You don’t know but you are a Data Scientist.
Being a data science learner, one thing I have observed that data science was with us since our childhood. During my childhood, I used to walk to school from my home, and there were 3 routes to reach it. I used each of these 3 routes and decided to take the one which would help me reach quickly(data -> decision). When I was in high-school, I never used to love history subject, so I decided to go library and see what’s the syllabus and weightage of each chapter in this subject, and I chose only those chapters which would likely give me more grades(data -> decision). Even our parents are unknowingly using data-science techniques.They decide their monthly spendings on their groceries and accordingly buy it from supermarkets. All these examples tell us that data science is an integral part of our life.
Then why it is being said that data-scientist is toughest job ? The reason is that, when it comes to job, data science domain becomes vast. You need business understanding skills, technical skills, knowledge of machine learning, stats, data visualization and most important problem solving skills. To give you an example, consider that Insurance company wants to detect fraud insurance claims, they have asked you to decide which machine learning technique to go with. One of their workers has stated that using Neural Networks will give 99% accuracy. Good number isn’t it ? but what if client asks how have you arrived at claiming whether the particular claim is fraud ? It would be very difficult to explain how neural network has made that conclusion. Hence, it is advisable to have a better business understanding.
So to give you a key takeaways from this post, follow this things:
- Blogs: Many data-scientists publish their understandings through a medium of blogs. Also, if you are a good story teller then you should opt for blogging as it will help you build story around any business problems. Key Platforms : Medium, Data Science Central, Analytics Vidhya, Towards data science.
- Books: Start reading books in domains of business science, stats, machine learning, programming and data visualization . There is no proper structure as to which book to start with but once started reading don’t quit. Key Books: Story telling with data, Data Science in Business, Data Science for Dummies.
- Hackathons : Start participating in Hackathons. They provide the best way to understand which companies are facing problems in data science and how you can solve them. Key Platforms: Kaggle, Analytics Vidhya, Techgig, crowdAI, NumerAI.
- MOOC: If you wish to master any skill-sets, then try to sign-up to data science course you wish to. They give you detailed insights about the subject. Key Platforms: Coursera, DataCamp, Udemy, Udacity.
- Programming Language: When it comes to delivering results, execution plays important role. So here are they important languages that you should put your hands on : Python, R, Tableau, SQL, PowerBI.
- Webinars: Try to attend as many Data Science webinars as you could. It will help you gain new knowledge. Key Platforms: Techgig webinars, Business Science.io
Most important advice, show your visibility. Being active on LinkedIn platform will help you keep updated in domain of data science and you could always publish your articles/findings so that others will find it helpful.
Some of the Key figures to follow on LinkedIn : Matt Dancho, Kyle Mckiou, Kristen Kehrer, Mike Tamir, Kate Strachnyi, Theophano Mitsa and Kirill Eremenko.
Last but not the least never stop learning.
By: Gaurav Chavan