My graduate discipline is Data Analytics Engineering, which deals with gathering of information and analyzing them to gain profitable outcomes. I became aware of its importance while I was working with GOOGLE. Attending Team meetings with Program Managers and Keynote presentations developed curiosity towards advancement in Information Technology and this in turn led me to the concept of Big Data which is my specialization under Data Analytics Engineering. Big Data is referred to as a technique where tremendous amount of data which hasn’t been looked upon for years is taken out and put through the different methodologies of analysis, visualization and mining for finding out the loop holes and worked upon in return to benefit the organization with profitable results. Big Data Specialist is the designation that a person earns who offers his expertise in Data mining, Analyzing, Visualization, Statistical applications and Business Intelligence. Many methodologies play important role in the process of implementation of Big Data technology and these are to be mastered by one with great interest and enthusiasm to become successful. My thirst for this passion of mine will be quenched by my zeal and so will I strive hard and break free all the hassles in my way to accomplish it.
Some of the key references, handbooks & encyclopedias useful in this discipline are:
1. Data Analysis: an Introduction (Lewis-Beck, Michael S., 1995).
2. Handbook of Statistical Methods (C. E. P., & Carruthers, N. 1953).
3. Quality Engineering Handbook (Pyzdek, T., & Keller, P. A. (Eds.). 2003).
4. Manual on Presentation of Data and Control Chart Analysis ()
5. Pragmatic Data Analysis (Veryard, R. 1984)
6. Big Data Now (Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. 2011.)
7. Mining of Massive Data Sets (Rajaraman, A., & Ullman, J. D. 2011)
8. Using Multivariate Statistics (Tabachnick, B. G., & Fidell, L. S. 2001)
9. Juran’s Quality Handbook (Juran, J., & Godfrey, A. B. 1999)
10. Encyclopedia of Computer Science (Van Nostrand Reinhold, 1993.)
These resources are a collection of knowledge, produced to educate for references and start up people. These books are used in universities by the professors in this field as they contain high comprehensive and valuable information. These resources lecture on the birth of data analysis, its methodologies and its limitations.
b.) Major journals in the field:
1. International Journal of Big Data Intelligence (Chen, H., Chiang, R. H., & Storey, V. C. (2012).)
2. Big Data & Society (Taylor, A. S., Lindley, S., Regan, T., & Sweeney, D. Big Data & Society.)
3. Journal of Economic Perspectives (American Economic association, 1987)
4. Computational Statistics (Givens, G. H., & Hoeting, J. A. (2012). )
5. Predictive Analytics With Surveillance Big Data (Ayhan, S., Pesce, J., Comitz, P., Gerberick, G., & Bliesner, S. (2012, November)
6. Asian Journal on Big Data (Atharva Scientific Publications 2013)
7. Twitter Analytics : A Big Data Management Perspective (Goonetilleke, O., Sellis, T., Zhang, X., & Sathe, S. (2014).)
8. DiNoDB: Efficient Large-Scale Raw Data Analytics (Tian, Y., Alagiannis, I., Liarou, E., Ailamaki, A., Michiardi, P., & Vukolic, M. (2014, September).
9. Advanced Data Analytics Education for Students and Companies (Marttila-Kontio, M., Kontio, M., & Hotti, V. (2014, June).)
10. Workflow-based Human-in-the-Loop Data Analytics (Liu, J., Wilson, A., & Gunning, D. (2014, April).)
These are some of the major journals in the field of Data Analytics. These journals are related to present studies, new technologies, etc. which are new up-comings in the world. They are highly influential and scholarly publications that educate on the daily events of Big Data and various other news related to this field.
c.) Some influential books or seminal works in special education are:
1. A Data Analytics Case Study Assessing Factors Affecting Pavement Deflection Values (Seyfi, M., Rawat, R., Weligamage, J., & Nayak, R. (2013))
2. Flexible MapReduce Workflows for Cloud Data Analytics (Goncalves, C., Assuncao, L., & Cunha, J. C. (2013))
3. Scalable Progressive Analytics on Big Data in The Cloud (Chandramouli, B., Goldstein, J., & Quamar, A. (2013).)
4. Business Intelligence and Analytics: From Big Data to Big Impact (Chen, H., Chiang, R. H., & Storey, V. C. (2012).)
5. System of Systems and Big Data Analytics – Bridging the gap (Tannahill, B. K., & Jamshidi, M. (2014).)
6. Transformational Issues of Big Data and Analytics in Networked Business (Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L.)
