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  • Jeff Wu. Statistics = Data Science?, http://www2.isye.gatech.edu/~jeffwu/presentations/datascience.pdf,
  • Samantha Renae. Data analytics: Crunching the future, Bloomberg Businessweek,
  • Molly Galetto. Top 50 Data Science Resources, http://www.ngdata.com/top-data-science-resources/?,
  • Gil Press. A Very Short History Of Data Science, http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#61ae3ebb69fd,
  • WIRED. How Europe Can Seize the Starring Role in Big Data, www.wired.com/insights/2014/09/europe-big-data/,
  • Claire Cain Miller. Data Science: The Numbers of Our Lives, New York Times, http://www.nytimes.com/2013/04/14/education/edlife/universities-offer-courses-in-a-hot-new-field-data-science.html?pagewanted=all&_r=0,
  • Nathan Yau. Rise of the Data Scientist, http://flowingdata.com/2009/06/04/rise-of-the-data-scientist/,
  • Irv Lustig and Brenda Dietrich and Christer Johnson and Christopher Dziekan. The Analytics Journey, Analytics, http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey,
  • R. W. Dai and J. Wang and J. Tian. Metasynthesis of Intelligent Systems, Zhejiang Sci. Technol. Press Hangzhou China,
  • X. S. Qian. Building Systematism, ShanXi Sci. Technol Press Taiyuan China,
  • Longbing Cao. Combined Mining: Analyzing Object and Pattern Relations for Discovering and Constructing Complex but Actionable Patterns, WIREs Data Mining and Knowledge Discovery, 3(2): 140--155,
  • Longbing Cao and Ruwei Dai and Mengchu Zhou. Metasynthesis: M-Space M-Interaction and M-Computing for open complex giant systems, IEEE Trans. On Systems Man and Cybernetics--Part A, 39(5): 1007--1021,
  • Longbing Cao and Ruwei Dai. Open Complex Intelligent Systems, Post & Telecom Press,
  • Longbing Cao. Non-IIDness learning in behavioral and social data, The Computer Journal, 57(9): 1358--1370,
  • Longbing Cao and Philip S Yu and Chengqi Zhang and Yanchang Zhao. Domain Driven Data Mining, Springer,
  • Thomas R. Stewart and Jr. Claude McMillan. Descriptive and prescriptive models for judgment and decision making: Implications for knowledge engineering, 305--320, Expert Judgment and Expert Systems, Springer-Verlag, 1987.
  • Torsten Priebe and Stefan Markus. Business information modeling: A methodology for data-intensive projects data science and big data governance, 2056--2065, 2015 IEEE International Conference on Big Data (Big Data), 2015.
  • Tokuro Matsuo and Toshikazu Fukushima and Hidekazu Iwamoto. A Challenging of Data Science in Association Research for Convention Management, 458--461, Proceedings of the 2015 IIAI 4th International Congress on Advanced Applied Informatics, IEEE Computer Society, http://dx.doi.org/10.1109/IIAI-AAI.2015.232, 2015.
  • O. Alter and P. Brown and D. Botstein. Singular value decomposition for genome wide expression data processing and modeling, Proceedings of the National Academy of Sciences (PNAS), 97(18): 10101-6, 2000.
  • Lucy Suchma. Human-Machine Reconfigurations: Plans and Situated Actions, Cambridge University Press, 2006.
  • L. Chen W. Zeng and Q. Yuan. A unified framework for recommending items groups and friends in social media environment via mutual resource fusion, Expert Systems With Applications, 40(8): 2889--2903,
  • L. Son. Dealing with the new user cold-start problem in recommender systems: A comparative review, Information Systems, 58: 87?-104,
  • N. Mirbakhsh and C. Ling. Improving top-N recommendation for cold-start users via cross-domain information, ACM Transactions on Knowledge Discovery from Data, 9(4): 33,
  • B. Lika and K. Kolomvatsos and S. Hadjiefthymiades. Facing the cold start problem in recommender systems, Expert Systems With Applications, 41(4): 2065--2073,
  • M. Jiang and P. Cui and X. Chen and F. Wang and W. Zhu and S. Yang. Social recommendation with cross-domain transferable knowledge, IEEE Transactions on Knowledge and Data Engineering, 27(11): 3084-3097,
  • B. Hidasi and D. Tikk. General factorization framework for context-aware recommendations, Data Min Knowl Disc, 30: 342?-371,
  • I. Gunes and C. Kaleli and A. Bilge and H. Polat. Shilling attacks against recommender systems: A comprehensive survey, Artificial Intelligence Review, 42(4): 767--799,
  • H. Gao and J. Tang and H. Liu. Addressing the cold-start problem in location recommendation using geo-social correlations, Data Min. Knowl. Discov., 29(2): 299--323,
  • Eman Aldhahri and Vivek Shandilya and Sajjan Shiva. Towards an Effective Crowdsourcing Recommendation System: A Survey of the State-of-the-Art, 372--377, 2015 IEEE Symposium on Service-Oriented System Engineering,
  • L. Cao and Y. Zhao and C. Zhang. Mining impact-targeted activity patterns in imbalanced data, IEEE Trans. on Knowledge and Data Engineering, 20(8): 1053--1066, 2008.
