data science

Data Science from Scratch: First Principles with Python

by Joel Grus  · 13 Apr 2015  · 579pp  · 76,657 words

See http://oreilly.com/catalog/errata.csp?isbn=9781491901427 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Data Science from Scratch, the cover image of a Rock Ptarmigan, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and

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, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-491-90142-7 [LSI] Preface Data Science Data scientist has been called “the sexiest job of the 21st century,” presumably by someone who has never visited a fire station. Nonetheless

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10 years, we’ll need billions and billions more data scientists than we currently have. But what is data science? After all, we can’t produce data scientists if we don’t know what data science is. According to a Venn diagram that is somewhat famous in the industry, data science lies at the intersection of: Hacking skills Math

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. This is emphatically not a math book, and for the most part, we won’t be “doing mathematics.” However, you can’t really do data science without some understanding of probability and statistics and linear algebra. This means that, where appropriate, we will dive into mathematical equations, mathematical intuition, mathematical axioms

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tax preparation or coal mining.) From Scratch There are lots and lots of data science libraries, frameworks, modules, and toolkits that efficiently implement the most common (as well as the least common) data science algorithms and techniques. If you become a data scientist, you will become intimately familiar with NumPy, with scikit-learn, with pandas,

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and with a panoply of other libraries. They are great for doing data science. But they are also a good way to start doing data science without actually understanding data science. In this

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book, we will be approaching data science from scratch.

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of our introduction to data science will take this same approach  —  going into detail where going into detail seems crucial or illuminating, at other times leaving details for you to figure out yourself (or look up on Wikipedia). Over the years, I’ve trained a number of data scientists. While not all of

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data ninja rockstars, I’ve left them all better data scientists than I found them. And I’ve grown to believe that anyone who has some amount of mathematical aptitude and some amount of programming skill has the necessary raw materials to do data science. All she needs is an inquisitive mind, a

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element indicates a warning or caution. Using Code Examples Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/joelgrus/data-science-from-scratch. This book is here to help you get your job done. In general, if example code is offered with this book, you may

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’s documentation does require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Data Science from Scratch by Joel Grus (O’Reilly). Copyright 2015 Joel Grus, 978-1-4919-0142-7.” If you feel your use of code examples falls

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have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/data-science-from-scratch. To comment or ask technical questions about this book, send email to bookquestions@oreilly.com. For more information about our books, courses,

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questions that no one’s ever thought to ask. In this book, we’ll learn how to find them. What Is Data Science? There’s a joke that says a data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician. (I didn’t say

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them, though). In short, pretty much no matter how you define data science, you’ll find practitioners for whom the definition is totally, absolutely wrong. Nonetheless, we won’t let that stop us from trying. We’ll say that a data scientist is someone who extracts insights from messy data. Today’s world

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political campaigns of the future will become more and more data-driven, resulting in a never-ending arms race of data science and data collection. Now, before you start feeling too jaded: some data scientists also occasionally use their skills for good — using data to make government more effective, to help the homeless,

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to click on advertisements. Motivating Hypothetical: DataSciencester Congratulations! You’ve just been hired to lead the data science efforts at DataSciencester, the social network for data scientists. Despite being for data scientists, DataSciencester has never actually invested in building its own data science practice. (In fairness, DataSciencester has never really invested in building its product either.) That will

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be your job! Throughout the book, we’ll be learning about data science concepts by solving problems that you

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(id 0) but only one mutual friend with Clive (id 5). As a data scientist, you know that you also might enjoy meeting users with similar interests. (This is a good example of the “substantive expertise” aspect of data science.) After asking around, you manage to get your hands on this data, as

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creating many, many functions. Strings Strings can be delimited by single or double quotation marks (but the quotes have to match): single_quoted_string = 'data science' double_quoted_string = "data science" Python uses backslashes to encode special characters. For example: tab_string = "\t" # represents the tab character len(tab_string) # is 1 If

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= len(grades) # equals 3 We will frequently use dictionaries as a simple way to represent structured data: tweet = { "user" : "joelgrus", "text" : "Data Science is Awesome", "retweet_count" : 100, "hashtags" : ["#data", "#science", "#datascience", "#awesome", "#yolo"] } Besides looking for specific keys we can look at all of them: tweet_keys = tweet.keys() # list of keys

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data scientist it would be a good idea to read a statistics textbook. Many are freely available online. A couple that I like are: OpenIntro Statistics OpenStax Introductory Statistics Chapter 6. Probability The laws of probability, so true in general, so fallacious in particular. Edward Gibbon It is hard to do data science

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What will we do with all this statistics and probability theory? The science part of data science frequently involves forming and testing hypotheses about our data and the processes that generate it. Statistical Hypothesis Testing Often, as data scientists, we’ll want to test whether a certain hypothesis is likely to be true.

