Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD Record, 22(2), 207–216. https://doi.org/10.1145/170035.170072
Ashenfelter, O., Ashmore, D., & Lalonde, R. (n.d.). Bordeaux Wine Vintage Quality and the Weather. http://www.liquidasset.com/orley.htm
Berlinger, E., Illés, F., Badics, M., Banai, Á., Daróczi, G., Dömötör, B., Gabler, G., Havran, D., Juhász, P., Margitai, I., Márkus, B., Medvegyev, P., Molnár, J., Árpád Szűcs, B., Tuza, Á., Vadász, T., Váradi, K., & Vidovics-Dancs, Á. (2015). Mastering R for Quantitative Finance. Packt Publishing. https://www.safaribooksonline.com/library/view/mastering-r-for/9781783552078/
Bertolucci, J. (2014, September 17). Data Scientists Want Big Data Ethics Standards | InformationWeek. http://www.informationweek.com/big-data/big-data-analytics/data-scientists-want-big-data-ethics-standards/d/d-id/1315798
Best, J. (2013, September 9). IBM Watson: The Inside Story of How the Jeopardy-Winning Supercomputer Was Born, and What It Wants to Do Next | TechRepublic. http://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/
Brynjolfsson, E. (2011). Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1819486
Buytendijk, F., & Heiser, J. (2013). Confronting the Privacy and Ethical Risks of Big Data. Financial Times. https://www.ft.com/content/105e30a4-2549-11e3-b349-00144feab7de
Chapman, P. (2000). CRISP-DM 1.0: Step-by-Step Data Mining Guide. SPSS. https://the-modeling-agency.com/crisp-dm.pdf
Cukier, K. N., & Mayer-Schoenberger, V. (2013). Why Big Data Is on the Rise | Foreign Affairs. https://www.foreignaffairs.com/articles/2013-04-03/rise-big-data
Dallaway, E. (2015a). Ten Data-Driven Sporting Victories - Part One | Technology. The Guardian. http://www.theguardian.com/technology/2015/mar/16/ten-data-driven-sporting-victories-part-one
Dallaway, E. (2015b). Ten Data-Driven Sporting Victories - Part Two | Technology. The Guardian. http://www.theguardian.com/technology/2015/mar/16/ten-data-driven-sporting-victories-part-two
Daróczi, G., Vidovics-Dancs, A., Havran, D., Berlinger, E., Michaletzky, M., Puhle, M., Csóka, P., Váradi, K., & Tulassay, Z. (2013). Introduction to R for Quantitative Finance. Packt Publishing. https://www.safaribooksonline.com/library/view/introduction-to-r/9781783280933/
Datasets for Data Mining and Data Science. (n.d.). http://www.kdnuggets.com/datasets/index.html
Davenport, T. (2007). The Dark Side of Customer Analytics. Harvard Business Review. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=24635693&site=ehost-live
de Bree, T. (n.d.). 8 Questions Every Business Analyst Should Ask | Modern Analyst. http://www.modernanalyst.com/Resources/Articles/tabid/115/ID/179/8-Questions-Every-Business-Analyst-Should-Ask.aspx
DeAsi, G. (n.d.). How to Use Customer Behavior Data to Drive Revenue (Like Amazon, Netflix & Google) | Pointillist. https://web.archive.org/web/20221020180045/https://www.pointillist.com/blog/customer-behavior-data/
Decision Support Systems Resources | DSSResources. (n.d.). http://dssresources.com/
Deloitte and Ibm, We’re Facing a Huge Uk Digital Skills Gap - Infographic | Ignite. (n.d.). https://www.ignite.digital/uk-digital-skills-gap-infographic/
Egger, D. (n.d.). Business Metrics for Data-Driven Companies: 20-Item Checklist | Onyx Reporting. https://web.archive.org/web/20210119104435/http://www.onyxreporting.com/uploads/4/0/8/5/40851971/20itemchecklist.pdf
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD Process for Extracting Useful Knowledge From Volumes of Data (Knowledge Discovery in Databases). Communications of the ACM, 39(11), 27–34. https://doi.org/10.1145/240455.240464
Gerber, A. S., Green, D. P., & Larimer, C. W. (2008). Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment. The American Political Science Review, 102(1), 33–48. https://www.jstor.org/stable/27644496
Go, A., Bhayani, R., & Huang, L. (n.d.). Twitter Sentiment Classification using Distant Supervision. http://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf
