Research/Blog
How to Take-up Data Science as a Career in the New Year!
- December 26, 2019
- Posted by: CellStrat Editor
- Category: Artificial Intelligence Computer Vision Deep Learning Machine Learning Natural Language Processing Reinforcement Learning Technology Uncategorized
Artificial Intelligence (AI) & Machine Learning (ML) engineers are the hottest people in demand these days. Though all large and small companies are in search and hunt for these engineers but these are scarcely available. Whoever is available, expects fat pay packets not every company can afford. AI is heavily dependent on Data Science which in turn is heavily governed by Mathematics. Thus, if one lacks in Mathematics skills, it will be very difficult for him/ her to be able to get into this domain.
What is Data Science?
Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. (Ref: Wikipedia)
Data Science encompasses activities from many fields within the context of mathematics, statistics, computer science, and information science. Scientists apply mathematics and statistics in data science to generate insights from the large datasets.
First, data scientists gather data from multiple sources and disciplines and compile it; do feature engineering over it to refine it. Then, they apply machine learning algorithms to derive insights and at last extract useful information from it and visualise it to do story telling over it to explain the knowledge generated to the business stakeholders at the top.
We at CellStrat have been running AILab for about more than 18 months now and I believe if Data Science enthusiasts follow under mentioned steps along with mastering above skills, it will fasten their journey to become successful data scientists:
- Become a data oriented person: There are a number of problems that can be solved through data science, namely all kinds of health care diagnosis like cancer, heart diseases etc., performing sentiment analysis over telecom and social media data etc. It’s all about asking the right questions and finding a way to get their answers.
- Learn by becoming a doer: Learning deep learning topics like neural networks, image recognition, natural language processing etc. is important but most data scientists spend 90% of their time doing data cleaning. Thus, most of the above, just get remaining 10% of the time. What this means is that best way to learn is to work on projects from start to end. This way the skills you gain are immediately applicable and useful. This also helps in building your github profile and portfolio to show to prospective employers.
- Learn to communicate insights: Data Scientists need to consistently present the results of their analysis to others at the higher levels. Good presentation skills thus can be a big role player and a differentiator between who gets hired or who doesn’t or between an ‘okay’ and a great data scientist.
Good communicability of insights involves following parts:
- Understanding the topic and theory well.
- Understanding how to organise the results well. &
- Finally, knowing how to explain your analysis clearly.
I have observed, it’s indeed an uphill task for techies to get good at communicating complex concepts effectively. So, we continuously recommend the following to our audience:
- Start a blog to post the results of your data analysis.
- Try to speak in meetups regularly.
- Be very active on sharing your expertise over Linkedin and other social media sites.
- Try to speak regularly on webinars to inculcate online presentation skills.
- Create and regularly use your Github profile to store all your analysis, projects and also show this Github link on your LinkedIn profile and vice-versa
- Domain knowledge also plays an important role and thus data scientists with domain knowledge are especially in demand and command higher salaries than domain agnostic data scientists.
Happy New Year to all our Readers and Happy Lifelong Learning!!!