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the null. Warning Make sure your data is roughly normally distributed before using normal_probability_above to compute p-values. The annals of bad data science are filled with examples of people opining that the chance of some observed event occurring at random is one in a million, when what they

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Object Notation (JSON). JavaScript objects look quite similar to Python dicts, which makes their string representations easy to interpret: { "title" : "Data Science Book", "author" : "Joel Grus", "publicationYear" : 2014, "topics" : [ "data", "science", "data science"] } We can parse JSON using Python’s json module. In particular, we will use its loads function, which deserializes a string representing

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a JSON object into a Python object: import json serialized = """{ "title" : "Data Science Book", "author" : "Joel Grus", "publicationYear" : 2014, "topics" : [ "data", "science", "data science"] }""" # parse the JSON to create a Python dict deserialized = json.loads(serialized) if "data science" in deserialized["topics"]: print deserialized Sometimes an API provider hates you and only provides responses

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, not the access token or secret: from twython import Twython twitter = Twython(CONSUMER_KEY, CONSUMER_SECRET) # search for tweets containing the phrase "data science" for status in twitter.search(q='"data science"')["statuses"]: user = status["user"]["screen_name"].encode('utf-8') text = status["text"].encode('utf-8') print user, ":", text print Note The

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Unicode text. If you run this, you should get some tweets back like: haithemnyc: Data scientists with the technical savvy & analytical chops to derive meaning from big data are in demand. http://t.co/HsF9Q0dShP RPubsRecent: Data Science http://t.co/6hcHUz2PHM spleonard1: Using #dplyr in #R to work through a procrastinated

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to save them to a file or a database, so that you’d have them permanently. For Further Exploration pandas is the primary library that data science types use for working with (and, in particular, importing) data. Scrapy is a more full-featured library for building more complicated web scrapers that

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. Histogram of uniform Two Dimensions Now imagine you have a data set with two dimensions. Maybe in addition to daily minutes you have years of data science experience. Of course you’d want to understand each dimension individually. But you probably also want to scatter the data. For example, consider another

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Machine Learning I am always ready to learn although I do not always like being taught. Winston Churchill Many people imagine that data science is mostly machine learning and that data scientists mostly build and train and tweak machine-learning models all day long. (Then again, many of those people don’t actually

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know what machine learning is.) In fact, data science is mostly turning business problems into data problems and collecting data and understanding data and cleaning

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the trendiest subfields of data science. However, most neural networks are “black boxes” — inspecting their details doesn’t give you much understanding of how they’re solving a problem. And large neural networks can be difficult to train. For most problems you’ll encounter as a budding data scientist, they’re probably not

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appears on resumes: data = [ ("big data", 100, 15), ("Hadoop", 95, 25), ("Python", 75, 50), ("R", 50, 40), ("machine learning", 80, 20), ("statistics", 20, 60), ("data science", 60, 70), ("analytics", 90, 3), ("team player", 85, 85), ("dynamic", 2, 90), ("synergies", 70, 0), ("actionable insights", 40, 30), ("think out of the box", 45

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where sentences end). We can do this using re.findall(): from bs4 import BeautifulSoup import requests url = "http://radar.oreilly.com/2010/06/what-is-data-science.html" html = requests.get(url).text soup = BeautifulSoup(html, 'html5lib') content = soup.find("div", "entry-content") # find entry-content div regex = r"[\w']+|[\.]" # matches

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seen verbatim in the original data. Having more data would help; it would also work better if you collected n-grams from multiple essays about data science. Grammars A different approach to modeling language is with grammars, rules for generating acceptable sentences. In elementary school, you probably learned about parts of

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. We’ll define a slightly more complicated grammar: grammar = { "_S" : ["_NP _VP"], "_NP" : ["_N", "_A _NP _P _A _N"], "_VP" : ["_V", "_V _NP"], "_N" : ["data science", "Python", "regression"], "_A" : ["big", "linear", "logistic"], "_P" : ["about", "near"], "_V" : ["learns", "trains", "tests", "is"] } I made up the convention that names starting with underscores refer

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trains','logistic','_NP','_P','_A','_N'] ['Python','trains','logistic','_N','_P','_A','_N'] ['Python','trains','logistic','data science','_P','_A','_N'] ['Python','trains','logistic','data science','about','_A', '_N'] ['Python','trains','logistic','data science','about','logistic','_N'] ['Python','trains','logistic','data science','about','logistic','Python'] How do we implement this? Well, to start, we’ll create a

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[word].append(count) return [output for word, counts in collector.iteritems() for output in wc_reducer(word, counts)] Imagine that we have three documents ["data science", "big data", "science fiction"]. Then wc_mapper applied to the first document yields the two pairs ("data", 1) and ("science", 1). After we’ve gone through all

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status updates. You manage to extract a data set of status updates that look like: {"id": 1, "username" : "joelgrus", "text" : "Is anyone interested in a data science book?", "created_at" : datetime.datetime(2013, 12, 21, 11, 47, 0), "liked_by" : ["data_guy", "data_gal", "mike"] } Let’s say we need to

