In this book, we have seen what the field of data science entails and how the profession of the data scientist came to be. We described what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity and veracity. We also looked into the different types of data scientists and the skill-sets of each one. We dug into what the role of the data scientist requires in terms of the relevant mindset, technical skills, experience and how he connects to other people. We also zoomed in on the daily life of a data scientist, examining the problems he may encounter and how he tackles with them, what programs he uses and how he expands his knowledge and know-how. We then looked into how you can become a data scientist based on where you are starting from: a programming, machine learning, data-related or student background. Moreover, we went step-by-step through the process of landing a data scientist job: where you need to look, how you would present yourself to a potential employer and what it takes to follow a freelancer path. Finally, we looked at case studies of experienced and senior-level data scientists in an attempt to get a better perspective of what this role is in practice.

Now it is your turn to put all this knowledge to good use. Whether you are opting for a position in a large organization or planning to work as a freelancer, you have a lot of interesting and educational challenges in front of you. This is practical knowledge that cannot fit in a book. Just remember to stay current on what is happening in the data science field so that you always remain competitive. Enrich your toolbox and knowledge-base constantly; good places to start are the websites, articles and books that are listed in the appendices. The book’s glossary can also be used as a hands-on reference for a variety of relevant terms.

The data science field is still in its toddler years, and few are those who are perceptive enough to foresee its potential. As distributed computing gains more ground, data storage becomes cheaper, data transfer becomes faster and, most importantly, people begin reaping the fruits of big data, we should expect it to become a big part of our everyday lives. This should lead to data science becoming a major profession in the not-so-distant future. And as big data technology continues to evolve, more and more interesting ways of making use of existing data will become available. The data scientist will continue to be an ever-fascinating role that will rely as much on creativity as it does on technical skills. By then, there will probably be university departments specializing in this field, and future data scientists will look back on the data scientists of this decade, the pioneers of the field, with great admiration.