  • L. Cao and H. Zhang and Y. Zhao and D. Luo and C. Zhang. Combined Mining: Discovering Informative Knowledge in Complex Data, IEEE Trans. SMC Part B, 41(3): 699--712, http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05621927&tag=1, 2011.
  • Vasant Dhar. Data science and prediction, Communications of the ACM, 56(12): 64--73, 2013.
  • W. Pan and Q. Yang. Transfer learning in heterogeneous collaborative filtering domains, Artificial Intelligence, 197: 39--55, 2013.
  • Zeno Gantner and Lucas Drumond and Christoph Freudenthaler and Steffen Rendle and Lars Schmidt-Thieme. Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, 176-185, The 10th {IEEE} International Conference on Data Mining {ICDM} 2010, 2010.
  • Longbing Cao and Xiangjun Dong and Zhigang Zheng. {e-NSP}: Efficient Negative Sequential Pattern Mining, Artificial Intelligence, 235: 156--182, 2016.
  • Qianqian Chen and Liang Hu and Jia Xu and Wei Liu and Longbing Cao. Document similarity analysis via involving both explicit and implicit semantic couplings, 42745, Proceedings of IEEE DSAA'2015, IEEE Press, 2015.
  • Alexandrin Popescul and Lyle H. Ungar and David M. Pennock and Steve Lawrence. Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, 437--444, Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 2001.
  • AP. Singh and GJ. Gordon. Relational Learning via Collective Matrix Factorization, 650-658, KDD'2008, 2008.
  • B. Fu and G. Xu and L. Cao and Z. Wang and Z. Wu. Coupling multiple views of relations for recommendation, 732-743, PAKDD (2), 2015.
  • L. Hu and J. Cao and G. Xu and L. Cao and Z. Gu and C. Zhu. Personalized recommendation via cross-domain triadic factorization, 595-606, WWW2013, 2013.
  • L. Hu and J. Cao and G. Xu and J. Wang and Z. Gu and L. Cao. Cross-domain collaborative filtering via Bilinear multilevel analysis, 42742, IJCAI2013, 2013.
  • L. Hu and J. Cao and G. Xu and L. Cao and Z. Gu and W. Cao. Deep modeling of group preferences for group-based recommendation, 1861-1867, AAAI2014, 2014.
  • G. Pang and L. Cao and L. Chen. Outlier Detection in Complex Categorical Data by Modelling the Feature Value Couplings, 42742, IJCAI2016, 2016.
  • Surya R. Kalidindi and Joshua Gomberg and Zachary T. Trautt and Chandler A. Becker. Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets, Nanotechnology, 26(34): 344--346, 2015.
  • Jeffrey Dean and Sanjay Ghemawat. {MapReduce}: Simplified Data Processing on Large Clusters, OSDI'2004, 2004.
  • Longbing Cao. Metasynthetic Computing and Engineering of Complex Systems, Springer, 2015.
  • Jennifer Rowley. The wisdom hierarchy: representations of the {DIKW} hierarchy, Journal of Information and Communication Science, 33(2): 163--180, 2007.
  • X. S. Qian and J. Y. Yu and R. W. Dai. A new discipline of science: The study of open complex giant system and its methodology, Chin. J. Syst. Eng. Electron., 4(2): 2--12, 1993.
  • X. S. Qian. Revisiting issues on open complex giant systems, Pattern Recognit. Artif. Intell., 4(1): 5--8, 1991.
  • Ana Viseu and Lucy Suchman. Wearable Augmentations: Imaginaries of the Informed Body, 161--184, Technologized Images Technologized Bodies, Berghahn Books, 2010.
  • Larry Smarr. Quantifying your body: A how-to guide from a systems biology perspective, Biotechnology Journal, 7(8): 980--991, WILEY-VCH Verlag, http://dx.doi.org/10.1002/biot.201100495, 2012.
