25 Things You need to Know about Data Science

With over 6 billion (and counting) devices connected to the internet right now, as much as 2.5 million terabytes of data are generated every single day. By 2020, millions of more devices are expected to get connected, projecting an estimate of around 30 million terabytes of data every day.

As a fresher or an IT professional, this should fascinate you. If you’ve been reading the news of late, I’m sure you’re aware of the massive layoffs across the tech companies in India. So, at this point in time, one thing that becomes essential is the need to reskill to something more rewarding and authoritative – Data Science.

So, if you’re someone who intends to switch to Data Science, here are the 25 things you should know about it. Read on!

    1. First things first – the Harvard Business Review calls Data Science as the hottest job of the 21st century. Domains across diver industries are all praises for data science for a number of business insights it uncovers. The co-founder and CEO of Springboard, Gautam Tambay, also claims data is the new oil. Over the decade, the consumption of information online has shot up remarkably and has led to a stage where all our basic activities are carried out online. With so much data produced every day, Data Science is the field that can help businesses uncover crucial business data and set them on track.
    2. There is a huge demand for data scientists today. The US leads the data science market, requiring 190,000 data scientists by next year. India also joins this elite bandwagon, requiring data scientists across a diverse range of industries. By 2025, the Big Data analytics sector in India is estimated to grow eightfold, reaching $16 billion.
    3. For the uninitiated, Data Science is the process of slicing through massive chunks of data, processing and analysing them for meaningful information that can help businesses get insights on concerns, customer experience, supply-chain and other prime aspects that would complement their business operations.
    4. From using your GPS to reach a nearby destination to using your online shopping app, you generate tons of data every day, which comes back to you as optimized performances. Noticed how the Amazon app comes up with the right recommendations as you keep using it?
    5. Data science requires you to have or develop skills in statistics, data science tools, communication skills, commendable knowledge in quants and business acumen. A data scientist puts to use all these skills to work on data, break it down, look for angles of approach, find patterns, analyse them, and extract information.
    6. You don’t have to necessarily possess a degree or a PhD. Data science requires you to know the fundamentals of analytics. You need to be capable of working on analytics tools and understand the basics of data processing to get started.
  1. Every company has a distinct approach to data science. It is impossible to know everything in data science. What would help is knowledge in some universally recognized and adopted technologies like SAS/R, Python coding, SQL database and Hadoop platform will help you switch to data science.
  2. Data scientists earn more than the average IT employee.
  3. Data scientists are preferred by both start-ups and tech companies. In fact, it’s the startups that are increasingly becoming aware of data science, looking forward to hiring more data scientists than before. Corporates and tech companies are catching up by reinvesting in analytics and data scientists.
  4. One of the biggest causes of tech companies laying off employees is not automation. It’s the massive difference between the evolving technology and the lack of manpower to work on it. Data science requires niche skills and only the talent pool that has failed to upskill to the in-demand skills has been laid off.
  5. Analytics can be classified into three broad categories – descriptive analytics, predictive analytics and prescriptive analytics.
  6. Descriptive analytics is when you work on a data set and describe the pieces of information you uncover. For instance, if you’re analysing your bank statement for the previous month, if you say that 30% of your income was spent on house rent, 20% on food, 10% on fuel and similar, that’s descriptive.
  7. Predictive analytics is what you can forecast or estimate with the history data. With your bank statements for the past 12 months, you can predict how your expenses will be for an upcoming month.
  8. Prescriptive analytics is when you want to rectify your expenditure on something. For instance, if you feel you’re spending too much on fuel or food, prescriptive analytics will tell the best category for you to work on to reduce the expense.
  9. Not all data generated online is crucial. Dark data refers to data that can never offer meaningful insight. From logs used in a call center to social media feeds, these are chunks of data that can never be analysed for insights.
  10. Data scientists should be aware of the term Machine Learning. In simple words, Machine Learning refers to the development of systems that can learn, adapt and improve depending on the data that is fed to them. Your Siri or Google Maps is one of the best examples of Machine Learning. If you’ve noticed, Siri gives better responses by finding patterns in your questions and responds better. The Google maps also get optimized and come up with predictive insights on your destination.
  11. Machine Learning requires you to master crucial algorithms. Some of the algorithms include Random Forest, Neural Networks, SVM, Logistic Regression and more.
  12. R is one of the most popular programming languages in Data Science. You can’t call yourself a skilled data scientist if you don’t know how to work on R.
  13. Data in Data Science is of two types – structured and unstructured data. While structured data is the data that can be categorized, segmented and put into databases, unstructured is the one that cannot be. Examples of unstructured data include social media posts, books, audio recordings and more.
  14. IoT is the latest technology that contributes to Data Science to a significant level. IoT refers to the ecosystem of devices connected to each other via the internet. Smart homes, smartwatches, health gears are all part of the IoT ecosystem. To surprise you more, there are even smart breweries now.
  15. Data science is very closely associated with IoT because IoT is all about data generation and Data Science is about analysing it. On becoming a data scientist, you will also be updating your skills enough to be part of this next big tech revolution.
  16. More than learning Data Science, what is more, effective is practicing it. If you intend to take up a Data Science course, make sure your course offers capstone projects, case studies and enough real-time data sets to work on. More about the theory, it’s your hands on experience that counts.
  17. Data is never clean. Before you start imagining about saving your company from the loss of millions of dollars, remember that you will be spending more time on cleaning data than generating insights from it. Only when it’s cleaned that you can sit down to perform analytics.
  18. Apart from technical skills, Data Science also requires excellent communication skills. Being a pro, you have a thorough understanding of the extracted insights. But when a layman sees your discovery for the first time, he or she is sure to stand puzzled. So, you must also be a good communicator of your insights and have good skills at working on presentations, spreadsheets and documents.
  19. Data Science is a rewarding career. At the time when employees are getting pink slips, pay cuts, and laid off, Data Science is one field that’s welcoming talent. You will not just have an authoritative role in your business or organization but receive good paychecks and enjoy a perfect work-life balance.

So, these are the essential things you need to know about Data Science. What are you waiting for? Get started with Data Science and switch to a high-paying career.

Also Read 

Data Scientist : The Hottest Job of the 21st Century

Confused About a Data Science with R vs. Big Data Analytics with Hadoop Course?


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