If you have been on the internet for as long as you can remember, you must have come across the ‘Sexiest Job of the 21st Century’ – Data Science. What makes the field particularly interesting, in my opinion, is its ability to give ‘interdisciplinary’ a new direction – one in which we combine not only the hard sciences of math, statistics and computer science but also the importance of domains such as biology, healthcare, finance, economics, sociology and the list goes on. To many of us, Data Science probably sounds no less intimidating than rocket science. At the time of this writing, in 2020, it’s still vastly uncharted territory, often confused with big data or artificial intelligence (AI) or any of the other currently trending buzzwords. We are here to explore what is data science in simple words. Maybe you’re a student, impressed by what data science has to offer, or maybe you are a working professional, who wishes to make a switch to one of the most promising career paths. There is a lot of information out there which might puzzle beginners – “spoilt for choice” – if you will. We will seek to consolidate much of that information and present it in small, understandable chunks, and, in the process, clarify many of the questions that many of you are sure to have.
What is Data Science in Simple Words?
What is data science in simple words? Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract value from data. Data scientists combine a wide range of skills — including statistics, computer science, and business knowledge — to analyse data collected from the web, smartphones, customers, sensors, and a bunch of other sources.
The idea is to reveal trends and produce insights that businesses can use to make better decisions and create more innovative products and services. Just businesses, you ask? Absolutely not; data is the bedrock of innovation and its value comes from the information data scientists can glean from it and then act upon – this extends its practice well beyond businesses and into academic and social pursuits as well.
What is Data Science in Simple Words: Tracing the Roots of Data Science
The term “data scientist” came into existence sometime around 2008, when companies began to realise the need for professionals adept at understanding and dealing with data – anywhere from your garden variety statisticians to PhD’s who are all too familiar with the rigours of research and experiments. To quote Hal Varian, Chief Economist at Google and UC Berkeley Professor of Information Sciences, Business, and Economics:
The ability to take data — to be able to understand it, to process it, to extract value from it, to visualise it, to communicate it — that’s going to be a hugely important skill in the next decades.
Interestingly enough, a little known fact is that “data science” can actually be attributed to D.J. Patil – Former U.S. Chief Data Scientist – who was revolutionising the way we consume data while working closely with the former POTUS, Barack Obama. What is even more striking, and this for those who are intimidated by the mathematical rigour with data science or are from a non-engineering background, is that DJ Patil, admittedly, was not the best student of Mathematics until his life completely changed when he joined De Anza College. His LinkedIn still reads – “Started out in Jr College and realised I was pretty good at math.”
Big Data vs Data Science
Big Data addresses the very front end of the pipeline where it primarily focuses on variety, velocity and volume of data. This further lends its strength to the data science field as the data collection ends up for all the bases for any type of analysis going forward.
While a data scientist’s priority would be to derive insights from data; a big data developer or architect would be interested in building a fault-tolerant system that is able to handle, store and process tons of data in an era eclipsed by frequent occurrences of data deluge.
Machine Learning / Artificial Intelligence vs Data Science
Machine Learning is not only a subset of Artificial Intelligence but also a subset of Data Science. While Machine Learning focuses on the implementation of statistical and mathematical algorithms through computer programming languages (i.e. code), Data Science is the superset which includes Machine Learning as a component, among other methods and techniques ranging from data collection to building models to data visualisation.
Artificial Intelligence is the subfield of Computer Science (or Mathematics, if you will) that is concerned with making machines think like humans (ref: Turing test). While components of AI are present in ML and Data Science through multiple use-cases such as predictive analytics and real-time analysis of data, its inclusion is limited by the complexity of the problem statement at hand. AI, as we understand it, is at a nascent stage till date with predicted advances in the medium to long term.
Potential Data Science Career Paths
Here is a non-exhaustive list of potential career paths and job roles within the all-encompassing field of Data Science:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Engineer
- Analytics Professional
- Research Engineer
- Artificial Intelligence Scientist
- Data Miner
- Natural Language Processing Scientist/Researcher
Required Data Science Skills
Typically the skills required to be a successful data scientist are as follows:
Mathematics & Statistics
- Linear Algebra
- Calculus I
- Theory of Distributions
- Basic Statistics
- Inferential Statistics
- Programming (this is language agonistic for the most part, however, Python, R and SQL is the usual stack)
- Fundamentals of Computing
- Discrete Mathematics
- Functional Programming
- Data Structures & Algorithms
- Economics or
- Biology or
- Political Science
- Or any other domain of your interest
Material for Further Reading
- Data Scientist Job Descriptions: How to write an effective Data Scientist resume
- 3 Key Steps to Landing Entry Level Data Science Jobs
- How to get Data Science Jobs
- How to Become a Data Scientist?
- Most asked Data Science Interview Questions in India
- Top 15 Data Science Interview Questions and the Best Way To Answer Them
- Top 10 Data Science Projects: Learn to Solve Real-World Problems with Data
- Mistakes to Avoid in Your Data Science Career Transition
- Data Modelling & Analysing Coronavirus (COVID19) Spread using Data Science & Data Analytics in Python Code
- Recommender System with Python: Collaborative Filtering for Movie Recommendation System
If this piques your interest, you can always check out what Springboard has to offer with their wide range of the aforementioned topics through their data science career track program that is 1:1 mentoring-led, project-driven and comes along with a job guarantee which will help you prepare yourself for a meaningful and successful data scientist jobs. The career track program is targeted at making you an employed professional in data science. Apply Now!