Combine forecasts No (Judgmental) Yes (Quantitative) NoYes Yes No Selection Tree for Forecasting Methods by J. Scott Armstrong NoYes NoYes Cross - SectionTime Series NoYes YesNo Yes No Yes No No Yes Experts Use best method Yes No
  72. 72. Super Fast Review of 10 Methods
  73. 73. Expert Forecasting The use of subject expert(s) opinion to forecast future outcomes.
  74. 74. Judgmental Bootstrapping Making a model of an expert’s prediction process by running a regression of his forecasts and the inputs he used.
  75. 75. Conjoint Analysis A survey-based method of measuring how people value different attributes when asked to make trade-offs among conflicting considerations (e.g. quality and price).
  76. 76. Conjoint Analysis? 50¢ $24
  77. 77. Intentions/Expectations Using probability scales that measure individual’s future behavior, plans, goals, or expectations about what they will do in the future.
  78. 78. Role Playing A way of predicting the decisions by people or groups engaged in conflicts, by observing the actions and reactions of those playing a role.
  79. 79. Analogies Consideration of similar situation’s time series to forecast the future outcome of a given situation.
  80. 80. Expert Systems Use of rules to represent experts’ reasoning in solving problems, based on knowledge about methods and the problem domain.
  81. 81. Rule-Based Forecasting A type of expert system that uses forecasting expertise to create a set of rules, using domain knowledge and the characteristics of the data to produce a forecast from a combination of simple extrapolation methods.
  82. 82. Extrapolation The basic assumption of extrapolation is that the variable will continue in the future as it has behaved in the past, so this methodology uses time-series or cross-sectional data to extrapolate the behavior of future data.
  83. 83. Econometric Modeling Econometric methods rely on statistical procedures, including multivariate and regression analysis, and estimate relationships for models specified on the basis of theory, prior studies, and domain knowledge.
  84. 84. # A L T E R Y X 1 9 90 CAPSTONE ANALYTICS UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  85. 85. # A L T E R Y X 1 9 91 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS P E O P L E A N A LY T I C S H O W T O M O R E E F F E C T I V E LY S C O R E A N D R A N K P E O P L E ’ S S K I L L S , E X P E R T I S E , A N D E X P E R I E N C E B Y A P P LY I N G N E W D ATA T O O L S , M L A L G O R I T H M S , A N D A D VA N C E D A N A LY T I C S M E T H O D O L O G I E S B Y U T I L I Z I N G T H E I R R E S U M E I N F O R M AT I O N A N D O T H E R D ATA S O U R C E S .
  86. 86. # A L T E R Y X 1 9 92 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS • People analytics, also known as talent analytics or HR analytics, refers to the method of analytics that can help managers and executives make decisions about their employees or workforce. • People analytics applies statistics, technology and expertise to large sets of talent data, which results in making better management and business decisions for an organization. • People analytics is a new domain for most HR departments. • Companies are looking to better drive the return on their investments in people. The old approaches of gut feel is no longer sufficient. • What Are the Benefits of People Analytics? • People analytics helps organizations to make smarter, more strategic and more informed talent decisions. With people analytic, organizations can find better applicants, make smarter hiring decisions, and increase employee performance and retention. • Cornerstone’s suite of people analytics products apply sophisticated data science and machine learning to help organizations more efficiently and effectively manage their people. Cornerstone’s analytics suite give organizations options for viewing, understanding and acting on talent data across the entire employee lifecycle. This includes Cornerstone View, an interactive data visualization application that gives business leaders deeper intelligence about their people, Cornerstone Planning, an intuitive workforce planning application that helps organizations easily create, manage and execute accurate hiring plans over multiple time horizons, as well as Cornerstone Insights, its predictive and prescriptive analytics solution that equips business leaders with the intelligence to better recruit, train, manage and develop their people. W H AT I S P E O P L E A N A LY T I C S ?
  87. 87. # A L T E R Y X 1 9 93 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Resume Rank For HR Directors & Recruiters Ranking, Scoring, Salary-Value Estimator
  88. 88. # A L T E R Y X 1 9 94 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Workforce Evolution For City, County, State Planning & Economic Development
  89. 89. # A L T E R Y X 1 9 95 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS Reverse Resume Rank For Job Seekers
  90. 90. # A L T E R Y X 1 9 96 FIVE KEY POINTS 1 2 3 4 5 Students “Experience” Alteryx Students “Use” Alteryx holistically Students “See” Alteryx in real world Students “Build” working models Students “Present & Defend” models HOW TO BUILD SCENARIO-BASED REVENUE & EXPENSE FORECASTS WITH ALTERYX PREDICTIVE TOOLS: ACHIEVING ACCURACY, RELIABILITY, AND USABILITY
  91. 91. BEFORE YOU LEAVE ATTENTION 97 • B E F O R E YO U L E AV E …
 Please take a moment to complete your evaluation survey. Hand it to the room monitors on your way out.
  92. 92. # A L T E R Y X 1 9 98 UCI'S MASTER’S OF ANALYTICS PROGRAM + ALTERYX = BIG ADVANTAGE FOR STUDENTS PREPARING STUDENTS AND JUNIOR STAFF FOR SUCCESS
  93. 93. # A L T E R Y X 1 9 THANK YOU 99 David Savlowitz CEO & Founder, Competitive Analytics, Professor of Predictive Analytics, UC Irvine dss@competitiveanalytics.com Michael Ponton Director of Analytics, Competitive Analytics, Professor of Predictive Analytics, UC Irvine mp@competitiveanalytics.com HOW TO BUILD SCENARIO-BASED REVENUE & EXPENSE FORECASTS WITH ALTERYX PREDICTIVE TOOLS: ACHIEVING ACCURACY, RELIABILITY, AND USABILITY

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