Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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The four data science skills I didn't learn in grad school (and how to learn them!)
- Take a course. There are a lot of online options out there, including courses by Khan Academy, DataCamp, Stanford and Udemy. In person courses are a bit harder to find, but if you check a local university, community college or code camp you might get lucky.
- Develop a SQL portfolio. Having examples of your ability to write queries on real databases is good evidence that you're familiar with the language. One option is to write kernels (i.e. hosted R or Python notebooks) on BigQuery datasets on Kaggle. I've written a quick how-to to get you started. (Full disclosure: I work for Kaggle. :) HackerRank and SQLZOO also have quite a few SQL exercises.
- Read outside your discipline. Academic disciplines tend to use a specialized set of statistical tools. In sociolinguistics, for example, we work a lot with mixed-effects regressionâ??-â??but there are a lot of other statistical approaches out there. Reading work in different disciplines will expose you to a wide range of different techniques and problems and help get you get comfortable jumping feet-first into a new topic.
- Practice analyzing new types of data. Data scientists need to work with all sorts of data. You probably already have deep experience with one type of data, but consider branching out. Have you worked with time series? Text? Images? Video? Audio? Pre-trained models? Relational databases? Figure out what the gaps there are in your knowledge and try your hand at working with some new and different sources. (Obligatory plug: Kaggle has more than 10k public datasets from a huge variety of sources. You can also check out Zenodo or the Dataverse project.)
- Talk about technical concepts with people outside your field. Not only will you learn a lot, you'll also have a chance to practice explaining technical concepts to people who don't share your specific academic background.
- Use version control on every single research project and paper from here on out. I'm 100% serious. My entire dissertation was version controlled and it saved my butt so many times I lost count.
- Use GitHub for your personal projects (if you have any) or research you can share. This is optional, but helpful if you end up joining a team that uses GitHub. In addition, an active GitHub profile is one way to demonstrate your workflow to potential employers.
- Work on identifying "done for now". The next time you work on a project, stop every so often, maybe before you wrap up every day, and think about whether you've already created something valuable (you probably have!). Take a minute to practice how you might describe what's useful or interesting about what you've already done.
- Consider sharing intermediate stages of your research. If you can, consider sharing the intermediate stages of your next research project, maybe in a blog or to a lab mate. It may not be ready for the limelight, but is this piece of your analysis novel? Did you learn something worth sharing during the data collection? What have you made that's already good enough that someone else might find it valuable?