Data science is a pretty hot industry, but that means that breaking into it is getting harder and harder. Training resources (e.g degrees, certificates, and bootcamps) are exploding – but that just means the supply of qualified entry-level workers is swamping the current demand. In the happy days before 2015, anybody looking for a data science job could easily get multiple interviews without trying hard. Today, any advertised position typically receives more than 200 applications. And that’s usually just in the first 24 hours!

These are the best tips from Harnham for making your data science job search faster and more effective:

1) Dial Back Your Social Calendar; Cut Out TV

This is a piece of general advice I like to give to anyone aiming at a serious goal that requires sustained high-intensity effort. It works for launching a startup or finishing a degree as well as finding a job.

Main Points:

* Make sure friends, relatives, and family members know that you’re prioritizing an important career goal for the foreseeable future.

* List the upcoming shows you want to watch and set the list aside. You can treat yourself to a mega-binge after you land a job.

2) Pursue Relevant Side Projects

If you’re serious about data science, you probably have more than a few “why doesn’t somebody do X” side projects floating around your head. Indulge some of those left-field thoughts and see if you can accomplish something of note with publically-available data.

Main Points:

* Target either a question you find interesting or one related to your current/prospective career.

* Do everything you can with the data. Not just engineering but modeling and visualization as well.

* The bigger the dataset, the better.

3) Keep An Up-To-Date, Living Resume

One thing that shocks me over and over is how many people I meet who are in the market for a job but who don’t have an up-to-date resume. Your resume should be updated at least annually, and refreshing more often is an absolute necessity if you’re actively seeking a job. Updating incrementally is important when you’re trying to break into the industry. As you complete relevant projects and acquire new proficiencies, add them to your resume.

Main Points:

* Include any projects (work or side) that are relevant to data science.

* List all the technologies and frameworks you can work with.

* Keep your up-to-date resume handy and easy to share with anyone who asks.

4) Practice For Interviews

Online resources can hook you up with tons of practice material for job interviews. Glassdoor maintains a great interview question archive, for instance. Sharpen your skills with both data analysis questions and coding challenges.

Main Points:

* Schedule an hour a day for interview practice. Rotate through different question types.

* If possible, arrange a mock interview with someone who’s already working in data science or IT.

5) Network Shamelessly

Of all the different activities you can pursue in the quest to land a data science job, networking has the very highest ROI. I don’t care if it’s a cliche; you have to recognize the truth that who you know matters as much as what you know.

Through networking, you can find out about brand-new job opportunities, learn about potential employers, and get a better understanding of where your background would be most useful.

Main Points:

* Set a weekly goal to meet new people working in data science or IT.

* Reach out to past coworkers and college friends. Go to meetups.