The newfound love for Data Science in today’s Computing World isn’t unjustified. Ranked as the hottest job on offer in the coming years by Harvard Business Review, and coupled with the sweet paycheques, the lacunae in the existing skills of professionals as compared to the industry standard skill set required for the position of Data Scientist means there are a lot already into learning data science. In such a scenario, what gives you the competitive edge? Unanth brings to you the 10 steps to follow enroute your path to becoming a Data Scientist.
- Develop Skills in Algebra, Statistics & Machine Learning
A data scientist is someone who is better at statistics than any software engineer, and better at software engineering than any statistician. The idea is to have the just the right balance, avoiding excessive or too less of an emphasis on either of the two.
- Learn to Love (Big) Data!
Data Scientists handle a humungous volume of segregated and non-segregated data on which computations often cannot be performed using a single machine. Most of them use Big Data software like Hadoop, MapReduce, and Spark to achieve distributed processing.
- Gain thorough Knowledge of Databases
Given the huge amount of data generated virtually every minute, most industries employ database management software such MySQL, Cassandra etc. to store and analyse data. A good insight towards the working of DBMS will surely go a long way in securing your dream job as a data scientist.
- Learn to Code
You cannot be a good data scientist until you learn the language in which data communicates. A well categorized chunk of data might be screaming out its analysis; the writing may be on the wall but you can only comprehend it if you know, well, the script. A good coder might not be a great data scientist, but a great data scientist is surely a good coder. Hence Love. Peace. Code.
- Master Data Munging, Visualisation & Reporting
Data munging is the process of converting the raw form of data into a form that is convenient to study, easy to analyse, and comfortable to visualise. Visualisation of data and its presentation are an equally important set of skills on which a data scientist relies heavy when facilitating managerial and administrative decisions using his/her data analysis.
- Work on Real Projects
Once you have become a good data scientist in theory, it is all about practice. Search the internet for data science projects (google quandl) and invest your time building your own forte, along with zeroing down on the areas that still require brush up.
- Knowledge Knowledge Everywhere!
A data scientist is a team player, and when you are working together with a group of like-minded people, being a keen observer always helps. Learn to develop the intuition required for analysing data and making decisions by closely following the working habits of your peers and decide upon what best suits you.
- Communication Skills
Communication skills differentiate a great data scientist from a good data scientist. More often than not you would find yourself behind closed doors explaining the findings of your data analysis to people who matter, and the ability to have your way with words will always play in handy when tackling unforeseen situations.
Websites such as Kaggle.com are a great training ground for budding data scientists as they try to find teammates and compete against one another to showcase their intuitive approach and hone their skills. With the rising credibility of the certifications provided by such sites in the industry, these competitions are fast becoming a stage to show to companies how innovatively your mind works.
- Stay up-to-date with the Data Scientist Community
Follow websites such as Kdnuggets, datascience101, DataTau etc. to remain in sync with the happenings of the World of Data Science and gain an insight regarding the type of job openings currently being offered in the field.We hope the above said list helps you take off on your data scientist ambitions and acts as a faithful companion as you steer your way ahead of everyone towards excellence.