Data Analytics Handbook p. 3, Interviews with Research Leaders and Academics, Free Download

Part 3 features interviews with research leaders and academics, including Hal Varian (Chief Economist, Google), Gregory Piatetsky (Editor, KDnuggets), and Analytics Thought Leader Tom Davenport (Professor, Babson College). Free download.

By Gregory Piatetsky, May 13, 2014.

A young team of 3 UC Berkeley students (Brian Liou, Tristan Tao, and Elizabeth Lin) has produced an interesting, although not entirely accurately named publication, called Data Analytics Handbook, available for free download.

Here is my summary of key takeaways from Parts 1 and 2, which featured interviews with data scientists and tech leaders from leading companies including LinkedIn, Cloudera, Facebook, Yelp, and Flurry.

They have now released Part 3 of the Data Analytics Handbook, which includes interviews with
  • Hal Varian, Chief Economist, Google Data Analytics Handbook, Part 3
  • Prasanna Tambe, Professor, NYU Stern School of Business
  • Gregory Piatetsky (me), President and Editor, KDnuggets
  • Michael Chui, Partner, McKinsey Global Institute
  • Jimmy Retzlaff, Professor, UC Berkeley I-School
  • Tim Piatenko, Chief Data Scientist,
  • David Smith, Chief Community Officer, Revolution
  • Tom Davenport, Professor, Babson College

  Here are their top 5 takeaways:
1. There are wrong questions to ask about data.
In addition to Type I and Type II errors in hypothesis testing there should be a Type III error - asking the "wrong" question about data. Type 3 error is spending a lot of effort on a question that cannot be answered with the available data.

2. Data Science is a strategic initiative.
The huge demand for data scientists is a result of companies early investment in Big Data and wanting to get returns from those investments. As more companies invest in Big Data it will result in the strategic recruitment of more data scientists and data science departments.

3. Data professionals must be humble.
Not only are humble people better to work with, but a data literate professional must be humble to its data. He/she must be willing to accept when hypotheses are disproven and be skeptical of results. He/she must recognize that data is the main channel in which users communicate with a company now.

4. Analytics is a basis for competition.
The effective use of data is going to form the basis for competition for every industry in every organization.

5. For data science, learn how to learn.
We are still in the early stages of data science so the tools will constantly evolve, therefore education is a continuing process and should not be tied to any specific tool. As more tools get commercialized, the build, buy, or outsource decision firms must make is impossible to predict so to be competitive become adept at learning new tools.

The team that produced the Handbook has also created an e-Learning application that aims to develop the statistical and programming skills necessary to become data literate professionals.

Learn more at .