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. And if we emit a value of 1 for each update that contains “data science,” we can simply get the total number using sum: def data_science_day_mapper(status_update): """yields (day_of_week, 1) if status_update contains "data science" """ if "data science" in status_update["text"].lower(): day_of_week = status_update["created_at

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Bokeh is a project that brings D3-style functionality into Python. R Although you can totally get away with not learning R, a lot of data scientists and data science projects use it, so it’s worth getting at least familiar with it. In part, this is so that you can understand people’s

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Data rescaling, Rescaling-Rescaling data mining, What Is Machine Learning? data scienceabout, Data Science defined, What Is Data Science? doing, projects of the author, Do Data Science from scratch, From Scratch learning more about, Go Forth and Do Data Science-And You? skills needed for, Data Science using libraries, Not from Scratch data visualization, Visualizing Data-For Further Explorationbar

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with vectors, Vectors About the Author Joel Grus is a software engineer at Google. Previously he worked as a data scientist at several startups. He lives in Seattle, where he regularly attends data science happy hours. He blogs infrequently at joelgrus.com and tweets all day long at @joelgrus. Colophon The animal on

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in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments 1. Introduction The Ascendance of Data What Is Data Science? Motivating Hypothetical: DataSciencester Finding Key Connectors Data Scientists You May Know Salaries and Experience Paid Accounts Topics of Interest Onward 2. A Crash Course in Python The Basics Getting Python

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

by Foster Provost and Tom Fawcett  · 30 Jun 2013  · 660pp  · 141,595 words

Business is intended for several sorts of readers: Business people who will be working with data scientists, managing data science–oriented projects, or investing in data science ventures, Developers who will be implementing data science solutions, and Aspiring data scientists. This is not a book about algorithms, nor is it a replacement for a book about algorithms. We deliberately

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data mining techniques and algorithms, as well as data science applications, quite generally. Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist Before proceeding, we should briefly revisit the engineering side of data science. At the time of this writing, discussions of data science commonly mention not just analytical skills and techniques for

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and to be willing to invest in data and experimentation. Furthermore, unless the firm has on its management team a seasoned, practical data scientist, often the management must steer the data science team carefully to make sure that the team stays on track toward an eventually useful business solution. This is very difficult if

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business, but generally few companies are able to execute it. For example, you must have confidence that you have one of the best data science teams, since the effectiveness of data scientists has a huge variance, with the best being much more talented than the average. If you have a great team, you may

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Your model is not what your data scientists design, it’s what your engineers implement. Superior Data Scientists Maybe our data scientists simply are much better than our competitors’. There is a huge variance in the quality and ability of data scientists. Even among well-trained data scientists, it is well accepted within the data science community that certain individuals have the

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, we have a substantial and sustained advantage over competitors who are having trouble hiring data scientists. Further, top-notch data scientists like to work with other top-notch data scientists, which compounds our advantage. We also must embrace the fact that data science is in part a craft. Analytical expertise takes time to acquire, and all the

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in the sense of what one might find in an online professional networking system; an effective data scientist needs to have deep connections to other data scientists throughout the data science community. The reason is simply that the field of data science is immense and there are far too many diverse topics for any individual to master. A

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And, the best data scientists have the best connections. Superior Data Science Management Possibly even more critical to success for data science in business is having good management of the data science team. Good data science managers are especially hard to find. They need to understand the fundamentals of data science well, possibly even being competent data scientists themselves. Good data science managers also must possess

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or expensive for a competitor to duplicate because we can hire data scientists and data science managers better. This may be due to our reputation and brand appeal with data scientists—a data scientist may prefer to work for a company known as being friendly to data science and data scientists. Or our firm may have a more subtle appeal. So

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we mentioned above, there can be a huge difference between the effectiveness of a great data scientist and an average data scientist, and between a great data science team and an individually great data scientist. But how can one confidently engage top-notch data scientists? How can we create great teams? This is a very difficult question to answer in

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that have an advantage in hiring are those that create an environment for nurturing data science and data scientists. If you do not have a critical mass of data scientists, be creative. Encourage your data scientists to become part of local data science technical communities and global data science academic communities. A note on publishing Science is a social endeavor, and the

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a very small number of clients to help them develop their data science capabilities (such as Data Scientists, LLC).[76] You can find a large list of data-science service companies, as well as a wide variety of other data science resources, at KDnuggets. A caveat about engaging data science consulting firms is that their interests are not always well

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deal-with issues that can be quickly dispatched, or even written about well as a section or chapter of a data science book. If you are either a data scientist or a business stakeholder in data science projects, you should care about privacy concerns, and you will need to invest serious time in thinking carefully about

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whether the target variable has been defined appropriately for the problem to be solved, etc. Knowing what are the different sorts of data science tasks helps to keep the data scientist from treating all business problems as nails for the particular hammers that he knows well. Thinking carefully about what is important to the

