Becoming a Data Scientist

Why Become a Data Scientist

So, the first question to ask, is why do you want to become a data scientist?

The world of technology is fast paced, with new languages, tools, methodologies, and approaches being devised and made mainstream all the time. Therefore, isn’t data science just the next new fad that while it is currently the latest and coolest thing to do in technology, will eventually be replaced by something else.

The role of data scientist marks a fundamental shift in the way we build technology. Previously we wrote specifications, designed application, systems and algorithms, understood exactly the functionality that was required – and then build the IT systems using these requirements documents.

With the recent success of machine learning, we now have a very different approach. We can create algorithms using data rather than coding them specifically. This is not like a new computer language or tool; this is a fundamental shift in our approach to building technology and algorithms. We have been able to demonstrate our ability to create functioning algorithms that do things we simply would not be able to define and specify using our old approach of writing requirements documents.

As a technologist, I have always been an advocate of understand the data that is managed by the IT systems and applications. If you don’t understand what the underlying data represents, you are likely to make mistakes in how you use and interpret the data. So having technology roles such as data scientist is a really good idea, given how things have progressed over the last 5 to 10 years.

However, every role changes with the advancement of technology. The role of a data scientist will also change over the coming years. Therefore, it is important to understand the full set of skills required to be a data scientist, which is discussed in more detail in the last chapter.

We must also acknowledge at the start of this book that there are different types of data scientist. Some data scientists will have a PhD in Machine Learning and will want to pursue a career in pure or applied research, creating new ML architectures, training algorithms or approaches that helps to move the whole industry forward in our ambition to create artificial general intelligence. Others might have an undergrad or MSc degree in data science and looking to apply their academic knowledge on specific industry sectors and help develop new applications. Some might have no formal training or academic grounding in Machine Learning but are keen to learn how to apply many of the existing types of ML to specific problems.

We need all levels of data scientist, and it really doesn’t matter where you start, as long as you are keen to learn and experiment, you will no doubt have a wonderful career as a data scientist.