Context: This article is part of Heicoders Academy’s continual effort to enhance our students’ conceptual understanding of Data Science concepts and tools.
I was recently interviewed by Troy from EADC on several data science related topics, in a bid to explain them to laypersons who had zero or limited exposure to data science. In this interview, the very first question Troy asked me was, “What is Data Science?”.
Weeks after the interview, driven by curiosity, I did a quick google search of “What is Data Science?” to see how experts in this field tackled this question. Here are common explanations that came from my Google search:
- Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
- IBM: Data science combines the scientific method, math and statistics, specialised programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.
I was surprised. While these were factual and “model” answers, it dawned on me that they lacked the intuitive approach much needed by the layperson.
So, here’s my attempt to provide a simplified and intuitive explanation below.
What is Data Science and why is it important?
Let me explain this with a relatable example — say a supermarket hires a sales promoter to recommend products to customers to increase sales. After observing 100 customer purchases, this intelligent sales promoter learnt that customers who bought eggs tend to also be willing to buy bacon upon some promotion from him.
Data Science does that too. Instead of having humans learn these patterns, we leverage on computers and mathematical models. Data Science is all about teaching or instructing your computer to interpret data and churn out insights using established mathematical formulas and statistical methods.
Data Science matters because there is a limit to how much data a human can process, and how many relationships one can uncover from the data. By relying on the powers of computers and models that can scale indefinitely, there is no limit to the amount of data we can go through, nor the number of insights and hidden patterns we can uncover!
We can liken the advent of data science to the industrial revolution. In those days, companies that were quick to adopt industrial machines thrived, while others that relied solely on physical labor quickly got displaced. Similarly, Data Science is our generation’s “industrial revolution” and companies that don’t adopt it may risk getting displaced.
What is Python, and why learn Python for Data Science?
Data Science is all about teaching or instructing your computer to interpret data and churn out insights using established mathematical formulas and statistics.
Your computer cannot understand human languages like English. To provide instructions to a computer, we must use a programming language, and Python is just one of the many programming languages out there. However, Python has unique properties that makes it a go-to language for most data scientists.
Firstly, the Python ecosystem has some of the most comprehensive and well-documented libraries for data science. Libraries are pre-written codes written by other programmers that we can adopt in our code, instead of having to rewrite the code from scratch. Let me give a simple analogy so you can appreciate its importance.
A world without libraries is like a world where skyscrapers are built brick by brick. Eventually, they do get built, but builders endure a long and tiring process. Some bricks are badly laid now and then, which could render the building structurally unsafe. Libraries, however, can be likened to prefabricated parts of the skyscraper, which can be used to build safer structures more effortlessly and in a much shorter amount of time.
In Python, developers have created many wonderful data visualization and machine learning libraries which significantly shorten the time required to create useful products. This allows us to stand on the shoulders of giants.
The second reason is because Python is a verbose programming language that reads somewhat like English. This makes Python easier to read and write, and less daunting for beginners. As such, even someone with no formal IT or programming education can easily learn Python, especially with the help of structured guidance in the form of courses.
Conclusion
Being able to explain complex concepts to a layperson is proof that you truly understand a subject. So the next time a colleague or your boss asks you to explain data science, be sure to impress them with your ability to provide an intuitive and easy-to-understand explanation!
