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Analytics in Action - Introduction
This document provides an overview of a business analytics course. It includes the following key points: - The objective of the course is to build students' knowledge of applying business analytics in various industrial settings. - The course will cover fundamentals, case methodology, and involve analyzing case studies from industries like community management, education, and health analytics. - Grading will be based on participation, a mid-term exam, and a final essay exam. Students will analyze case studies and apply appropriate data science techniques.
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Analytics in Action - Introduction
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- 2. Introduction ©2016 L. SCHLENKER Agenda Introduction AdministrativeDetails The Fundamentals Case Methodology
- 3. Course Portal ; http://DSign4.education ©2017Business Analytics Institute Introduction The objective of this course is to build the students’ knowledge of the practice of Business Analytics in a variety of industrial settings
- 4. Module Facilitator • Iwork with managers to help them understand how enterprise applications, web and mobile technologies can enrich their careers. • The client portfolio in the ICT industry includes Microsoft, Apple, Ernst & Young, France Telecom, HP, IBM, Oracle and SAP . •The work with the IT industry in Europe has included fifty partner and customer conferences, a dozen case studies, and various marketing support activities. Prof. Lee SCHLENKER, The Business Analytics Institute Mail : lee@lhstech.com Skype : leeschlenker Web : www.leeschlenker.com Introduction
- 5. • How doesthe author define the “Fourth Industrial Revolution”?* • The concept of looking “outside-in” suggests that we must understand the shifting business context affects our work, our careers and our business. Give at least one example. • What are digital natives and how do they look at business differently? • How are values changing in a digitally intermediated world? A Fourth Industrial Revolution ? Introduction Schwab, K. (2017), The Fourth Industrial Revolution
- 6. 6©2016 LHST sarl •Analyze the context of each case to document the key processes of the organization or the market • Qualify the data at hand to understand the nature of the business challenges • Apply the appropriate methodologies in your predictive and prescriptive analyses, and • Integrate elements of visual communications in transforming the data into a call for collective action In this module , you will www.dsign4.education Administrative Details
- 7. 7 Analytics is allabout making sense of the data ©2016 LHST sarl 04 Feb 14.00 - 18.15Introduction 05 Feb 08.30 - 16.30Community Management 06 Feb 08.30 - 16.30Education, Financial Services 07 Feb 08.30 - 16.30Health Analytics, Public Service 08 Feb 08.30 - 18.15Privacy 09 Feb 10.15 – 13.30 Human Resource Management 10 Feb 08.30 – 11.45 Wrap Up and Final Exam Administrative Details
- 8. Grading Scale Participation: 20%of your grade will be based upon your case study presentation, as well as your presentation in class Mi-term exam: 30% of your grade will be based upon your results on the multiple- choice exam. Final exam: 50% of your grade will be based upon on the quality of your final essay • What is the organization’s business model? • Why does the organization focus on data? • How can you apply the AI Roadmap to a specific challenge in this industry? • Which data science techniques does the organization favor ? • What is the link between data science and decision making? Administrative Details https://www.dsign4.education/case_studies
- 9. • Data isnothing more than answers waiting for you to ask the right question • Behind one door I’ve hidden the key to a great career, and a beer behind the other two • You can choose any door • To lend you a hand, I will show you one bottle of beer. • What are your chances of finding the key? What do we mean by a better decision?
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- 11. Analytics is theuse of data, methods, analysis and technology to help managers make better decisions. 1-11 Fundamentals psychological models data mining cognitive science decision theory information theory databases Business Analytics neuroscience statistics evolutionary models control theory Data science is the study of the generalizable extraction of knowledge from data
- 12. • Management isall about taking better decisions • What do better decisions mean (faster, more impressive, more precise) ? • Is it observable – how is something more precise answer to a problem? • The challenge is deciding what we want to measure Lewis Mumford, Technics and Civilization Decision Making ©2019 L. SCHLENKER
- 13. What is AI? MachineLearning Artificial Intelligence Nature Knowledge Intelligence Vision Self-learning Algorithms Mimic human Behavior Use Scenario Learns from data Solve Complex Problem Aim Sufficient Solution Optimal Solution Evaluation Metrics Accuracy Success Fundamentals
- 14. What does bettermean? • Rule-based automation • Produce probabilistic predictions based on the data • Combine prediction with judgement to learn • Generate art and design • Extract and quantify emotional states • Develop mental constructs • Adapt to environmental needs The Machine Intelligence Continuum Fundamentals
- 15. • Scan thecontext • Qualify the data at hand • Choose the right method • Transform data into action The Business Analytics Institute https://baieurope.com Fundamentals
- 16. Analytics • Logic andStatistics • Programming and Database • Trade knowledge • Data Storytelling davidpritchard.org
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- 18. Data Science Challenges Datapreparation is by far the most time-consuming part of Data Science, but case studies rarely address this Fundamentals (1)Data Quality (2)Feature Extraction (3)Machine Learning (4)Data Storytelling (5)Productizing Chandan Rajah
- 19. Case Groups Case Study Group1 Community Management Group 2 Education Group 3 Financial Services Group 4 Health Analytics Group 5 Public Service Group 6 Transportation Group 7 Data Protection
- 20. Case Methodology Case Study •What is the organization’s business model? • Why does the organization focus on data? • How can you apply the AI Roadmap to a specific challenge in this industry? • Which data science techniques does the organization favor ? • What is the link between data science and decision making?
- 21. • Carr, N.The World Wide Cage • Anderson L. and Wladawsky-Berger, L. The 4 Things It Takes to Succeed in the Digital Economy • Pine, B. and Gilmore, J. (1999). The Experience Economy. St. Paul, Minn.: HighBridge Co. • Schlenker L., (2018), As a manager in 2019, what will you really need to know about AI? • Schwab, K. (2017), The Fourth Industrial Revolution Bibliography Next Steps
Editor's Notes
- #14 Judgement : How much is it worth to respond quickly? How costly is it to not respond if it turns out that there was an intruder in the home?
- #19 Definition error Capture error Measurement error Coverage error Sampling errors Inference errors Unknown errors Undefined Goals Failing to pin down the reason for collecting data Definition Error - Unfortunately, even a simple goal like this will require fleshing out a number of assumptions before you can get the information that you want. Capture Error - Once you’ve identified the type of data that you wish to collect, you’ll need to design a mechanism to capture it. Measurement errors occur when the software or hardware that you use to capture data goes awry, either failing to capture usable data or producing spurious data. Processing Error – Coverage error describes what happens with survey data when there is insufficient opportunity for all targeted respondents to participate. Sampling errors occur when you analyze data from a smaller sample that is not representative of your target population. This is unavoidable when data only exists for some groups Inference errors are made by statistical or machine learning models when they make incorrect predictions from the available ground truth. unknown unknowns about the universe that leave a gap between your representation of reality, in the form of data, and reality itself