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errors, Regression via Mathematical Functions accuracy (term), Plain Accuracy and Its Problems accuracy results, From Holdout Evaluation to Cross-Validation ACM SIGKDD, Superior Data Scientists, Is There More to Data Science? ad impressions, Example: Targeting Online Consumers With Advertisements adding variables to functions, Example: Overfitting Linear Functions advertising, Example: Targeting Online Consumers With

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Pieces business strategy, Data Science and Business Strategy–A Firm’s Data Science Maturity accepting creative ideas, Be Ready to Accept Creative Ideas from Any Source case studies, examining, Examine Data Science Case Studies competitive advantages, Achieving Competitive Advantage with Data Science–Achieving Competitive Advantage with Data Science, Sustaining Competitive Advantage with Data Science–Superior Data Science Management data scientists, evaluating, Superior Data Scientists–Superior Data Scientists evaluating proposals, Be

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Ready to Evaluate Proposals for Data Science Projects–Flaws in the Big Red Proposal historical advantages and, Formidable

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Historical Advantage intangible collateral assets and, Unique Intangible Collateral Assets intellectual property and, Unique Intellectual Property managing data scientists

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effectively, Superior Data Science Management–Superior Data Science

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Management maturity of the data science

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, A Firm’s Data Science

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Maturity–A Firm’s Data Science Maturity thinking data-analytically for, Thinking Data-Analytically, Redux–Thinking Data-Analytically, Redux C Caesars Entertainment, Data

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Final Words and adding value to applications, Decision Analytic Thinking I: What Is a Good Model? as craft, Superior Data Scientists as strategic asset, Data and Data Science Capability as a Strategic Asset–Data and Data Science Capability as a Strategic Asset baseline methods of, Summary behavior predictions based on past actions, Example: Hurricane Frances Big

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“Big Data” data-driven decision-making, Data Science, Engineering, and Data-Driven Decision Making–Data Science, Engineering, and Data-Driven Decision Making engineering, Data Science, Engineering, and Data-Driven Decision Making–Data Science, Engineering, and Data-Driven Decision Making engineering and, Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist evolving uses for, From Big Data

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development vs., A Firm’s Data Science Maturity structure, Machine Learning and Data Mining techniques, Data Science, Engineering, and Data-Driven Decision Making technology vs. theory of, Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist–Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist understanding, The Ubiquity of Data

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Opportunities, Data Processing and “Big Data” data science maturity, of firms, A Firm’s Data Science Maturity–A Firm’s Data Science Maturity data scientists academic, Attracting and Nurturing Data Scientists and Their Teams as

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scientific advisors, Attracting and Nurturing Data Scientists and Their

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Teams attracting/nurturing, Attracting and Nurturing Data Scientists and

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Their Teams–Attracting and Nurturing Data Scientists and Their Teams evaluating, Superior Data Scientists–Superior Data Scientists managing, Superior Data Science Management–Superior Data Science Management Data Scientists,

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LLC, Attracting and Nurturing Data Scientists and

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Their Teams data sources, Evaluation, Baseline Performance, and Implications for Investments in Data data understanding, Data Understanding–Data Understanding expected value decomposition and, From an Expected Value Decomposition to a Data Science Solution–From

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an Expected Value Decomposition to a Data Science

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Example: Jazz Musicians, Example: Jazz Musicians email, Why Text Is Important engineering, Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist, Business Understanding engineering problems, business problems vs., Other Data Science Tasks and Techniques ensemble method, Bias, Variance, and Ensemble Methods–Bias, Variance, and Ensemble Methods entropy, Selecting Informative Attributes

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Other Analytics Techniques and Technologies big-data, Data Processing and “Big Data” theory in data science vs., Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist–Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist term frequency (TF), Term Frequency–Term Frequency defined, Term Frequency in TFIDF, Combining

Doing Data Science: Straight Talk From the Frontline

by Cathy O'Neil and Rachel Schutt  · 8 Oct 2013  · 523pp  · 112,185 words

. Who knows what you might find? After I got my PhD, I worked at Google for a few years around the same time that “data science” and “data scientist” were becoming terms in Silicon Valley. The world is opening up with possibilities for people who are quantitatively minded and interested in putting their brains

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. Reason 1: I wanted to give students an education in what it’s like to be a data scientist in industry and give them some of the skills data scientists have. I was working on the Google+ data science team with an interdisciplinary team of PhDs. There was me (a statistician), a social scientist, an engineer

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Engineering in July 2012. This course created an opportunity to develop the theory of data science and to formalize it as a legitimate science. Reason 3: I kept hearing from data scientists in industry that you can’t teach data science in a classroom or university setting, and I took that on as a challenge. I

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thought of my classroom as an incubator of data science teams. The students I had were very impressive and are turning into top-notch data scientists. They’ve contributed a chapter to this book, in fact. Origins of the Book The class would not

