5 Simple Steps to Kick-Start A Data Science Career

So you’ve decided you want to pivot your career into data science.

That’s awesome.

Below you can see the trends data on Data Science as a field of study. As you can see it has been increasing over the last 5 years.

As the upward trend in Data Science careers continues, it’s a great time to get involved.

How do you get into data science?

These are 5 steps you can take. These steps will give you an understanding of the skills required and help you build a network that can drive your data science career.

Understanding the basics

The first step is to make sure you have a good understanding of the area of data science you want to work in.

My recommendation is to do some research on job boards and see what interests you. This will give you a good idea of the types of skills that are required to get your dream job.

As an example, when I first began scoping out a move into data science, specifically machine learning, career, I looked at jobs in that space.

This helped me understand the programming language I needed to learn. Furthermore, it gave me a good overview of the skills employers were looking for.

Complete a course

The next step is to learn the skills you have identified. Where there are gaps in your current portfolio or CV, you need to fill them.

I find online courses a great way to do this. There are plenty of courses available to you. You can also find free courses online through YouTube.

If you are specifically interested in machine learning, the following posts can help you find your perfect solution.

• Choosing a python machine learning course
• YouTube channels to learn machine learning techniques.

Develop Your Soft Skills

The experience you have gained in previous jobs or from courses is relevant.

You will have developed soft skills throughout your life that can help you in a data science career. Don’t underestimate the value in these skills.

For example, you will likely have developed some level project management and communication skills through previous roles. These are very applicable to any job you would want in data science.

Even if this is going to be your first professional role, the process of teaching yourself the data science skills, teaches you to research. Being able to research problems effectively is a core skill for a data scientist.

Meetups and community

The old saying goes, it’s not what you know but who.

Though this could be considered unfair, it is an unfortunate fact of life. If you are looking to make a career pivot don’t underestimate the power of networks.

One fun way you can build up a good network is through Meetups. Tech meetups happen all over the world. They are free to attend, you’ll learn something new, and best of all, they usually have free food!

Head on over to one in your area and try to speak to at least one person. I know it can seem scary, but the tech community is very welcoming.

You never know what opportunities will come from the connections you make.

Build a Data Science Portfolio

The final recommendation is to start building out your portfolio. Once you are comfortable with the entry-level processes you need to run data science projects, you can start getting some projects under your belt.

You can start with example projects to practice that you can find online. Once you feel ready, you can then look at taking on clients through a platform like Upwork.

Following these 5 steps will help you get your dream career in data science.

First things first, you need to get yourself a machine learning project portfolio. Click here to join the FREE Bootcamp that will help you build a strategic portfolio that will get you noticed.

To summarise you can:

1. Understand the basic requirements from job listings
2. Fill the gaps in your CV with courses
3. Leverage existing soft skills to stand out
4. Build your network at Meetups
5. Develop a portfolio through completing freelance or other projects online

If you can put in the work, there’s no reason why you wouldn’t be able to succeed.

Good luck and enjoy the journey!

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