  • X. Liu. Towards a highly effective and robust Web credibility evaluation system Source, Decision support systems, 79(2): 99--108, 2015.
  • Ron Kohavi and Neal J. Rothleder and Evangelos Simoudis. Emerging trends in business analytics, Communications of the ACM, 45(8): 45--48, 2002.
  • Dongliang Chu and Chase Qishi Wu and Zongmin Wang and Yongqiang Wang. A fully generalized over operator with applications to image composition in parallel visualization for big data science, 560--567, 2014 20th IEEE International Conference on Parallel and Distributed Systems, 2014.
  • P. Resnick and N. Iacovou and M. Suchak and P. Bergstrom and J. Riedl. {GroupLens}: An open architecture for collaborative filtering of netnews, 175-186, CSCW1994 Proceedings, 1994.
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  • Paul E. Anderson and James F. Bowring and RenĂ©e McCauley and George Pothering and Christopher W. Starr. An undergraduate degree in data science: Curriculum and a decade of implementation experience, 145--150, Computer Science Education: Proceedings of the 45th ACM Technical Symposium (SIGCSE'14), 2014.
  • P. E. Anderson and C. Turner and J. Dierksheide and R. McCauley. An extensible online environment for teaching data science concepts through gamification, 1--8, 2014 IEEE Frontiers in Education Conference (FIE), 2014.
  • Mitchell L. Stevens. An Ethically Ambitious Higher Education Data Science, Research & Practice in Assessment, 9: 96--97, 2014.
  • Ben Baumer. A data science course for undergraduates: Thinking with data, The American Statistician, 69(4): 334--342, 2015.
  • Tim O'Reilly. What is Web 2.0, http://oreilly.com/pub/a/web2/archive/what-is-web-20.html?page=3, 2005.
  • Swami Chandrasekaran. Becoming a Data Scientist, http://nirvacana.com/thoughts/becoming-a-data-scientist/, 2013.
  • Nassim Nicholas Taleb. The Black Swan: The Impact of the Highly Improbable, Random House, 2007.
  • G. Wolf. The {Data-Driven} Life, New York Times, www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html, 2012.
  • Kelly Clay. {CES} 2013: The Year of The Quantified Self?, http://www.forbes.com/sites/kellyclay/2013/01/06/ces-2013-the-year-of-the-quantified-self/#4cf4d2b55e74, 2013.
  • Karl Broman. Data science is statistics, https://kbroman.wordpress.com/2013/04/05/data-science-is-statistics/, 2013.
  • Irving Wladawsky-Berger. Why Do We Need Data Science When We’ve Had Statistics for Centuries?, The Wall Street Journal, http://blogs.wsj.com/cio/2014/05/02/why-do-we-need-data-science-when-weve-had-statistics-for-centuries/, 2014.
  • David Donoho. 50 Years of Data Science, http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf, 2015.
  • DJ Patil. Building Data Science Teams, O'Reilly Media, 2011.
  • WEF. The Global Competitiveness Report 2011-2012: An Initiative of the World Economic Forum, Personal Data: The Emergence of a New Asset Class, 2011.
  • C. Rudin. Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society, http://www.amstat.org/policy/pdfs/BigDataStatisticsJune2014.pdf, 2014.
  • J. Bailer and R. Hoerl and D. Madigan and J. Montaquila and T. Wright. Report of the {ASA} Workgroup on Master’s Degrees, American Statistical Association, http://magazine.amstat.org/wp-content/uploads/2013an/masterworkgroup.pdf, 2012.
  • Bin Yu. {IMS} Presidential Address: Let us own Data Science, IMS Bulletin Online, 2014.
  • ASA. {ASA} views on data science, http://magazine.amstat.org/?s=data+science&x=0&y=0, 2015.
  • David Van Dyk and Montse Fuentes and Michael I. Jordan and Michael Newton and Bonnie K. Ray and Duncan Temple Lang and Hadley Wickham. {ASA} Statement on the Role of Statistics in Data Science, http://magazine.amstat.org/blog/2015/10/01/asa-statement-on-the-role-of-statistics-in-data-science/, 2015.
  • IASC. International Association for Statistical Computing, http://www.iasc-isi.org/, 1977.
  • CCF-BDTF. China Computer Federation Task Force on Big Data, http://www.bigdataforum.org.cn/, 2013.
  • IEEEBD. {IEEE} Big Data Initiative, http://bigdata.ieee.org/, 2014.