Goldfarb, A., & Tucker, C. E. (2011). Privacy Regulation and Online Advertising. Management Science, 57(1), 57–71. https://doi.org/10.1287/mnsc.1100.1246
Goodwin, K. (2013, January 8). A Stakeholder Interview Checklist | Boxes and Arrows. http://boxesandarrows.com/a-stakeholder-interview-checklist/
Grace-Martin, K. (n.d.). Seven Ways to Make up Data: Common Methods to Imputing Missing Data | The Analysis Factor. https://www.theanalysisfactor.com/seven-ways-to-make-up-data-common-methods-to-imputing-missing-data/
Hahsler, M., Grun, B., Hornik, K., & Buchta, C. (n.d.). Introduction to Arules – A Computational Environment for Mining Association Rules and Frequent Item Sets. https://cran.r-project.org/web/packages/arules/vignettes/arules.pdf
Hays, C. L. (2004). What Wal-Mart Knows About Customers’ Habits. The New York Times. http://www.nytimes.com/2004/11/14/business/yourmoney/what-walmart-knows-about-customers-habits.html?_r=1
Hetherington, R. (2015). Preventing Customer Churn With Better Data Analytics. https://www.digitalistmag.com/industries/banking/2015/06/23/preventing-customer-churn-with-better-data-analytics-3-02974982
How to Become a Data Scientist (Part 1/3) – Towards Data Science A Medium. (n.d.). https://medium.com/towards-data-science/how-to-become-a-data-scientist-part-1-3-8706a62b809e?imm_mid=0f59d0&cmp=em-data-na-na-newsltr_20170823
How to Use Customer Behavior Data to Drive Revenue (Like Amazon, Netflix & Google). (n.d.). https://www.pointillist.com/blog/customer-behavior-data/
How to Use read.csv() to Import Data in R. (n.d.). http://www.dummies.com/programming/r/how-to-use-read-csv-to-import-data-in-r/
Hype Cycle for Business Intelligence and Analytics, 2016. (n.d.). https://www.gartner.com/document/code/290879?ref=grbody&refval=3574217
Hype Cycle for Data Science, 2016. (n.d.). https://www.gartner.com/document/code/303293?ref=grbody&refval=3574217
IBM Big Data and Analytics - Case Studies - United Kingdom. (2015). IBM Corporation. https://web.archive.org/web/20150221100253/http://www.ibm.com/big-data/uk/en/big-data-and-analytics/case-studies.html
IBM Cognos Analytics on Cloud - United Kingdom. (n.d.). https://web.archive.org/web/20210604092138/https://www.ibm.com/uk-en/products/cognos-analytics
IBM’s Watson Computer Plays Jeopardy!!! | YouTube. (2013). https://www.youtube.com/watch?v=P18EdAKuC1U
ITScore for BI and Analytics. (n.d.). https://www.gartner.com/document/code/314086?ref=grbody&refval=3574217
Jain, D., & Gautam, S. (n.d.). Implementation of Apriori Algorithm in Health Care Sector: A Survey. International Journal of Computer Science and Communication Engineering, 2(4), 26–32.
Jeet, P., & Vats, P. (2017). Learning Quantitative Finance with R. Packt Publishing. https://www.safaribooksonline.com/library/view/learning-quantitative-finance/9781786462411/
Kenny, G. (2014). Five Questions to Identify Key Stakeholders. Harvard Business Review. https://hbr.org/2014/03/five-questions-to-identify-key-stakeholders
Lee, L., & Pang, B. (n.d.). Opinion Mining and Sentiment Analysis [open access]. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf
Lehmann, J., & Joseph, S. (2009). Biochar for Environmental Management: Science and Technology. Earthscan.
Lewis, M. (2004a). Moneyball: The Art of Winning an Unfair Game. W.W. Norton & Company.
Lewis, M. (2004b). Moneyball: The Art of Winning an Unfair Game. W.W. Norton & Company.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011, May 1). Big Data: The Next Frontier for Innovation, Competition, and Productivity | McKinsey & Company. https://web.archive.org/web/20200606014002/https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation
Mason, H. (2010, September 25). A Taxonomy of Data Science | Dataists. https://web.archive.org/web/20210728133552/http://www.dataists.com/2010/09/a-taxonomy-of-data-science/
Miller, B. (2011). Moneyball. Columbia Pictures.