7. A Case Study of the Emergence of Data Analytics in Health Care (Vo, A., & Bhaskar, R. (2012).)
8. Next Generation Data Analytics at IBM Research (Hassanzadeh, O., Kementsietsidis, A., Kimelfeld, B., Krishnamurthy, R., Özcan, F., & Pandis, I. (2013) )
9. Embedded Analytics and Statistics for Big Data (Louridas, P., & Ebert, C. (2013) )
10. Using In-Memory Analytics to Quickly Crunch Big Data (Garber, L. (2012).)
11. Big Data Analytics: Perspective Shifting from Transactions to Ecosystems (Zeng, D., & Lusch, R. (2013).)
12. Ubiquitous Analytics: Interacting with Big Data Anywhere, Anytime (Elmqvist, N., & Irani, P. (2013).)
These influential and seminal works portray the developments related to Analytics and the curriculum involved in it. The recent developments the field are looked into and expert reviews with their research are engulfed to display their initiative towards progress of Big Data in the global market. Seminal works are illustrated to be a bank of knowledge through which exploratory knowledge is conveyed and implemented.
d.) Relevant professional associations or organizations:
1. Advanced Analytics Institute (AAI)
2. American Statistical Association (ASA)
3. National Center for Data Mining (NCDM)
4. Web Analytics Association (WAA)
5. International Institute of Business Analysis (IIBA)
6. The International Machine Learning Society (IMLS)
7. Data Governance Professional Organization (DGPO)
8. Certified Health Data Analyst (CHDA)
9. International Statistical Institute (ISI)
10. The Data Management Association (DAMA)
These organizations and professional associations impart knowledge on standards of technologies and implementations of the technologies to the modern market. They intend to research on the limitations of the technology and share them around associations to overcome them. They also enforce training standards to the employees and even work ethics to make them better professional practitioners.
References:
• Lewis-Beck, M. S. (Ed.). (1995). Data analysis: An introduction (No. 103). Sage.
• Brooks, C. E. P., & Carruthers, N. (1953). Handbook of statistical methods in meteorology. Handbook of statistical methods in meteorology.
• Pyzdek, T., & Keller, P. A. (Eds.). (2003). Quality engineering handbook. CRC Press.
• Pallant, J. (2010). SPSS survival manual: A step by step guide to data analysis using SPSS. McGraw-Hill International.
• Veryard, R. (1984). Pragmatic data analysis. Oxford, England: Blackwell Scientific Publications.
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
• Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Cambridge University Press.
• Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics.
• Defeo, J., & Juran, J. M. (2010). Juran’s Quality Handbook: The Complete Guide to Performance Excellence 6/e. McGraw Hill Professional.
• Encyclopedia of computer science. Vol. 536. New York: Van Nostrand Reinhold, 1993.
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.
• Taylor, A. S., Lindley, S., Regan, T., & Sweeney, D. Big Data & Society.
• Givens, G. H., & Hoeting, J. A. (2012). Computational statistics (Vol. 708). John Wiley & Sons.
• Ayhan, S., Pesce, J., Comitz, P., Gerberick, G., & Bliesner, S. (2012, November). Predictive analytics with surveillance big data. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (pp. 81-90). ACM.
• Goonetilleke, O., Sellis, T., Zhang, X., & Sathe, S. (2014). Twitter analytics: a big data management perspective. ACM SIGKDD Explorations Newsletter,16(1), 11-20.
• Tian, Y., Alagiannis, I., Liarou, E., Ailamaki, A., Michiardi, P., & Vukolic, M. (2014, September). DiNoDB: Efficient Large-Scale Raw Data Analytics. InProceedings of the First International Workshop on Bringing the Value of Big Data to Users (Data4U 2014) (p. 1). ACM.
• Marttila-Kontio, M., Kontio, M., & Hotti, V. (2014, June). Advanced data analytics education for students and companies. In Proceedings of the 2014 conference on Innovation & technology in computer science education (pp. 249-254). ACM
• Liu, J., Wilson, A., & Gunning, D. (2014, April). Workflow-based Human-in-the-Loop Data Analytics. In Proceedings of the 2014 Workshop on Human Centered Big Data Research (p. 49). ACM
• Seyfi, M., Rawat, R., Weligamage, J., & Nayak, R. (2013). A data analytics case study assessing factors affecting pavement deflection values.International Journal of Business Intelligence and Data Mining, 8(3), 199-226.
• Goncalves, C., Assuncao, L., & Cunha, J. C. (2013). Flexible MapReduce Workflows for Cloud Data Analytics. International Journal of Grid and High Performance Computing (IJGHPC), 5(4), 48-64.
• Chandramouli, B., Goldstein, J., & Quamar, A. (2013). Scalable progressive analytics on big data in the cloud. Proceedings of the VLDB Endowment, 6(14), 1726-1737
• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.
• (Tannahill, B. K., & Jamshidi, M. (2014). System of Systems and Big Data analytics–Bridging the gap. Computers & Electrical Engineering, 40(1), 2-15
• Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L. Transformational Issues of Big Data and Analytics in Networked Business
• Vo, A., & Bhaskar, R. (2012). A Case Study of the Emergence of Data Analytics in Health Care. Journal of Cases on Information Technology (JCIT),14(4), 56-62
• Hassanzadeh, O., Kementsietsidis, A., Kimelfeld, B., Krishnamurthy, R., Özcan, F., & Pandis, I. (2013). Next generation data analytics at IBM research.Proceedings of the VLDB Endowment, 6(11), 1174-1175
• Louridas, P., & Ebert, C. (2013). Embedded Analytics and Statistics for Big Data. IEEE software, 30(6), 33-39.
• Garber, L. (2012). Using in-memory analytics to quickly crunch big data.Computer, 45(10), 16-18
• Zeng, D., & Lusch, R. (2013). Big Data Analytics: Perspective Shifting from Transactions to Ecosystems. IEEE Intelligent Systems, 28(2), 2-5.
Recent Comments