Networking is a very important aspect of being a data scientist, especially if you are in the initial stages of your career. You never know how a professional acquaintance can be of use to you in your life in data science. This is because this field is interconnected with other professions and its reputation has grown significantly over the past few years, making others (especially business people) more interested in meeting and connecting with people in this intriguing field. Many data scientists have already started taking advantage of this trend by attending networking groups dedicated to data science topics.

In this chapter, we will examine what networking for a data scientist entails, how it is different from other professionals’ networking and how it can hone relationships that are essential for his career (namely within academia and the business world). It will not touch the topic of how networking is employed for pursuing a data scientist job, however, as this subject will be covered in detail in Chapter 13.

7.1 More than Just Professional Networking

For a data scientist, networking is an integral part of his job, enabling him to learn more about techniques, tools and other things he ought to know in order to be a better data scientist. It is an educational opportunity that he cannot afford to neglect, considering the pace at which things are moving in the data science field. It is also possible that through such meetings he may find a mentor, which can be very beneficial, especially in the initial stages of his career.

Networking is also a chance for the data scientist to connect with business people, not just for finding out about business opportunities, but to gain a better understanding of how the business world is faring and how data science contributes to it. This is not a simple task since it involves talking to many different people in various industries to get a solid and reliable first-hand understanding of the matter, forming his own opinions about it and possibly his own solutions to the problems that are out there. Also, the language that business people use is quite different than that used by tech geeks, so it takes a bit of getting used to this kind of networking.

In addition to these more tangible benefits, networking can help you develop a professional way of presenting yourself to others. You may have heard of the “elevator speech,” which was introduced for screenplay writers hoping to secure a deal for their stories by bumping into a producer in the elevator of the company they worked at. During the one minute or so of the elevator ride, they had to be able to present their story idea clearly, make that person interested in their idea and sketch the main benefits of this idea for that person (i.e. how the story’s originality can help the producer’s bottom line). Networking has similar opportunities, but you are promoting yourself as a data scientist.

Networking can be crucial for a data scientist’s project. Normally he would have a team of his own, but it is not uncommon to look for collaborators or business partners. Since people work better with people with whom they can communicate well, networking can be a filtering process for commencing a data science project. By presenting yourself well, you give yourself the opportunity to be a potential recruit.

7.2 Relationship with Academia

A data scientist is not an academic, but that does not mean that he shouldn’t have a connection with academia. Whether he hangs out with academics in order to discuss the latest innovations in fields of common interest (e.g., machine learning, parallel computing, etc.) or takes classes at a local university, he should maintain a relationship with the academic world.

Data scientists understand the language of science, which enables them to communicate effectively with researchers, ask interesting questions and even think of potential applications of state-of-the-art research in the researcher’s field. The academics who develop research projects are familiar enough with their field to propose their own applications, and it is not unusual for some of them to have connections with the industry (usually to supplement their relatively limited income). The data scientist is similar to this type of academic and can benefit professionally from a mutually beneficial relationship – the data scientist learns about the latest innovations, while the academic learns about industry problems that he can investigate scientifically. However, for this relationship to develop, it is usually the data scientist who has to take the first step by networking at conferences, workshops and other events open to both academics and other professionals. These events may be more costly than reason would dictate, but for someone who is serious about data science, it may be worth the investment.

Networking can yield a lot of collaboration potential that, in turn, can provide an edge to the company the data scientist works for. Think about how the large companies of the Western world have benefitted from such an approach. Using cutting-edge know-how in an industry can provide an enormous boost, if done properly. The data scientist can contribute significantly towards such an objective by, for example, getting involved in a research project and learning about new research trends first-hand, then introducing them to his organization. This kind of project takes a lot of time (a typical journal paper may take one to two years until it is finally printed) and it is not uncommon for there to be a monetary cost involved. (Yes, in the academic world you often need to pay to get your stuff published, especially if it’s in a worthwhile journal!) However, if you are part of a team, these issues are mitigated by the limit of your contribution, keeping all the frustratingly painful academic bureaucracy at a healthy distance. Sounds like an interesting option to consider, doesn’t it?

7.3 Relationship with the Business World

A data scientist should extend networking efforts to the business world, too. The most notable benefit of this strategy is that you become intimately familiar with real-world problems, what various industries are in need of and how other professionals are tackling the data-related challenges of the real world. The business world can also keep you grounded. It is very easy to get carried away and let your mind wander in the fascinating gardens of mathematical modeling, data analysis theories and the like, but these things won’t pay any bills. The data scientist always needs to keep in mind what all his work is for, to keep up to date about the latest developments in the business arena and to think of potential applications of big data in this setting. Business networking supports these objectives.

A very intriguing aspect of networking in the business world is the potential for strategically beneficial business opportunities. These are not just job opportunities, which will be covered later in the book (Chapter 13), but other opportunities such as the creation of a new group, collaborations with other professionals on an independent project and even business research partnerships. The key thing here is to be open and think outside the box, something that should come naturally if you express your data scientist side.

One important point to remember when engaging in business networking is to keep the technical jargon to a minimum and show genuine interest in what other people are doing. We’ll look into this in more detail in Chapter 14, where various self-presentation tactics will be discussed.

7.4 Key Points

• Networking is a very important aspect of being a data scientist, especially in the initial stages of your career.
• Networking can help you develop your communication skills and adapt yourself to approach different types of people, something essential in the data scientist’s work.
• Networking can be an invaluable source of useful knowledge about the latest innovations related to the data science field or other fields that are adjacent to it.
• A data scientist should maintain a healthy relationship with academia, through networking, to keep himself updated with the latest advances and for potentially beneficial partnerships.
• A data scientist needs to remain grounded by maintaining contact with the business world through networking. This can help him gain a better understanding of what is needed, about new potential applications of big data and interesting business opportunities that are not limited to job openings.