How can you get your first entry-level job as a Data Scientist or Analyst? Many focus on doing Kaggle competitions as a way to build their portfolios. Kaggle is an excellent way to practice, but it should only be one of many avenues you use to work on data science projects. This is because Kaggle competitions only focus on a narrow part of data science work.
To build your skills more holistically, it is a good idea to work on your own projects. It is common to share this code on GitHub to interest potential employers, but it is important to be very purposeful in what code you put up and how.
While it is fast to throw up some code on GitHub and hope someone looks at it, it is far more effective in the long run to put time and effort into how you construct and present your portfolio.
Make a portfolio
The reason a Data Science portfolio is useful is that it demonstrates that you can do the things that an employer wants to hire you for. It is effectively a substitute for the job experience that you lack. A strong Data Science portfolio is made up of several medium sized data science projects that combined demonstrate to the employer that you have the key skills that they are looking for.
Get on LinkedIn
Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles. Connect with Data scientist on LinkedIn and tell them you are looking for a data science position.
Get on GlassDoor
Read three data scientist job descriptions. Note skills you need to build and apply for a position, whether you are qualified for it or not.