
Following my article on Switching Career to Data Science in your 30s, many readers asked me for more details. Especially on how to get started. Despite the sea of information, it can be daunting to navigate through hundreds of videos, courses, and articles. As a result, you feel stuck and might lose your motivation. Data science, machine learning and Artificial Intelligence are exciting topics, but we need to walk before we can run.
Finding someone who understands where you are in your journey is not easy. The majority of authors and YouTubers assume that all you want is something as straightforward as a Python tutorial. However, as beginners, I believe most of us have two kinds of concerns when learning Data Science:
- Direct concerns. Learning the practical aspects of Data Science, such as coding in Python, understanding how to use NumPy and getting started with Kaggle. The direct concern is, by far, the most covered on the internet and you will have no problem finding reliable sources.
- Indirect concerns. Here, we have more than questions: we have doubts, insecurity and fear of significant changes. Why? If you are over 30 and has committed to a new career, then you want to feel confident that Data Science is right for you. After all, your already have enough concerns, such as your full-time job, making financial decisions, or even having a family.
Very few experts appreciate what it is like to start from the beginning while dealing with your personal challenges. So, this is one of the reasons you will find several tutorials teaching the technical aspect of Data Sciences. That is alright, don’t get me wrong. Their videos and articles are helpful and you can learn a lot from them: I do.
However, after years of hard work, it’s easy for experts to forget how beginners find overwhelming all that information. Their lapse of memory is a cognitive bias that psychologists call the curse of knowledge [1]. Some professionals are so advanced in their careers or incredibly skilled that they take for granted what it was like in the beginning. As a consequence, they often suggest unrealistic ideas for those who are still dipping their toes on the water.
With that in mind, don’t immediately commit to a paid course or spend hours in front of the computer with an empty Jupyter Notebook. Try answering whether Data Science is for you. Though you will never be ‘100% sure’ it is worth aiming at least more than chance (50/50). Giving both indirect and direct aspects, I propose an alternative, a safer way to get started: expose and familiarize yourself with the field before making long-term commitments.
Familiarize and expose yourself
There are multiple ways to connect with Data Science, but here is a list of books that will expose yourself to key ideas and concepts before you get into the action. These books are accessible to readers of all backgrounds who are interested in Data Science. You will gain invaluable information to help you with indirect concerns, such as:
- ‘Is Data Science for me?’
- ‘Do I enjoy statistics?’
- ‘Do I see myself working as a Data Scientist?’
Put yourself in the shoes of someone who is involved in in the field. Live the life of a data scientist, a Machine Learning engineer or an artificial intelligence researcher. Instead of signing up for a bootcamp, why not read a few books? As the author of Game of Thrones and A Dance with Dragons said:
"A reader lives a thousand lives before he dies . . . The man who never reads lives only one." – George R.R. Martin
Best Data Science books for beginners
Live the life of a data professional. So, before you dig in, remember to make yourself a cup of tea, relax and enjoy these brilliant books. Live the life of a data professional.
1) Machine Learning revised and updated edition (The MIT Press Essential Knowledge series) by Ethem Alpaydin.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition – as well as some we don’t yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of the new AI. This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.
Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)
2) The Art of Statistics: Learning from Data by David Spiegelhalter.
I do not want to create any bias, but this is one of my favourite books. David, a professor at the University of Cambridge, does a brilliant job in guiding the reader through the essential principles in statistics required to create knowledge. He uses simple examples that are easy to understand, especially if you are not familiar with statistics. Science could not evolve without statistics; neither can any serious Data Scientist. This is one of the best stats book for Data Science professionals. A must-read.
BY David Spiegelhalter The Art of Statistics Learning from Data (Pelican Books) Paperback – 13 Feb…
3) Code: The Hidden Language of Computer Hardware and Software by Charles Petzold.
Although Code was published in 2000, it is still relevant to anyone who is about to start learning how to code. Charles explains how humans manipulate language: from creating different ways of communication to current day automation. Code will help you understand the logic behind Programming languages, regardless of which language you decide to learn: a vital skill for beginners.
4) Data Science Jobs: How to Become a Data Scientist by Przemek Chojecki.
Now is the right time to make a career change and become a Data Scientist. In this practical book, one of the Forbes 30 under 30, Przemek will guide you through the process of working with multiple companies as a Data Scientist consultant. You will get a glimpse of the hiring process and what it is like to build Data Science teams. Also, there is valuable information on what you need to land your first job as a Data Scientist and analysts. If you want to know more about the job market, then this is the best Data Science book for beginners.
5) Mastery by Robert Greene.
Mastery is a bonus book. It is perfect for those switching career from a completely unrelated field to Data Science. Although anyone would benefit from reading Greene’s best-selling book, Mastery is particularly relevant for people without programming skills. It shouldn’t come as a surprise that once you begin the Data Science journey, you will have to get good at coding. This is because the competition is high and there are a lot of outstanding Data Scientists. Robert Greene will teach you the steps required to become a master in any field – literally. Mastery is the best gift anyone could receive before starting a new career. I wish I had read Mastery eighteen years ago.
Mastery: Robert Greene (The Modern Machiavellian Robert Greene)
Conclusion
If you are an aspiring Data Scientist, I am sure you have come across different sources of information on where or how to begin your journey. Like any other career, Data Science also requires long hours of practice until you become good at it. But, some of us have questions that go beyond what a basic YouTube tutorial can offer. For those people, it can be frustrating to invest both money and time in a Masters degree, and later find out that they did not enjoy as much as expected. So here are two introductory courses you might find it useful – Intro to Python and Intro to Statistics and Probability.
In this article, I have suggested a few books which are useful to get your head around the topic without committing to a long-term course, like bootcamps or a graduate degree. Once you read these books, you will have gained enough information to decide whether Data Science is for you. I hope it is.
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References:
[1] The Psychologist at https://thepsychologist.bps.org.uk/volume-27/december-2014/cursed-knowledge