  • TFDSAA. {IEEE} Task Force on Data Science and Advanced Analytics, http://dsaatf.dsaa.co/, 2013.
  • DABS. Data Analytics Book Series, http://www.springer.com/series/15063, 2016.
  • SSDS. Springer Series in the Data Sciences, http://www.springer.com/series/13852, 2015.
  • IFSC-96. Data Science Classification and Related Methods, IFSC-96, http://d-nb.info/955715512/04, 1996.
  • SIGKDD. {IJCAI-89} Workshop on Knowledge Discovery in Databases, http://www.kdnuggets.com/meetings/kdd89/index.html, 1989.
  • DSAA. {IEEE/ACM} International Conference on Data Science and Advanced Analytics, www.dsaa.co, 2014.
  • TOBD. {IEEE} Transactions on Big Data, https://www.computer.org/web/tbd, 2015.
  • JFDS. The Journal of Finance and Data Science, http://www.keaipublishing.com/en/journals/the-journal-of-finance-and-data-science/, 2016.
  • IJRDS. International Journal of Research on Data Science, http://www.sciencepublishinggroup.com/journal/index?journalid=310, 2017.
  • IJDS. International Journal of Data Science, http://www.inderscience.com/jhome.php?jcode=ijds, 2016.
  • DSE. Data Science and Engineering, http://link.springer.com/journal/41019, 2015.
  • JDSA. International Journal of Data Science and Analytics {(JDSA)}, http://www.springer.com/41060, 2015.
  • EPJDS. {EPJ} Data Science, http://epjdatascience.springeropen.com/, 2012.
  • JDS. Journal of Data Science, http://www.jds-online.com/, 2002.
  • DSJ. Data Science Journal, datascience.codata.org, 2014.
  • INFORMS. Candidate Handbook, https://www.informs.org/Certification-Continuing-Ed/Analytics-Certification/Candidate-Handbook, 2014.
  • Silk. Data Science University Programs, http://data-science-university-programs.silk.co/, 2016.
  • Github. Data science colleges, https://github.com/ryanswanstrom/awesome-datascience-colleges, 2016.
  • USDSC. {US} Degree Programs in Analytics and Data Science, http://analytics.ncsu.edu/?page_id=4184, 2016.
  • DSC. College & University Data Science Degrees, http://datascience.community/colleges (accessed on 16 April 2016.), 2016.
  • UTS. Master of Analytics by Research and {PhD} Thesis: Analytics {{Advanced} Analytics Institute University of Technology Sydney}, www.analytics.uts.edu.au, 2011.
  • NCSU. Master of Science in Analytics Institute for Advanced Analytics North Carolina State University, http://analytics.ncsu.edu/, 2007.
  • Open edX. {Open edX} Online education platform, https://open.edx.org/, 2016.
  • Google. Google Online Open Education, https://www.google.com/edu/openonline/, 2016.
  • Udacity. Udacity Courses, https://www.udacity.com/courses/data-science, 2016.
  • Udemy. Udemy Courses, https://www.udemy.com/courses/search/?ref=home&src=ukw&q=data+science&lang=en, 2016.
  • edX. {edX} Courses, https://www.edx.org/course?search_query=data+science, 2016.
  • Coursera. Coursera, www.coursera.org/data-science, 2016.
  • Hardin. Github, hardin47.github.io/DataSciStatsMaterials/, 2016.
  • Classcentral. Data Science and Big Data | Free Online Courses, https://www.class-central.com/subject/data-science, 2016.
  • CA. Canada Capitalizing on Big Data, http://www.sshrc-crsh.gc.ca/news_room-salle_de_presse/latest_news-nouvelles_recentes/big_data_consultation-donnees_massives_consultation-eng.aspx, 2016.
  • Research Councils UK. Research Councils UK Program on Big Data, http://www.rcuk.ac.uk/research/infrastructure/big-data/, 2016.
  • European Commission. Commission urges governments to embrace potential of big data, europa.eu/rapid/press-release_IP-14-769_en.htm 02/07/2014, 2014.
  • HLSG. An {RDA} Europe Report, The Data Harvest: How sharing research data can yield knowledge jobs and growth, http://www.e-nformation.ro/wp-content/uploads/2014/12/TheDataHarvestReport_-Final.pdf, 2014.
  • HLSG. Final Report of the High Level Expert Group on Scientific Data, Riding the wave: How Europe can gain from the rising tide of scientific data, http://ec.europa.eu/information_society/newsroom/cf/document.cfm?action=display&doc_id=707, 2010.
