Doing Data Science: Straight Talk from the Frontline by Rachel Schutt, Cathy O'Neil
By Rachel Schutt, Cathy O'Neil
Now that individuals are conscious that facts could make the variation in an election or a company version, facts technological know-how as an career is gaining floor. yet how will you start operating in a wide-ranging, interdisciplinary box that’s so clouded in hype? This insightful publication, according to Columbia University’s advent to facts technological know-how type, tells you what you must know.
in lots of of those chapter-long lectures, info scientists from businesses akin to Google, Microsoft, and eBay proportion new algorithms, tools, and versions by way of featuring case reviews and the code they use. If you’re accustomed to linear algebra, likelihood, and statistics, and feature programming event, this publication is a perfect creation to information science.
• Statistical inference, exploratory information research, and the information technology method
• unsolicited mail filters, Naive Bayes, and information wrangling
• Logistic regression
• monetary modeling
• suggestion engines and causality
• info visualization
• Social networks and information journalism
• information engineering, MapReduce, Pregel, and Hadoop
Doing info Science is collaboration among direction teacher Rachel Schutt, Senior vice president of information technological know-how at information Corp, and information technological know-how advisor Cathy O’Neil, a senior info scientist at Johnson study Labs, who attended and blogged concerning the course.
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Additional info for Doing Data Science: Straight Talk from the Frontline
But if you took them and counted how many email messages each person sent, and used that to estimate the underlying distribution of emails sent by all indiviuals at BigCorp, you might get entirely different answers. So if even getting a basic thing down like counting can get distorted when you’re using a reasonable-sounding sampling method, imagine what can happen to more complicated algorithms and models if you haven’t taken into account the process that got the data into your hands. 20 | Chapter 2: Statistical Inference, Exploratory Data Analysis, and the Data Science Process Populations and Samples of Big Data But, wait!
It’s traditionally presented as a bunch of histograms and stem-and-leaf plots. They teach that stuff to kids in fifth grade so it seems trivial, right? No wonder no one thinks much of it. But EDA is a critical part of the data science process, and also repre‐ sents a philosophy or way of doing statistics practiced by a strain of statisticians coming from the Bell Labs tradition. John Tukey, a mathematician at Bell Labs, developed exploratory data analysis in contrast to confirmatory data analysis, which concerns it‐ self with modeling and hypotheses as described in the previous section.
As we mentioned earlier, a data science team works best when different skills (profiles) are represented across different people, because no‐ body is good at everything. It makes us wonder if it might be more worthwhile to define a “data science team”—as shown in Figure 1-3— than to define a data scientist. A Data Science Profile | 11 Figure 1-3. Data science team profiles can be constructed from data scientist profiles; there should be alignment between the data science team profile and the profile of the data problems they try to solve 12 | Chapter 1: Introduction: What Is Data Science?