Confident Data Skills

Master the Fundamentals of Working with Data and Supercharge Your Career

Kirill Eremenko

• Publisher: Kogan Page
• Year: 2018
• ISBN:
  9780749481551
• Edition: 1
• Topic: Computer Science
• Subtopic: Databases
• Index: Computer Science

Data science is the most exciting skill you can master. Data has dramatically changed how our world works. From entertainment to politics, from technology to advertising and from science to the business world, data is integral and its only limit is our imagination. If you want to have a vibrant and valuable professional life, being skilled with data is the key to a cutting-edge career. Learning how to work with data may seem intimidating or difficult but with Confident Data Skills you will be able to master the fundamentals and supercharge your professional abilities. This essential book covers data mining, preparing data, analysing data, communicating data, financial modelling, visualizing insights and presenting data through film making and dynamic simulations.

In-depth international case studies from a wide range of organizations, including Netflix, LinkedIn, Goodreads, Deep Blue, Alpha Go and Mike's Hard Lemonade Co. show successful data techniques in practice and inspire you to turn knowledge into innovation. Confident Data Skills also provides insightful guidance on how you can use data skills to enhance your employability and improve how your industry or company works through your data skills. Expert author and instructor, Kirill Eremenko, is committed to making the complex simple and inspiring you to have the confidence to develop an understanding, adeptness and love of data.

PART ONE

‘What is it?’ Key principles

With all the attention given to the apparently limitless potential of technology and the extensive opportunities for keen entrepreneurs, some may ask why they should bother with data science at all – why not simply learn the principles of technology? After all, technology powers the world, and it shows no signs of slowing down. Any reader with an eye to their career might think that learning how to develop new technologies would surely be the better way forward.

It is easy to regard technology as the force that changes the world – it has given us the personal computer, the internet, artificial organs, driverless cars, the Global Positioning System (GPS) – but few people think of data science as the propeller behind many of these inventions. That is why you should be reading this book over a book about technology; you need to understand the mechanics behind a system in order to make a change.

We should not consider data only as the boring-but-helpful parent, and technology as the stylish teenager. The importance of data science does not begin and end with the explanation that technology needs data as just one of many other functional elements. That would be denying the beauty of data, and the many exciting applications that it offers for work and play. In short, it is not possible to have one without the other. What this means is that if you have a grounding in data science, the door will be open to a wide range of other fields that need a data scientist, making it an unusual and propitious area of research and practice.

Part I introduces you to the ubiquity of data, and the developments and key principles of data science that are useful for entering the subject. The concepts in the three chapters will outline a clear picture of how data applies to you, and will get you thinking not only about how data can directly benefit you and your company but also how you can leverage data for the long term in your career and beyond.

Striding out

Chapter 1 will mark the beginning of our journey into data science. It will make clear the vast proliferation of data and how in this Computer Age we all contribute to its production, before moving on to show how people have collected it, worked with it and crucially how data can be used to bolster a great number of projects and methods within and outside the discipline.

We have established that part of the problem with data science is not its relative difficulty but rather that the discipline is still something of a grey area for so many. Only when we understand precisely how much data there is and how it is collected can we start to consider the various ways in which we can work with it. We have reached a point in our technological development where information can be efficiently collected and stored for making improvements across all manner of industries and disciplines – as evidenced in the quantity of publicly available databases and government-funded projects to aggregate data across cultural and political institutions – but there are comparatively few people who know how to access and analyse it. Without workers knowing why data is useful, these beautiful datasets will only gather dust. This chapter makes the case for why data science matters right now, why it is not just a trend that will soon go out of style, and why you should consider implementing its practices as a key component of your work tasks.

Lastly, this chapter details how the soaring trajectory of technology gives us no room for pause in the field of data science. Whatever fears we may have about the world towards which we are headed, we cannot put a stop to data being collected, prepared and used. Nevertheless, it is impossible to ignore the fact that data itself is not concerned with questions of morality, and this has left it open to exploitation and abuse. Those of you who are concerned can take charge of these developments and enter into discussion with global institutions that are dealing with issues surrounding data ethics, an area that I find so gripping that I gave it its own subsection in Chapter 3, The data science mindset.