  • Horizon. European Commission Horizon 2020 Big Data Private Public Partnership, http://ec.europa.eu/programmes/horizon2020/en/h2020-section/information-and-communication-technologies, 2014.
  • EU. {EU} Towards a Thriving Data-driven Economy, https://ec.europa.eu/digital-single-market/en/towards-thriving-data-driven-economy, 2014.
  • CNSF. National Science Foundation China, http://www.nsfc.gov.cn/, 2015.
  • CMIST. China Will Establish A Series of National Labs, http://news.sciencenet.cn/htmlnews/2016/4/344404.shtm, 2016.
  • CN. China Big Data, http://www.gov.cn/zhengce/content/2015-09/05/content_10137.htm, 2015.
  • Longbing Cao. Strategic Recommendations on Advanced Data Industry and Services for the Yanhuang Science and Technology Park. Research Projects, 2011.
  • ACEMS. The Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers, acems.org.au/, 2014.
  • Australian Government Department of Finance. Australian Big Data, http://www.finance.gov.au/big-data/, 2016.
  • Gavin Brown. Review of Education in Mathematics Data Science and Quantitative Disciplines: Report to the Group of Eight Universities, https://go8.edu.au/publication/go8-review-education-mathematics-data-science-and-quantitative-disciplines, 2009.
  • NCSU. Institute for Advanced Analytics North Carolina State University, http://analytics.ncsu.edu/, 2007.
  • UMichi. Michigan Institute For Data Science University of Michigan, http://midas.umich.edu/, 2015.
  • UTSAAI. Advanced Analytics Institute University of Technology Sydney, https://analytics.uts.edu.au/, 2011.
  • Stanford. Stanford Data Science Initiatives Stanford University, https://sdsi.stanford.edu/, 2014.
  • DSKD. Data Science and Knowledge Discovery Lab {UTS}, http://www.uts.edu.au/research-and-teaching/our-research/quantum-computation-and-intelligent-systems/data-sciences-and, 2007.
  • Australian Government Information Management Office. Big Data Strategy – Issues Paper, www.finance.gov.au/files/2013/03/Big-Data-Strategy-Issues-Paper1.pdf, 2013.
  • Australian Government Federal Register of Legislation. Data-matching Program, http://www.comlaw.gov.au/Series/C2004A04095, 1990.
  • Australian Government Attorney-General's Department. Australia Joins Open Government Partnership, http://www.attorneygeneral.gov.au/Mediareleases/Pages/2013/Second\%20quarter/22May2013-AustraliajoinsOpenGovernmentPartnership.aspx, 2013.
  • Australian Government Department of Finance and Deregulation. Declaration of Open Government, http://agimo.gov.au/2010/07/16/declaration-of-open-government/, 2010.
  • UK HM Government. Open Data White Paper: Unleashing the Potential, https://data.gov.uk/sites/default/files/Open_data_White_Paper.pdf, 2012.
  • Data Team in the Cabinet Office. UK Government Data, http://data.gov.uk/, 2016.
  • Publications Office of the European Union. The European Union Open Data Portal, https://open-data.europa.eu/, 2016.
  • U.S. General Services Administration. U.S. Government?s Open Data, https://www.data.gov/, 2016.
  • OECD. {OECD} Principles and Guidances for Access to Research Data from Public Funding, https://www.oecd.org/sti/sci-tech/38500813.pdf, 2007.
  • John King and Roger Magoulas. 2015 Data Science Salary Survey, http://duu86o6n09pv.cloudfront.net/reports/2015-data-science-salary-survey.pdf, 2015.
  • Linda Burtch. The Burtch Works Study: Salaries of Data Scientists, http://www.burtchworks.com/files/2014/07/Burtch-Works-Study_DS_final.pdf, 2014.
  • Kirk D. Borne and Suzanne Jacoby and Karen Carney and Andy Connolly and Timothy Eastman and M. Jordan Raddick and J. A. Tyson and John Wallin. The revolution in astronomy education: Data science for the masses, Astro2010 Decadal Survey, http://arxiv.org/pdf/0909.3895v1.pdf, 2010.
  • CRISP-DM. {CRISP-DM}, www.sv-europe.com/crisp-dm-methodology, 2016.
  • Wikipedia. Informatics, https://en.wikipedia.org/wiki/Informatics, 2016.
  • KDnuggets. Kdnuggets, http://www.kdnuggets.com/, 2016.
