Data Science Provides Huge Job Opportunities. Here’s How You Can Capture Them
This article is originally the part of Analytics and Data Science India Jobs Study 2017- by Edvancer & AIM The Indian IT industry is in the throes of a mid-life crisis riled by a deep slowdown which is resulting in redundancy and job losses. But as the sun sets in the IT industry in India as we know it, another sun is rising in the form of the data science industry which is growing by leaps and bounds. India with its immense potential talent in the form of engineers, quants, business managers is fast emerging as the Data Science Capital of the world. This is borne out by the fact that global organizations like NEC, Mercedes-Benz, Target, Walmart, PayPal, AIG, Accenture etc. have set up their data science centers of excellence in India. The slowdown in old IT technologies coupled with the rapid rise of data science has resulted in IT companies trying to hire data scientists by the droves and creating a job boom for analytics & data science professionals in India. Jobs in analytics & data science have grown by 100% over the last year adding tens of thousands of employment opportunities and we expect this growth to only intensify over the next few years. IT companies have an excess of talent in technologies which are no longer required and a large proportion of these people will eventually need to be re-skilled in areas which are in demand of which data science is one of the biggest. Nasscom expects that almost 50% of the IT workforce will need to learn these new technologies to avoid becoming redundant. The challenge for employers thus lies in the shortage of people who are skilled in data science tools & technologies and their applications in business. Salaries are rapidly increasing given the increased demand and shortfall of talent and hence there is no better time than now for you to develop the data science skill sets needed by industry. At Edvancer, when we speak to aspiring data scientists looking to take up our courses, a common theme from the questions we are asked is how will I fit into data science given my existing background and will I get a job? The unique and best aspect of data science is that it is not limited to any industry or sector. Data Science today is being used in every industry in this world from manufacturing to retail to healthcare etc. Data is almost omnipresent now as the collection of data has become much easier and storage is cheaper. With humongous amounts of data being generated daily, companies across sectors are taking advantage and making use of the insights gained from the analysis of that data to benefit. Data science through machine learning & artificial intelligence is proving to be instrumental in pushing the boundaries of science and what was science fiction 10-15 years ago is turning into reality now. This has opened numerous opportunities in data science for a large cross section of people from varied backgrounds and experiences who have developed the relevant data science skill sets. So how does one capture this opportunity? You need to take a structured and patient approach to creating a career in data science. Follow the path below and you will find yourself in the “sexiest career of the 21st century.” Get trained in the relevant skillsets: Data Scientists have expertise in a wide variety of tools and technologies with the most in-demand tools being R, Python, SAS, Hadoop, Spark, NoSql while being well versed in statistics, predictive modeling, linear algebra, machine learning, text mining, and handling big data. Don’t let all these different requirements worry you. Take a systematic approach. Start with learning R/SAS, statistics and predictive analytics. Then move on to Python, linear algebra, and machine learning before working on the Hadoop, Spark & NoSql stack for big data. Implement your learning on projects and display them: Employers look for people with a practical perspective and the ability to start contributing on the job on day one. To that end, they will look for data science projects on your CV. Either you can work on projects provided on Kaggle and Crowdanalytix or use the many freely available data sets on the internet. Another way is to combine your learning and project work by joining a course which provides you multiple projects to implement your learning. Create your Github profile and host your projects and codes over there so that you can share it when looking for a job. Create a blog where you can write about these projects and on data science in general. Create your CV and update your Linkedin profiles: Well you obviously have to let the world know that you have the necessary capabilities for a data science role. So, update your CV with the tools and technologies that you have learned and describe the projects you have worked on with an emphasis on the outcomes you were able to deliver. Also, provide your Github link in the CV. Make sure you update your Linkedin profile too as all potential recruiters will check it. Prepare for interviews: Practice makes perfect and you should prepare and practice for interviews too. Go through various frequently asked interview questions on the internet and prepare for them. Get ready to be tested on what you have mentioned on your CV and also be ready to be tested with case studies and take-home problems to solve. Keep yourself updated on developments in data science. Practice answering estimate based case studies which are designed to test your structured thinking. Network and search for a job: Now that you have the building blocks in place, it is time to go out and get a job in data science. This will be a stage which will require patience. Start by creating a list of target sectors and companies that you are interested in working with or you believe would be compatible with your background. Research about them and be ready with relevant reasons as to why you want to work for them and how you would be able to add value. Check Linkedin, their websites and other job portals for current openings in data science in these companies and apply to them while utilizing your friends and family network too. Network with data scientists on Linkedin & Facebook through groups. Connect with hiring managers and data science leaders as they keep posting their requirements outside of normal channels too. Attend data science meetups and events where you can take the networking offline while continuing to apply for suitable roles through job portals. Opportunities will definitely come your way. In conclusion, data science is a booming industry with lots of opportunities being created. Persistence, the right approach, and hard work will get you into a dream career in this field. Go through this report also to know more about the job situation in details.
This article is originally the part of Analytics and Data Science India Jobs Study 2017- by Edvancer & AIM The Indian IT industry is in the throes of a mid-life crisis riled by a deep slowdown which is resulting in redundancy and job losses. But as the sun sets in the IT industry in India as […]
This article is originally the part of Analytics and Data Science India Jobs Study 2017- by Edvancer &amp; AIM The Indian IT industry is in the throes of
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["data science", "machine learning", "artificial intelligence", "AI", "R", "RAG", "Python", "Aim", "analytics", "predictive analytics"]
["AI", "artificial intelligence", "machine learning", "data science", "analytics", "Aim", "RAG", "predictive analytics", "Python", "R"]
Aatash Shah – is the Co-founder at Edvancer, one of India’s leading analytics training institutes. Prior to starting Edvancer he worked for over 6 years in investment banking.
He started his career with ICICI Bank and then went on to work with global investment banks Lehman Brothers and Nomura Securities as a Vice-President analysing equity investments for clients across the globe. He is a post-graduate in management from IIM Lucknow and is also a BE from Mumbai University. Aatash is passionate about combining education with technology to create a new paradigm of learning which is relevant, timely, cost-effective and focused on fulfilling the end goal of education, which is meeting the needs of the employers.