Vincent Granville - Developing Analytic Talent: Becoming a Data Scientist

Sometimes, understanding what something is includes having a clear picture of what it is not . Understanding data science is no exception. Thus, this chapter begins by investigating what data science is not, because the term has been much abused and a lot of hype surrounds big data and data science. You will first consider the difference between true data science and fake data science. Next, you will learn how new data science training has evolved from traditional university degree programs. Then you will review several examples of how modern data science can be used in real-world scenarios.

Finally, you will review the history of data science and its evolution from computer science, business optimization, and statistics into modern data science and its trends. At the end of the chapter, you will find a Q&A section from recent discussions Ive had that illustrate the conflicts between data scientists, data architects, and business analysts.

This chapter asks more questions than it answers, but you will find the answers discussed in more detail in subsequent chapters. The purpose of this approach is for you to become familiar with how data scientists think, what is important in the big data industry today, what is becoming obsolete, and what people interested in a data science career dont need to learn. For instance, you need to know statistics, computer science, and machine learning, but not everything from these domains. You dont need to know the details about complexity of sorting algorithms (just the general results), and you dont need to know how to compute a generalized inverse matrix, nor even know what a generalized inverse matrix is (a core topic of statistical theory), unless you specialize in the numerical aspects of data science.

Books, certificates, and graduate degrees in data science are spreading like mushrooms after the rain. Unfortunately, many are just a mirage: people taking advantage of the new paradigm to quickly repackage old material (such as statistics and R programming) with the new label data science.

Expanding on the R programming example of fake data science, note that R is an open source statistical programming language and environment that is at least 20 years old, and is the successor of the commercial product S+. R was and still is limited to in-memory data processing and has been very popular in the statistical community, sometimes appreciated for the great visualizations that it produces. Modern environments have extended R capabilities (the in-memory limitations) by creating libraries or integrating R in a distributed architecture, such as RHadoop (R + Hadoop). Of course other languages exist, such as SAS, but they havent gained as much popularity as R. In the case of SAS, this is because of its high price and the fact that it was more popular in government organizations and brick-and-mortar companies than in the fields that experienced rapid growth over the last 10 years, such as digital data (search engine, social, mobile data, collaborative filtering). Finally, R is not unlike the C, Perl, or Python programming languages in terms of syntax (they all share the same syntax roots), and thus it is easy for a wide range of programmers to learn. It also comes with many libraries and a nice user interface. SAS, on the other hand, is more difficult to learn.

To add to the confusion, executives and decision makers building a new team of data scientists sometimes dont know exactly what they are looking for, and they end up hiring pure tech geeks, computer scientists, or people lacking proper big data experience. The problem is compounded by Human Resources (HR) staff who do not know any better and thus produce job ads that repeat the same keywords: Java, Python, MapReduce, R, Hadoop, and NoSQL. But is data science really a mix of these skills?

Sure, MapReduce is just a generic framework to handle big data by reducing data into subsets and processing them separately on different machines, then putting all the pieces back together. So its the distributed architecture aspect of processing big data, and these farms of servers and machines are called the cloud .

Hadoop is an implementation of MapReduce, just like C++ is an implementation (still used in finance) of object oriented programming. NoSQL means Not Only SQL and is used to describe database or data management systems that support new, more efficient ways to access data (for instance, MapReduce), sometimes as a layer hidden below SQL (the standard database querying language).

There are other frameworks besides MapReduce for instance, graph databases and environments that rely on the concepts of nodes and edges to manage and access data, typically spatial data. These concepts are not necessarily new. Distributed architecture has been used in the context of search technology since before Google existed. I wrote Perl scripts that perform hash joins (a type of NoSQL join, where a join is the operation of joining or merging two tables in a database) more than 15 years ago. Today some database vendors offer hash joins as a fast alternative to SQL joins. Hash joins are discussed later in this book. They use hash tables and rely on name-value pairs . The conclusion is that MapReduce, NoSQL, Hadoop, and Python (a scripting programming language great at handling text and unstructured data) are sometimes presented as Perls successors and have their roots in systems and techniques that started to be developed decades ago and have matured over the last 10 years. But data science is more than that.

Indeed, you can be a real data scientist and have none of these skills. NoSQL and MapReduce are not new concepts many people embraced them long before these keywords were created. But to be a data scientist, you also need the following:

• Business acumen
• Real big data expertise (for example, you can easily process a 50 million-row data set in a couple of hours)
• Ability to sense the data
• A distrust of models
• Knowledge of the curse of big data
• Ability to communicate and understand which problems management is trying to solve
• Ability to correctly assess lift or ROI on the salary paid to you
• Ability to quickly identify a simple, robust, scalable solution to a problem
• Ability to convince and drive management in the right direction, sometimes against its will, for the benefit of the company, its users, and shareholders
• A real passion for analytics
• Real applied experience with success stories
• Data architecture knowledge
• Data gathering and cleaning skills
• Computational complexity basics how to develop robust, efficient, scalable, and portable architectures
• Good knowledge of algorithms

A data scientist is also a generalist in business analysis, statistics, and computer science, with expertise in fields such as robustness, design of experiments, algorithm complexity, dashboards, and data visualization, to name a few. Some data scientists are also data strategists they can develop a data collection strategy and leverage data to develop actionable insights that make business impact. This requires creativity to develop analytics solutions based on business constraints and limitations.