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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.

Probably the most valuable resource to me during my Masters research project was my notebook. I recorded everything about my project (meeting notes, analyses, ideas, theories, rough drafts) and even summaries of research papers that were interesting or relevant to my research.

It’s easy to run headfirst into writing code and analysing data and forget about project layout, so make it part of your routine to get started with a simple structure that you either maintain throughout the project or evolve as you go.

Starting a new data science project (especially for the first time) can feel like a monumental task. You’ve probably read a bunch of data science articles or gone through an online course and you’re keen to apply your new-found knowledge to some actual data (that does not involve petal width).