  • EU-DSA. The European Data Science Academy, edsa-project.eu, 2016.
  • DSA. Data Science Association, http://www.datascienceassn.org/, 2016.
  • Datasciences.org. Datasciences.org, www.datasciences.org, 2005.
  • INFORMS. Institute for Operations Research and the Management Sciences, https://www.informs.org/, 2016.
  • IDA. International Institute of Data & Analytics, www.idawise.com, 2014.
  • DSCentral. Data Science Central, http://www.datasciencecentral.com/, 2016.
  • DSC. The Data Science Community, http://datasciencebe.com/, 2016.
  • Jim Gray. eScience — A Transformed Scientific Method, http://research.microsoft.com/en-us/um/people/gray/talks/NRC-CSTB_eScience.ppt, 2007.
  • EMC. Data Science Revealed: A {Data-Driven} Glimpse into the Burgeoning New Field, EMC, www.emc.com/collateral/about/news/emc-data-science-study-wp.pdf, 2011.
  • Kaggle. Kaggle Competition Data, https://www.kaggle.com/competitions, 2016.
  • UCI. {UCI} Machine Learning Repository, archive.ics.uci.edu/ml/, 2016.
  • GEO. Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, 2016.
  • Yahoo. Yahoo Finance, finance.yahoo.com, 2016.
  • GTD. Global Terrorism Database, https://www.start.umd.edu/gtd/, 2016.
  • Google. Open Mobile Data, https://console.developers.google.com/storage/browser/openmobiledata_public/, 2016.
  • Tutiempo. Global Climate Data, http://en.tutiempo.net/climate, 2016.
  • NIST. {NIST} Text Retrieval Conference Data, http://trec.nist.gov/data.html, 2015.
  • LDC. Linguistic Data Consortium, https://www.ldc.upenn.edu/about, 2016.
  • National Visualization and Analytics Center. Visual Analytics Community, http://vacommunity.org/HomePage, 2016.
  • Google. Google Trends, https://www.google.com.au/trends/explore?date=all&q=data science,data analytics,big data,data analysis,advanced analytics, 2016.
  • David Feinleib. Big Data Landscape, www.bigdatalandscape.com, 2016.
  • Matt Turck. Big Data Landscape 2016 v18 Final, http://mattturck.com/big-data-landscape-2016-v18-final/, 2016.
  • Technavio. Top 10 Healthcare Data Analytics Companies, http://www.technavio.com/blog/top-10-healthcare-data-analytics-companies, 2016.
  • Github. List of Recommender Systems, https://github.com/grahamjenson/list_of_recommender_systems, 2016.
  • Predictive Analytics Today. 29 Data Preparation Tools and Platforms, http://www.predictiveanalyticstoday.com/data-preparation-tools-and-platforms/, 2016.
  • Solutions Review. Data Integration and Application Integration Solutions Directory, http://solutionsreview.com/data-integration/data-integration-solutions-directory/, 2016.
  • Capterra. Top Project Management Tools, http://www.capterra.com/project-management-software/, 2016.
  • Wikipedia. List of reporting software, https://en.wikipedia.org/wiki/List_of_reporting_software, 2016.
  • Wikipedia. Comparison of cluster software, https://en.wikipedia.org/wiki/Comparison_of_cluster_software, 2016.
  • Capterra. Top Reporting Software Products, http://www.capterra.com/reporting-software/, 2016.
  • Jessica Davis. 10 Programming Languages And Tools Data Scientists Used, http://www.informationweek.com/devops/programming-languages/10-programming-languages-and-tools-data-scientists-use-now/d/d-id/1326034, 2016.
  • KDnuggets. Visualization Software, http://www.kdnuggets.com/software/visualization.html, 2015.
  • Devendra Desale. Top 30 Social Network Analysis and Visualization Tools, http://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html, 2015.
  • Visone. Visone, http://visone.info/index.html.
  • Sentinel Visualizer. Sentinel Visualizer, http://www.fmsasg.com/.
  • SocNetV. ocial Networks Visualizer, http://socnetv.sourceforge.net/.
  • NodeXL. NodeXL, http://nodexl.codeplex.com/.
  • NetworkX. NetworkX, http://networkx.github.io/.
  • Network Workbench. Network Workbench, http://nwb.cns.iu.edu/.
  • NetMiner. NetMiner, http://www.netminer.com/main/main-read.do.
  • Netlytic. Netlytic, https://netlytic.org/home/.
  • Meerkat. Meerkat, http://www.aicml.ca/node/41.
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