Write Like a Data Scientist. Read on to find out why and how
By: Ryan Joshua Mahindapala
Tags: Writer, Data Science, Consistency, Problem Solving, Learning

Write Like a Data Scientist

Photo by Mika Baumeister on Unsplash

Definition

Data science is about infrastructure, testing and learning to enable better decision making. The data analysis process is aimed at, inter alia, predictive modelling, reducing inefficiencies and operational blockages, enhancing performance measurement capabilities and maximisation of profits. Essentially, it forms the backbone of strategic and sound decision making. It is and has been the driving force behind innovation and research in many fields.

Applicability to writing

What is writing all about? Writing is a form of communication. It is a way for you to express your opinion, ideas and thoughts about a particular issue. An effective writer will present ideas and arguments in a manner that is succinct and clear in its form and approach. This is done by organizing, prioritizing and presenting information in a concise manner.

What do data scientists do? Data scientists share knowledge. They gather large amounts of data, makes sense of it and presents it to the world in a way that is easy to understand and digest. They do this because chances are, readers out there have not come across such information.

Hence, the job of a writer and that of a data scientist is similar. Both wish to educate the reader and aspire to do so by accurately and effectively communicating information they’ve seen to someone who does not fully understand the information.

Storytelling

Data scientists are skilled in analysing large amounts of data, making sense of it and telling a story with it. Such skills also will serve you well as a writer, explaining why thinking like a data scientist will help you improve as a writer. By providing people with a story to go along with data, you give the data meaning and that is the eventual outcome of any writer. To be able to write a piece of work that is meaningful and that will resonate with readers. A piece of writing that you do is a data set that has to be pieced together in the best possible way to paint a picture that is not only clear and understandable but also enticing to the reader. What then do you need to do to think like a data scientist?

Consistency

Keep momentum to become an effective storyteller. Just like how a data scientist will endeavour to write a couple of lines of code every day, as a writer, you have to do the same. For example, if your next project is an article that you foresee will be 7,000 words long and a week to write, build up your “stamina” by writing 1,500 words a day in your journal/blog. Over 7 days, that will be 10,500 words. After doing this exercise, writing 7,000 words in a week will not seem like such a daunting task.

Learn

Secondly, keep learning. Embark on your own personal journey of discovery and explore the magical world of data. You can pick up digital skills and take short courses on Datacamp and SQLZOO, read up about data protection and cyber-security, participate in data modelling virtual boot camps and also speak to tech industry “gurus” to share ideas and engage in discussion on development areas in the data field. By doing all of these, you are learning new and improved methods of communicating and presenting data. As a writer, develop the habit of constant reflection, keep an open mind and always relate what you have learnt on your journey to how it will help you improve as a writer.

Be a problem solver

Being a data scientist entails being flexible, open-minded and ready to embrace complexity to solve problems. Data scientists are often expected to analyze data that is incomplete, vague and may not even make any sense on a first look. Similar to writers, there may be plot-holes that need filling, logical fallacies in character development, convoluted plot twists that require clearer logical explanation or insufficient research to back-up an argument in an essay. All of these present problems to the writer that he/she must get through to bring a piece of writing to a point or completion. If it is not done, the piece writing will not make sense. Just like a data scientist that writes a programme that is missing a few lines of code, the final product simply would not be of any use to anyone!