Moro, S., Cortez, P., & Rita, P. (2014). A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, 62, 22–31. https://doi.org/10.1016/j.dss.2014.03.001
Pious, K. (2013, June 20). Interacting With Stakeholders as a Business Analyst: Who Are You Dealing With? | Captech Consulting. https://web.archive.org/web/20151015043859/https://www.captechconsulting.com/blogs/interacting-with-stakeholders-as-a-business-analyst-who-are-you-dealing-with
Provost, F., & Fawcett, T. (2013a). Data Science for Business. O’Reilly. https://ebookcentral.proquest.com/lib/rhul/detail.action?docID=1323973
Provost, F., & Fawcett, T. (2013b). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013c). Data Science for Business. O’Reilly. https://ebookcentral.proquest.com/lib/rhul/detail.action?docID=1323973
Provost, F., & Fawcett, T. (2013d). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013e). Data Science for Business. O’Reilly. https://ebookcentral.proquest.com/lib/rhul/detail.action?docID=1323973
Provost, F., & Fawcett, T. (2013f). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013g). Data Science for Business. O’Reilly. https://ebookcentral.proquest.com/lib/rhul/detail.action?docID=1323973
Provost, F., & Fawcett, T. (2013h). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013i). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013j). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013k). Data Science for Business. O’Reilly.
Provost, F., & Fawcett, T. (2013l). Data Science for Business. O’Reilly.
Sharda, R. (2014a). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition). Pearson.
Sharda, R. (2014b). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition). Pearson.
Sharda, R., Delen, D., & Turban, E. (2014). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition). Pearson.
Sharma, A. S. (2013, March 5). Stakeholder Analysis and Management | ExpertBA. https://web.archive.org/web/20161028033209/http://expertbusinessanalyst.com/stakeholder-analysis-and-management/
Sherman, R. (2014). Business Intelligence Guidebook: From Data Integration to Analytics. Morgan Kaufmann.
Sherman, R. (2015). Business Intelligence Guidebook: From Data Integration to Analytics. Elsevier/Morgan Kaufmann. https://ebookcentral.proquest.com/lib/rhul/detail.action?docID=1832704
SIGKDD. (n.d.). http://kdd.org/
TDWI | Advancing All Things Data | Business Intelligence, Data Warehousing, Analytics | Education & Research. (n.d.). https://tdwi.org/Home.aspx
Teradata University Network. (n.d.). http://www.teradatauniversitynetwork.com/
The Case for Data Ethics | Accenture Outlook. (n.d.). https://www.accenture.com/gb-en/insight-outlook-case-data-ethics
Top 10 Strategic Technology Trends for 2017: Artificial Intelligence and Advanced Machine Learning. (n.d.). https://www.gartner.com/document/code/319573?ref=grbody&refval=3645332
UCI Machine Learning Repository: Data Sets. (n.d.). https://archive.ics.uci.edu/ml/datasets.php
UK Government Statistics. (n.d.). https://www.gov.uk/government/statistics
U.S. Data and Statistics | USA.gov. (n.d.). https://www.usa.gov/statistics
Watson and the Jeopardy! Challenge. (2014). https://www.youtube.com/watch?v=_Xcmh1LQB9I
Webinars and Videos On Demand. (n.d.). https://www.rstudio.com/resources/webinars/?mkt_tok=eyJpIjoiWVdNNFltTXlaREUxWlRVMyIsInQiOiJlZ0NHNkIzc0tPTTNldzFmaHNFaU5YOGJFcEVSZU81NWpxYThOb0ZFZGVIWjFaSTc1cFFtZzQ0cWxNbU9MMW1seFFKSGZ2aXFjZ1pSRGs5UFRnYkt2Wko1a1lcLzNcL1hFblZxRkdOWXpGTVF3PSJ9
Welcome! | Score a Hit! (n.d.). https://web.archive.org/web/20160704080559/http://www.scoreahit.com/
Wick, A. (2012, October 1). Six Effective Elicitation Questions to Ask Your Stakeholders | BA Times. http://www.batimes.com/articles/six-effective-elicitation-questions-to-ask-your-stakeholders.html
Zhang, C., & Zhang, S. (n.d.). Association Rule Mining. Springer Berlin Heidelberg.
Zumel, N., & Mount, J. (2014a). Practical Data Science With R. Manning. https://www.safaribooksonline.com/library/view/-/9781617291562/?ar
Zumel, N., & Mount, J. (2014b). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014c). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014d). Practical Data Science With R. Manning. https://www.safaribooksonline.com/library/view/-/9781617291562/?ar
Zumel, N., & Mount, J. (2014e). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014f). Practical Data Science With R. Manning. https://www.safaribooksonline.com/library/view/-/9781617291562/?ar
Zumel, N., & Mount, J. (2014g). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014h). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014i). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014j). Practical Data Science With R. Manning Publications Co.
Zumel, N., & Mount, J. (2014k). Practical Data Science With R. Manning Publications Co.