- Basic statistics and mathematics is your compass in the field of Data Science. You must have it to have a sense of direction.
- You are better-off choosing one of R or Python or Matlab or any other platform, and become a master in it than attempting to get dirty with all of these.
- Right from the start, version-control your work with a tool like Git/Github/Bitbucket. It can save you from a lot of headaches.
- Don’t under-estimate the importance of understanding the problem domain. Domain knowledge is the secret weapon for Data-Manipulation.
- Don’t under-estimate data cleansing. It pays to cleanse your data.
- Pay attention to how you impute your data and document why you took that approach.
- Don’t ignore exploratory data analysis (a.k.a EDA). It not only improves your problem-domain knowledge, but also instigates your creative juices towards solutioning.
- The EDA that you do in the beginning is the road that you lay for your presentation on insights and solutioning to business stake-holders in the end.
- Master the art of Feature Engineering to improve your probabilities of becoming the celebrity Sherlok Homes of Data Science.
- Don’t jump into esoteric modelling. Start simple.
- Don’t jump into a model because it is popular. Know how the model works and how you can tune it to improvise it.
- It is better to make Data Scaling as mandatory part of your data pipeline, than have it as an optional thing. It gives more options to try out various models.
- Data-modelling is both the art and science of finding that optimal trade-off between bias and variance. Enjoy the game.
- Almost always you don’t get a 100% accuracy with your data-modelling. And when you get it, question yourself and your approach. Triple-check your understanding and get it reviewed before celebrating. Save yourself the unwarranted heart-break.
- Find opportunities to pair-up with someone to teach or learn. You will end up learning something for sure. It has its definitive ROI for you.
- Up-skill with deliberate practice. How well you practice (quality)is equally important if not more than how much you practice (quantity).
That is all to the list…for now.
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