Data Scientists’ Skills in Detecting Archetypes in Iran

Hamideh Iraj - University of Tehran, Chamran, Tehran, Iran
Babak Sohrabi - University of Tehran, Chamran, Tehran, Iran


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.1.2.17

Abstract


The use of data-driven decision making and data scientists is on the rise in Iran as companies have rapidly been focusing on gathering data and analyzing it to guide corporate decisions. In order to facilitate the process and understand the nature and characteristics of this transformation, the current study intends to learn about data scientists’ skills and archetypes in Iran. Detecting skills archetypes has been done via analyzing the skills of data scientists which were self-expressed through an online survey. The results revealed that there are three archetypes of data scientists including high level data scientists, low level data scientists and software developers. The archetypal patterns are based on levels of data scientists’ skills rather than the type of dominant skills they possess which was the most frequent pattern in previous studies.


Keywords


Data science; Data scientists; Archetypes; skills

Full Text:

PDF

References


ASI team. (2015). Retrieved from ASI Data Science & Business Analytics: http://www.theasi.co/

Fraser, B., Treagust, D., & Dennis, N. (1986). Development of an instrument for assessing classroom psychosocial environment at universities and colleges. Studies in Higher Education, 11(1), 43-54.

Han, J., Kamber, M., & Pei, J. (2011). Data mining : concepts and techniques (3rd ed.). Morgan Kaufmann Publishers.

Harris, H. D., Murphy, S., & Vaisman, M. (2013). Analyzing the Analyzers An Introspective Survey of Data Scientists and Their Work. O’Reilly Media. Retrieved from http://www.oreilly.com/data/free/analyzing-the-analyzers.csp

Harris, J., Shetterley, N., Alter, A., & Schnell, K. (2013). The Team Solution to the Data Scientist Shortage. Accenture Institute for high performance. Retrieved from http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Team-Solution-Data-Scientist-Shortage.pdf

Hennig, C. (2015). fpc: Flexible Procedures for Clustering. Retrieved from R package version 2.1-10: http://CRAN.R-project.org/package=fpc

Kandel, S., Paepcke, A., Hellerstein, J. M., & Heer, J. (2012). Enterprise Data Analysis and Visualization: An Interview Study. IEEE Visual Analytics Science & Technology (VAST).

Leisch, F. (2006). A Toolbox for K-Centroids Cluster Analysis. Computational Statistics and Data Analysis, 51(2), 526-544.

Liberatore, M., & Luo, W. (2010). The Analytics Movement: Implications for Operations Research. Interfaces, 40(4), 313–324.

Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2015). cluster: Cluster Analysis Basics and Extensions. Retrieved from R package version 2.0.1: https://cran.r-project.org/web/packages/cluster/index.html

Maruyama, H. (2013). Developing Data Analytics Skills in Japan: Status and Challenge. The Institute of Statistical Mathematics. Retrieved from http://datascientist.ism.ac.jp/pdf/DSTN2013Report_Summary.pdf

Stadelmann, T., Stockinger, K., Braschler, M., Cieliebak, M., Baudinot, G., Dürr, O., & Ruckstuhl, A. (2013). Applied Data Science in Europe: Challenges for Academia in Keeping Up with a Highly Demanded Topic. European Computer Science Summit. Amsterdam, Netherlands.

The Data Incubator team. (2015). Retrieved from The Data Incubator: https://www.thedataincubator.com/

Tukey, J. (1977). Exploratory Data Analysis. Addison-Wesley Publishing Company.

Williams, G. (2011). Data Mining with Rattle and R: The art of excavating data for knowledge discovery. Springer.

YiLan, L., & RuTong, Z. (2015). clustertend: Check the Clustering Tendency. Retrieved from R package version 1.4.: http://CRAN.R-project.org/package=clustertend