Uploaded byLee Schlenker
Introduction
This document provides an introduction to a course on business analytics. It outlines the course objectives to build students' knowledge of applying analytics in various industry settings. It discusses administrative details like the grading structure and course schedule. It also introduces fundamental concepts in data science and analytics, including common techniques. The document describes the case study methodology that will be used, involving analyzing organizations' data-driven business models and decision-making processes.
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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, Professor ESC Pau Mail : lee@lhstech.com Skype : leeschlenker Web : www.leeschlenker.com Introduction
- 5. • Management isabout taking decision • Improving decision making through the study of digital economics, managerial decision making, machine learning and data storytelling • Partnership with SDMIMD to promote analytics in management education http://baieurope.com lee@baieurope.com @DSign4Analytics Skype : leeschlenker ©2017 Business Analytics Institute Introduction
- 6. This a placewhere managers and students of management can discuss and debate best practises in the digital economy, new developments in data science and decision making. Ask questions and get practicable answers, and learn how to use data in decision making. Analytics for Management https://www.linkedin.com/ groups/13536539 Introduction
- 7. • 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
- 8. 8©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
- 9. 9 Analytics is allabout making sense of the data ©2016 LHST sarl Day 1 Introduction Day 2 Digital Economics Day 3 Community Management Day 4 Education Day 5 Financial Services Day 6 Health Analytics Day 7 Public Service Day 8 Privacy and Data Protection Day 9 Visual CVs - Employment Day 10 Wrap Up and Final Exam Administrative Details
- 10. Grading Scale Participation: 50%of your grade will be based upon your participation and engagement in class. Final exam: 50% of your grade will be based upon your results on the final multiple choice exam. • What is the organization’s business model? • Why does the organization focus on data? • Which data science techniques does the organization favor ? • What is the link between data science and decision making? • How is the Data Science team organized? • How does the organization use Data Science to propel growth? Administrative Details
- 11. Decision Trees Supervised Categorical It’ssunny, hot, normaly humid, and windy – should I play tennis?
- 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 ©2016 L. SCHLENKER
- 13. Analytics is theuse of data, methods, analysis and technology to help managers make better decisions. 1-13 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
- 14. • More datahas been created in the past two years than in the previous history of the human race • « Strategists still confuse technology with purpose … instead of garnering context and empathy to inform change…” - Brian Solis • We have more and more data – but does this lead to better decisions? Data Explosion 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 Privacy and Data Protection Group 7 Visual CVs - Employment
- 20. • What isthe organization’s business model? • Why does the organization focus on data? • How is the Data Science team organized? • Which data science techniques does the organization favor ? • What is the link between data science and decision making? • How does the organization use Data Science to propel growth Case Methodology Case Study
- 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., (2017), Digital Economics • Schwab, K. (2017), The Fourth Industrial Revolution Bibliography Next Steps