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Task automation has become an invaluable tool in my arsenal as a data scientist. My only regret is not getting started with automation sooner. In the past, I have felt intimidated by the process of automation because I always thought it was an intensive, tedious process.

Those companies that view data as a strategic asset and develop robust data and analytics strategies are the ones that will succeed in this new data-driven world. — Bernard Marr

It is quick and easy to start analysing Google Sheets data in Python using the EZSheets library. I typically write python scripts that automate the extraction and analysis of data stored in Google Drive.

The Python logging module has been part of my toolkit since the very beginning of my data science journey (thanks to a former colleague who helped me build my first web scraper and showed me some good practices to start with early rather than later).

Power BI has been my go-to data visualisation and business intelligence tool for the last 3 years. I have built corporate dashboards with it and used it as my own little exploration tool when I’m tackling a new data set for the first time.

At what point do you get to declare “I am a data scientist”? How many PhD’s, decades of experience or deep learning models do you need to have under your belt before you feel like a ‘real’ data scientist?

If you have read my beginners guide to building a data science portfolio then you already know how important it is to find a way to demonstrate your skills, especially if you don’t have any work experience yet.

The projects in your data science portfolio should show that you have mastery in a particular area within data science. At the end of this post you should know how many projects to include as well as the types of projects that should make up your portfolio.

Some of the biggest, most innovative tech giants are still struggling with gender equality in their workforce. Only 33% of the employees at Google are female and only 23% of the workforce at Facebook is female.

If you have little to no experience as a data scientist, then you have to find a way to demonstrate that you have the skills to do the job.