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implement it yourself and play around with it to truly understand it. Who This Book Is For Because of the media coverage around data science and the characterization of data scientists as “rock stars,” you may feel like it’s impossible for you to enter into this realm. If you’re the type of

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here, which constitute an ongoing discussion of the difference between a statistician and a data scientist. Cosma basically argues that any statistics department worth its salt does all the stuff in the descriptions of data science that he sees, and therefore data science is just a rebranding and unwelcome takeover of statistics. For a slightly different

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. Much of the development of the field is happening in industry, not academia. That is, there are people with the job title data scientist in companies, but no professors of data science in academia. (Though this may be changing.) Not long ago, DJ Patil described how he and Jeff Hammerbacher—then at LinkedIn and

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Facebook, respectively—coined the term “data scientist” in 2008. So that is when “data scientist” emerged as a job title. (Wikipedia finally gained an entry on data science in 2012.) It makes sense to us that once the skill set required to thrive at Google

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social sciences, which could be thought of as a subset of data science. But we can go back even further. In 2001, William Cleveland wrote a position paper about data science called “Data Science: An action plan to expand the field of statistics.” So data science existed before data scientists? Is this semantics, or does it make sense? This all

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begs a few questions: can you define data science by what data scientists do? Who gets to define the field, anyway? There

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specialize in all those things. We’ll talk about this more after we look at the composite set of skills in demand for today’s data scientists. A Data Science Profile In the class, Rachel handed out index cards and asked everyone to profile themselves (on a relative rather than absolute scale) with respect

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expertise Communication and presentation skills Data visualization As an example, Figure 1-2 shows Rachel’s data science profile. Figure 1-2. Rachel’s data science profile, which she created to illustrate trying to visualize oneself as a data scientist; she wanted students and guest lecturers to “riff” on this—to add buckets or remove skills

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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. 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

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Harris, Sean Murphy, and Marck Vaisman based on a survey of several hundred data science practitioners in mid-2012 OK, So What Is a Data Scientist, Really? Perhaps the most concrete approach is to define data science is by its usage—e.g., what data scientists get paid to do. With that as motivation, we’ll describe what

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data scientists do. And we’ll cheat a bit by talking first about data scientists in academia. In Academia The

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” at a university, or for applying for a grant that supplies money for data science research. Instead, let’s ask a related question: who in academia plans to become a data scientist? There were 60 students in the Intro to Data Science class at Columbia. When Rachel proposed the course, she assumed the makeup of

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information to form an opinion. Given that this is how the popular press is currently describing and influencing public perception of data science and modeling, it’s incumbent upon us as data scientists to be aware of it and to chime in with informed comments. With that context, then, what do we mean when

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and making a plan for how the problem will be attacked. And that someone is the data scientist or our beloved data science team. Let’s revise or at least add an overlay to make clear that the data scientist needs to be involved in this process throughout, meaning they are involved in the actual coding

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as in the higher-level process, as shown in Figure 2-3. Figure 2-3. The data scientist is involved in every part of this process Connection to the Scientific Method We can think of the data science process as an extension of or variation of the scientific method: Ask a question. Do background

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), and, as we’ll learn a little later, Kaggle itself. Some remarks about data science competitions are warranted. First, data science competitions are part of the data science ecosystem—one of the cultural forces at play in the current data science landscape, and so aspiring data scientists ought to be aware of them. Second, creating these competitions puts one in

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point: what attributes of the existing competitions capture data science, and what aspects of data science are missing? Finally, competitors in the the various competitions get ranked, and so one metric of a “top” data scientist could be their standings in these competitions. But notice that many top data scientists, especially women, and including the authors of this

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and more about more and more, until you know nothing about everything. — Will Cukierski Kaggle is a company whose tagline is, “We’re making data science a sport.” Kaggle forms relationships with companies and with data scientists. For a fee, Kaggle hosts competitions for businesses that essentially want to crowdsource (or leverage the wider

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data science community) to solve their data problems. Kaggle provides the infrastructure and attracts the data science talent. They also have in house a bunch of top-notch data scientists, including Will himself. The companies are their paying customers, and they provide datasets

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an interview. There were 422 competitors. We think it’s convenient for Facebook to have interviewees for data science positions in such a posture of gratitude for the mere interview. Cathy thinks this distracts data scientists from asking hard questions about what the data policies are and the underlying ethics of the company. Kaggle

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engines, also called recommendation systems, are the quintessential data product and are a good starting point when you’re explaining to non–data scientists what you do or what data science really is. This is because many people have interacted with recommendation systems when they’ve been suggested books on Amazon.com or gotten

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computer science, and got her PhD in information systems at NYU. She now teaches a class to business students on data science, where she addresses how to assess data science work and how to manage data scientists. Claudia is also a famously successful data mining competition winner. She won the KDD Cup in 2003, 2007, 2008

