Data Science Discussion

Data Science Discussion

The discussion will be based on the following articles. Please post at least one discussion point
Supplemental Readings include:

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Stop Hiring Data Scientists Until You’re Ready for Data Science by Greta Roberts, CEO, Talent Analytics Corp. Available at: https://venturebeat.com/2015/07/22/stop-hiring-data-scientists-until-youre-ready-for-data-science/

Davenport, T. H., & Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century.Harvard Business Review, (October), 70–77. Available at:

https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

2 – Essentials of Analytics
The discussion is based on the information below. Please post at least one discussion point
Supplemental Readings include:

Davenport, T. (2015). 5 Essential Principles for Understanding Analytics. Harvard Business Review, (October 21), Available at:

https://hbr.org/2015/10/5-essential-principles-for-understanding-analytics?cm_sp=Article-_-Links-_-Top%20of%20Page%20Recirculation

3 – Value of Analytics
The discussion will be based on the following articles. Please post at least one discussion point
Supplemental Readings include:

Analytics: The new path to value [Video file]. Retrieved from http://youtu.be/kwp3oei6KVk

The top 38 data visualisation tools. Retrieved from http://www.creativebloq.com/design-tools/data-visualization-712402 This article gives a brief overview to 38 tools and programming libraries that can be used to create graphs and interactive visualizations. D3 is a widely used Javascript library for visualizing data, WolframAlpha is an extremely powerful AI language and platform and a large amount of built in functionality and access to data sets. This is absolutely worth spending an hour or two exploring when you get the chance. (http://www.wolframalpha.com/ )

4 – Data Credibility Problem –
The discussion will be based on the following articles. Please post at least one discussion point
Supplemental Readings include:

Redman, T. C. (2013). Data’s Credibility Problem: Management – Not Technology – Is the Solution. Harvard Business Review, (December), 84–88. (https://hbr.org/2013/12/datas-credibility-problem )

Carifo, J., Perla, R. J., & Carifio, J. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert Scales and Likert Response Formats and their antidotes. Journal of Social Sciences, 3(3), 106–116. (http://thescipub.com/PDF/jssp.2007.106.116.pdf )

5 – Reproducible research
The discussion will be based on the following articles. Please post at least one discussion point
Supplemental Readings include:

Sandve GK et al. (2013) Ten Simple Rules for Reproducible Computational Research. PLoS Comput Biol 9(10): e1003285. doi:10.1371/journal.pcbi.1003285 (http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285 )

6 – Interpersonal barriers to decision making
The discussion will be based on the following articles. Please post at least one discussion point
Supplemental Readings include:

Argyris, C. (2001). Interpersonal barriers to decision making. In Harvard Business Review on decision making (pp. 59-96). Boston, MA: Harvard Business School Press. (Original work published 1966) (https://hbr.org/1966/03/interpersonal-barriers-to-decision-making )