Data Science Techniques in Business Intelligence and Analytics
Explore various data science tasks and techniques in business intelligence and analytics, including co-occurrences, associations, complexity control, surprise measurement, profiling, and link prediction. Learn how these methods help in understanding patterns, behaviors, and connections within data to drive better business decisions.
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Business Intelligence and Analytics OTHER DATA SCIENCE TASKS AND TECHNIQUES Session 12
Co-occurrences and Associations Complexitycontrol: Supportofassociation Let s saythatwerequirerulestoapplytoatleast0.01%ofall transactions Confidenceorstrengthoftherule Let s say that we requirethat5%ormoreofthetime,abuyerofAalso buysB Measuringsurprise:
Example: MIE and TELUR Weoperateasmallconveniencestorewherepeoplebuygroceries, liquor,lotterytickets,etc.Weestimatethat: 30%ofalltransactionsinvolveMIE, 40%ofalltransactionsinvolveTelur , and20%ofthetransactionsincludebothmieandtelur.
Example: Mie and Telur If the two products are unrelated: Otherwise: Support (mie,telur)=20 % Strength(mie,telur)=p(telur|mie)=67%
Profiling: Finding Typical Behavior Profilingattemptstocharacterizethetypicalbehaviorofan individual,group,orpopulation Profilingcanessentiallyinvolveclustering,iftherearesubgroupsof thepopulationwithdifferentbehaviors
Profiling Source: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking.
Profiling Source: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking.
Profiling Source: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking.
Prof Source: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking.
Link Prediction and Social Recommendation Sometimes,insteadofpredictingaproperty(targetvalue)ofadata item,itismoreusefultopredictconnectionsbetweendataitems Acommonexampleofthisispredictingthatalinkshouldexist betweentwoindividuals Linkpredictioncanalsoestimatethestrengthofalink
Data Reduction and Latent Information Trade-offbetweentheinsightormanageabilitygainedagainstthe informationlost
P . Adamopoulos New York University Source: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking.
Latent Information and Movie Recommendation Source: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking.
Bias, Variance, and Ensemble Methods The errors a model makes can be characterized by three factors: 1. Inherent randomness, 2. Bias, and 3. Variance.
References Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining and Data- Analytic Thinking. O Reilly, CA 95472, 2013. Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009 Eibe Frank, Mark A. Hall, and Ian H. Witten : The Weka Workbench, M Morgan Kaufman Elsevier, 2016. Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017 Sharda, R., Delen, D., Turban, E., (2018). Business intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson.