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Claudia started by asking what people’s reference point might be to evaluate where they stand with their own data science profile (hers is shown in Table 13-1. Referring to the data scientist profile from Chapter 1, she said, “There is one skill that you do not have here and that is the

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external communication to represent M6D; and 20% of her time in “leadership” of her data group. On Being a Female Data Scientist Being a woman works well in the field of data science, where intuition is useful and is regularly applied. One’s nose gets so well developed by now that one can smell

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the creators of Hadoop, and Jeff Hammerbacher, who we mentioned back in Chapter 1 because he co-coined the job title “data scientist” when he worked at Facebook and built the data science team there. Cloudera is like Red Hat for Hadoop, by which we mean they took an open source project and built

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, but…” The lectures were discretized in this way, and it was our job to interpolate a continuous narrative about data science. We had to create our own meaning from the course, just as data scientists continue to construct the field to which they belong. That is not to say that Rachel left us in

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the dark. On the first day of class, she proposed a working definition of data science. A data scientist, she said, was a person whose aptitude was distributed over the following: mathematics, statistics, computer science, machine learning, visualization, communication, and domain expertise. We

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members of the class to create an essay-grading algorithm. The homework often simulated the data scientist’s experience in industry, but Kaggle competitions could be described as the dick-measuring contests of data science. It encouraged all of the best data science practices we had learned while putting us in the thick of a quintessential

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of problem you might be assigned in a more general machine learning or data mining class. As fledgling data scientists, our first reaction is now skeptical. At which point in the process of doing data science would a problem like this even present itself? How much would we have to have already done to

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the book. It was the theme of the book, the central question, and the mantra. Data science could be defined simply as what data scientists do, as we did earlier when we talked about profiles of data scientists. In fact, before Rachel taught the data science course at Columbia, she wrote up a list of all the things

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your abilities—give yourself reality checks by making sure you can code what you speak and by chatting with other data scientists about approaches. Thought Experiment Revisited: Teaching Data Science How would you design a data science class around habits of mind rather than technical skills? How would you quantify it? How would you evaluate it

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traction they have. The preceding chapters—dedicated to the means for cultivating the diverse capacities of the data scientist—make mincemeat of any facile dichotomy of the data expert and the traditional expert. Doing data science has put a tempering of hubris, especially algorithmic hubris, at the center of its technical training. Obama’

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thought experiment, Thought Experiment: Meta-Definition privacy and, Privacy process of, The Data Science Process–A Data Scientist’s Role in This Process RealDirect case study, Case Study: RealDirect–Sample R code scientific method and, A Data Scientist’s Role in This Process scientists, A Data Science Profile–Thought Experiment: Meta-Definition sociology and, Gabriel Tarde teams, A

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Data Science Profile Venn diagram of, The Current Landscape (with a Little History) data science competitions, Background: Data Science Competitions Kaggle and, A Single Contestant data scientists, A Data Science Profile–Thought Experiment: Meta-Definition as

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problem solvers, Being Problem Solvers chief, The Life of a Chief Data Scientist defining, A Data Science Profile ethics of, Being an Ethical Data Scientist–Being an Ethical Data Scientist female

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, On Being a Female Data Scientist hubris and, Being an Ethical Data Scientist–Being an Ethical Data Scientist

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in academia, In Academia in industry, In Industry next generation of, What Are Next-Gen Data Scientists?–Being Question Askers questioning as, Being

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Question Askers role of, in data science process, A Data Scientist

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learning Suriowiecki, James, Background: Crowdsourcing Survival Analysis, Example: User Retention T tacit knowledge, Being an Ethical Data Scientist Tarde, Gabriel, Gabriel Tarde, Your Mileage May Vary Idea of Quantification, Gabriel Tarde Taylor Series, In practice teaching data science thought experiment, What Just Happened? test sets, Training and test sets tests, Code readability and reusability

Becoming Data Literate: Building a great business, culture and leadership through data and analytics

by David Reed  · 31 Aug 2021  · 168pp  · 49,067 words

the enterprise by getting the foundations right. Empower – unlocking the value of data to support and empower the business. Beat the market – using AI and data science to take analytics to the next level. Mindset – building a data-driven mindset across the enterprise. Lloyd’s of London, in another example of an

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figure report Compliance KYC manager Identity validation Ecommerce Channel manager Identity validation Information security Information security officer Identity validation Customer experience Cx manager Behavioural modelling Data science Data scientist Behavioural modelling Centralisations of roles, for example into a data and analytics centre of excellence, removes role and task duplication while supporting multiple internal

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analyst Data and analytics centre of excellence Board, Marketing Churn propensity modelling Customer churn analyst Data and analytics centre of excellence Marketing Behavioural modelling Data scientist Data and analytics centre of excellence Cx management Identity validation Information security officer Information security Ecommerce The reality of multi-stakeholder data and analytics tasks

