Why should we Learn Data Science and how to start?

It’s been reviewed by Harvard Business that Data Science is the “sexiest job of 21st century”. But it is important to know what makes this job so valuable? Why Data science comes under the category of “Highly paid profession”? In this article, we will walk through some of the main reasons as to why one should learn Data Science. We will also understand the market scenario and how Data Scientist helps in making good decisions.

Here are the 3 main reasons that will surely convince you to make a career in Data Science:

· High in Demand

There is a huge abundance of data and hence more data-related problems. However, there is a lack of resources to convert this data into useful products. That is, there aren’t enough people who possess the required skills to help companies utilize the potential that data holds. Hence, there is a huge demand for specialized people who are proficient at handling, cleaning, analyzing, and understanding data trends. There are so many vacant positions than the number of Data Scientists in this world. Since there is heavy demand, organizations pay galactic figures for these positions. Hence, one should learn Data Science so as to tap this opportunity to enhance their career.

· A remunerative Career

To be a data scientist, one requires being an expert in various fields like statistics, programming, and various tools, the learning curve is quite steep. This is the reason why Data scientist is very high in demand. This is a very prestigious position in the company since the company relies on these experts to make highly complicated decisions. Further; the role of a Data Scientist depends on the specialization of his employer company.

For example — A health-care company will require data scientists to help them to diagnose the correct problems on the basis of symptoms. Which is not an easy task to perform so the pay scale of Data Scientist is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in finding the insight. This, combined with the number of vacancies in Data Science makes it an untouched gold mine. Therefore, you should learn Data Science in order to enjoy a fruitful career.

· This is the future

Now a day every industry is becoming data-driven and new innovations are being made every day. The field of technology is growing faster than any other field. Since more people are interacting with the internet, more data is generated everyday and hence more jobs in Data Science. Every industry focuses on profit and for that, better decisions need to be taken, hence demand of data scientists is growing every year. In today’s world, it has become a necessity to possess data-literacy. We must know how to predict data out of the past. We must learn the techniques and understand the requirements to analyze and draw insights from the data. Data holds an untapped potential that must be realized in order to develop useful products. With the advent of machine learning technologies, it is now possible to predict and intelligently classify information. Big Data and Data Science hold the key to the future.

One of the major questions people often ask :Is it possible for non-technical candidates into the field of Data Science & Machine Learning? If Yes how?

Well, the answer is yes, it is possible for a non-technical candidate to jump into the field of Data Science & Machine Learning. Though it won’t be easy I have seen people coming from different fields making this difficult task possible. The creator of the Hadoop framework, “Doug Cutting” who is one of the most famous data engineers in the world. He helped propel data science into the mainstream graduated with an AB in linguistics. Tim O’Reilly, now known as the founder of O’Reilly Media, and the curator of thousands of data and programming resources, graduated in Classics. Data science is not about gathering technical degrees, writing codes, making models, or creating good visualization. Data science is about the amount you spend in understanding the data, and the curiosity that helps you using some skills on data to create as much impact as its possible for a company.

Following are some steps following which one can make a career in data science:

1. Have some determination

Now, the first thing that you need to do before you think of enrolling in a course or started watching YouTube videos on Data science is to determine yourself. Sit down and think carefully about what you hope to get out of it, is this field really interests you? Is it worth your time and money? If the answer is Yes and you still want to continue then your 1st step is successful and you need to follow 2nd step.

2. Set a schedule

Nothing can be done without making a proper schedule for it. Getting some time out of your daily time would help you to learn it even faster. Try to make a road map, curate the resources you need, and to the amount of time you will take to learn them. Getting early in the morning would help a lot in scheduling the right road map.

3. Learn to Code and different tools

To start with, one should learn basic coding, to gain some confidence in this field. Coding will give you valuable skills. Learning how to code for beginners will provide you with enough skills and experience to pursue a career as a coder or programmer. There are various tools that play an important role in learning data science and machine learning that is, tableau, advance excel, Power BI, and so on. Learning them would really help in understanding data science faster.

4. Do project on real life

It is really important to test your knowledge on some real-life project, these projects would be your portfolio and that portfolio of your projects can help get you noticed and build your credentials as an aspiring data scientist. You will also learn how to apply your skills and improve them at a much faster pace. There are many platforms available like Kaggle, Datakind and Datadriven that allow you to work with real corporate or social problems. These platforms not only provide data and problem statements but helps beginners to share some notebooks solved by other candidates.

5. Join some communities and get a mentor

Inevitably, you’re going to want to branch out and look for data science communities to get the latest news and to discuss any problems you might be having. You’ll also begin seeing how data scientists interact with one another.
If you want the latest and greatest data science content, you might want to check out KDNuggets or Datatau. Datatau is an aggregator of data science content where people can upvote the best selection of data science content. This would keep you updated with all the new algorithms and the latest changes happing technologies.

6. Keep on learning

To perfect every skill data science requires would mean spending several lifetimes on the topic. You’re never going to finish learning, and you should always keep the spirit of intellectual curiosity that brought you to data science in the first place. Paul Kalanithi, a Stanford surgeon who had to confront early mortality, wrote poetically about his outlook on life in his memoir, When Breath Becomes Air: “You can’t ever reach perfection, but you can believe in an asymptote toward which you are ceaselessly striving.”

Conclusion: Data science is way more then what you think, there is so much to explore and learn.

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