The future is data

Everything, every process, every sensor, will soon be driven by data. This will dramatically change the way in which business is carried out. In 10 years from now, I predict that every employee of every organization in the world will be expected to have a level of data literacy and be able to work with data and derive some insights to add value to the business. Not such a wild thought if we consider how, at the time of this book’s publication, many people are expected to know how to use the digital wallet service Apple Pay, which was only brought onto the market in 2014.

Chapter 2, How data fulfils our needs, makes clear that data is endemic to every aspect of our lives. It governs us, and it gathers power in numbers. While technology has only been important in recent human history, data has always played a seminal role in our existence. Our DNA provides the most elementary forms of data about us. We are governed by it: it is responsible for the way we look, for the shape of our limbs, for the way our brains are structured and their processing capabilities, and for the range of emotions we experience. We are vessels of this data, walking flash drives of biochemical information, passing it on to our children and ‘coding’ them with a mix of data from us and our partner. To be uninterested in data is to be uninterested in the most fundamental principles of existence.

This chapter explains how data is used across so many fields, and to illustrate this I use examples that directly respond to Abraham Maslow’s hierarchy of needs, a theory that will be familiar to many students and practitioners in the field of business and management. If this hierarchy is news to you, don’t worry – I will explain its structure and how it applies to us in Chapter 2.

Arresting developments

The final chapter in Part I will show how those new to data science can reshape their mindset to enter the subject, and reveal the areas that I believe show the most potential for immediate engagement with the discipline. Many of the developments made within the field have had knock-on effects on other subjects, and have raised questions about the future for data scientists as well as for scholars and practitioners beyond its disciplinary boundaries. If you are looking to develop your career in data science, this chapter could even fire up some ideas for niches within which you may already work.

To add further weight to the examples offered in Chapter 2 that show compelling arguments for data’s supportive role across many walks of life, in Chapter 3 I also give you some critical approaches that you can use to get yourself started as a practitioner. We might think that its wide application will make data difficult to penetrate, but learning data science is much easier than becoming proficient in many other scientific disciplines. You do not need to be a life learner to master the principles of data science. What you really need is an ability to think about the various ways in which one or more questions – about business operations, about personal motivations – might be asked of data. Because data scientists are there to examine the possibilities of the information they have been given. You may be surprised to know that you already have some skills and experience that you can leverage in your journey to mastering the discipline.

Having said that, due caution is necessary for newcomers. Anyone who has used Excel, worked in an office environment or taken a subject comprising scientific components at university will probably have already come across data in their professional or academic lives. But some of the methods for using data that you may have picked up will be inefficient, and holding true to what you know may prevent you from learning the most effective ways of exploiting datasets: we will discuss this in detail in Parts Two and Three.

Despite the clear positive effects of data, it is also important not to be blinded by it. Thus, Chapter 3 also addresses the various security threats that data can pose to its users, and how data practitioners are working to address present and future issues that may arise. Data ethics is a particularly compelling area to make note of, as it holds the power to alter and direct future developments in data science. From what we understand of information collection, to the extent to which it can be used within machines and online, data ethics is setting the stage for how humans and technology communicate. When you read this chapter, consider how each of the areas might tie in with the way you work, and how beneficial further investment in the topic might be for your business.

01

Defining data

Think about the last film you saw at the cinema. How did you first hear about it? You might have clicked on the trailer when YouTube recommended it to you, or it may have appeared as an advertisement before YouTube showed you the video you actually wanted to see. You may have seen a friend sing its praises on your social network, or had an engaging clip from the film interrupt your newsfeed. If you’re a keen moviegoer, it could have been picked out for you on an aggregate movie website as a film you might enjoy. Even outside the comfort of the internet, you may have found an advertisement for the film in your favourite magazine, or you could have taken an idle interest in the poster on your way to that coffeehouse with the best Wi-Fi.

None of these touchpoints was coincidental. The stars didn’t just happen to align for you and the film at the right moment. Let’s leave the idealistic serendipity to the onscreen encounters. What got you into the cinema was less a desire to see the film and more of a potent concoction of data-driven evidence that had marked you out as a likely audience member before you even realized you wanted to see the film.

When you interacted with each of these touchpoints, you...