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The market for data practitioners is overheated at almost all levels, leading to salary inflation, which is exaggerated within certain in-demand roles, such as data science or data engineering. If these are broken down into task sets, it may be possible to define a role that does not have the inflationary

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tasks required to avoid duplication of effort. Chapter 3: Becoming data literate Roadmap – in this chapter: Aviva changed its culture to be customer-centric using data science. Data literacy has five dimensions: vision, business strategy, value creation, culture and data foundations. Each of these needs the support of four pillars: strategy,

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in 2016 with the creation of its customer science team as part of a vision to create engaging, relevant customer experiences through intelligent use of data science. A key moment occurred in 2019 when this team realised it needed to make customer data central and relevant to the whole organisation. This

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would enable data science to avoid being just another siloed department by bringing data science to life for colleagues and providing a common language for the business to talk about customers. Of course, data foundations

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on 16m customers covering product holding, demographics and behavioural indicators – the largest customer data set in UK insurance. But as Tom Spencer, head of customer data science at Aviva, notes, the data asset in itself would not lead to customer-centricity. “Essential to the success of this work was close collaboration with

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said. At the level of business strategy, the company wanted to move forward from conventional age- and affluence-based rules models by developing a new data science-led customer segmentation using machine learning (ML). Crucially, says Spencer, “in order to achieve cultural change, the segmentation had to be simple and to

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CDO brings to the table. For business strategy – test-and-learn will become part of the working method across the enterprise, not just within data science and analytics. There will be no fear of failure, but rather a desire to fail fast in order to identify successful strategies. Ensuring engagement by

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own these successes. Once data practitioners get to work examining the available data, they will often uncover contradictory truths. This is especially true of data science if it is given free licence to look at the organisation end-to-end. Few of those business myths survive this examination. It is the

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data scientists were recruited and tasked to “move fast and break things”. Significant changes did start to be realised, including a reduction in the friction during insurance pricing and quotation through predictive modelling based on customer data. Robust data underpinnings and platforms were put in place to support both customer science and data science

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could misinterpret BI if they were not data literate and numerate, for example. But equally, there was a need to get data scientists to understand the commercial business. It had 30 data scientists who needed to make things “painfully simple” for business users, rather than following their natural desire to explain the supporting

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identified a similar need and are moving to create cross-departmental courses that allow everybody to learn, adopt and deploy new data and analytics skills. Data science is a prime example. There is a large appetite from the C-suite down to bring leading-edge techniques, such as AI and ML,

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an understanding of multiple disciplines, including data, coding and mathematics. In DataIQ research, for example, 39.5% of data practitioners identified a weak understanding of data science in their business as the number one challenge they face. But these concepts are teachable when broken down into digestible components that build towards a

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aspect of learning and development. NatWest Group started its data academy – the first one to be launched by a UK bank – initially with a data science course, working with the Bayes Centre in Edinburgh to build a community of practice across the group. It set a clear goal of developing techniques

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pathways to allow it to drive innovation and be at the leading edge of disruptive data trends. To do this, it needed to move data science higher up the value chain by training its analysts to automate basic tasks, deliver self-service data tools and support the rollout of AI initiatives

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, it is better to accept an estimated figure than to fail to agree and miss out on the eventual benefit being seen as stemming from data science. While these metrics provide departmental-level indicators of productivity, the data leader will also need to measure individual-level output. A growing method for

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governance roles, for example. These individuals are ‘force multipliers’ since their actions help to enhance what other team members are able to achieve. A data scientist may be heavily reliant on a data engineer to deliver a working model into the production environment – their individual outputs are very different, but mutually

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tend to have well-established graduate programmes. Recently, some of these have adapted to new skills requirements in the data department, such as the data science graduate scheme at a global bank which is now in its second year and offers a combination of experience across customer science, ML and data

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technology department or as part of the compliance team and has no direct line to value creation – this takes place inside advanced analytics or data science functions that operate within a host department. It is also the case that a data practitioner drawn to this type of CDO role is unlikely

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may sit separately within lines of business, such as data engineering being part of IT, or as stand-alone functions, such as advanced analytics or data science. According to DataIQ research, while 34.5% of organisations have a centralised data department, the same percentage have federated data while 13.8% operate

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hub-and-spoke basis. That means nearly half of data teams (48.3%) could find themselves working alongside stakeholders rather than their own peers. Data science operates in a more consolidated way with 40.7% having a centralised function and 26.7% a team working within a centralised analytics function (see

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Figure 7.1). Figure 7.1: How is data science set up in your organisation? Whatever approach has been taken to the data department and its component functions, the data leader is presented with a

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creation for the organisation. Data can appear to its internal customers as something of a ‘black box’, especially the work of functions like analytics and data science which generate models through an invisible, quasi-magical process. The level of communication with stakeholders has a significant impact on this, especially if data teams

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is true only if the audience for what scientists produce is only other scientists and then only if they specialise in the same domain. For data scientists – indeed, for all analysts and everybody working in data – the audience will most often be non-technical, business-oriented. These stakeholders may be highly

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insights digestible, winning belief and support even when what they have to say does not want to be heard. As Tom Spencer, head of customer data science at Aviva, says: “Keep communication clear and concise. In a technical field, it’s all too tempting to fall into jargon and complexity. Senior

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While the data department does not operate in the same way as R&D, it can still incubate pioneering solutions, especially if it houses a data science function and allows a proportion of time to be given over to free exploration. One major initiative that can support this is a partnership with

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academia, especially by building links with universities that run analytics, data science or related courses. These have expanded significantly in number in recent years and a key feature of most is an appetite for access to real

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funding for a new university in Milton Keynes, which will have 5,000 students and be the UK’s first university focused on digital and data science skills. Similarly, the University of Cambridge has established the Cambridge Centre for Data-Driven Discovery (C2D3), a hub intended to harness the knowledge of

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academics in data science, ethics and a wide range of disciplines. C2D3 will allow experts across a number of areas, including those with technical, mathematical and topic knowledge, to

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spectrum of skills is essential, not just specific technical abilities. A personal brand gives credibility, visibility and accessibility to data practitioners. Data individuals – especially data scientists – can be at the heart of digital transformations, but cultural obstacles can prevent this from happening. To continue to develop as individuals (and as a

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data native at the point where it starts a digital transformation (or has one thrust upon it). DataIQ researched the issue of how to get data science – often a key innovator that drives digital transformation – transferred into production. What the survey revealed is that culture factors make up three out of

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five of the biggest challenges identified. Significantly, a lack of understanding of data science within the business emerged as the leading challenge, even above legacy infrastructure (39.5% vs 38.4% – see Figure 8.2). This underlines just

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it is for data teams to demonstrate the art of the possible to stakeholders, as discussed in Chapter 7. Similarly, the issue of conflict between data science and IT, as identified by 33.6%, shows how difficult it is to establish data as an accepted and engaged department. Assuming, that is,

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that enough data practitioners can be recruited, trained and retained – some 32.7% struggle to staff up their data science function. Again, this is where a positive data culture helps because it makes the organisation more appealing to candidates and incumbent practitioners. Figure 8.2

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: What are your biggest data science challenges? Becoming a data champion One way to avoid these obstacles to getting data accepted as an enabler of digital transformation is by becoming a

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identify since automation, AI, ML and the rest all require significant stretch by both data practitioners and the organisation. Recalling the challenges identified around data science in Figure 8.2, there is still a lot of work to be done in bringing these tools into the production environment. What is often

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potential stakeholders. Should I do a Masters? A notable feature of recent years has been the emergence of formal, academic qualifications in data (including data science). It is now possible to build on degree-level technical skills, re-skill or up-skill from non-technical disciplines, or validate abilities that have

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and analytics enjoyed a relatively prolonged honeymoon phase during the 2010s and some functions, such as data science, still do. In DataIQ research in H2 of 2020, 4.9% of organisations said they were still running data science as a cost centre, for example. By contrast, in a LinkedIn post in January 2021,

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his team. Just as significantly, the same DataIQ study found 29.1% of organisations did not know what level of revenue uplift to expect from data science. At some point, all investments come under scrutiny and need to prove their worth, with the CFO likely to want to see a significant

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in 2013 by Michal Kosinski, then deputy director of the University of Cambridge Psychometric Centre, revealed just what had become possible when ‘big data’ met data science. He started with the research question of whether psychological traits – in particular the ‘big five’ personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) together with

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one of his peers – Aleksandr Kogan – sold a similar solution to a then-nascent business called Cambridge Analytica which specialised in applying big data and data science to political campaigns. (Kosinski demurred from having his own work applied in the political realm on principle.) As is now only too familiar, this

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and over 2,900 other orders for critical services during April and May 2020 alone. Transport for London (TfL): TfL’s transport analytics and data science team is instrumental in providing analysis of travel patterns on the public and private transport network. During the Covid-19 crisis, it provided analysis of

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, or that movie rental is probably not a great industry to build a career in right now, or that you have a master’s in data science. Honestly, I have no idea what you saw in that job in the first place, Sarah. Once you learn and strengthen your ability to use

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was privileged—his dad still sent him checks every month to cover his living expenses and tuition. Before Covid, he had worked part time doing data science for a professor to help cover his student loans, but now he was mostly on the parental dole. He knew there were a lot of

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The Outside In.” Science 292: 1284-1286. Jansen, R., H. Yu, et al. (2003). “A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data.” Science 302: 449-453. Jin, M., M. Blank, et al. (2000). “ERK1/2 Phosphorylation, Induced by Electromagnetic Fields, Diminishes During Neoplastic Transformation.” Journal of Cell Biology

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