Joining the Data Industry in 2025

When I joined the data industry, there was a lot I got wrong.

And a lot more I’ve learned along the way since!

Here’s what I’d do if I was starting over in 2025…

Prioritise programming first, data second?

To be a good data analyst, you need to have a strong foundation in Python and SQL. Knowing how to program well means your code will run better and you’ll be able to debug more effectively.?

Spend some time becoming familiar with Python libraries for data analysis and learning NumPY and pandas for data manipulation. There are so many great tools like Kaggle, Leetcode and Codecadamy that can help learn the basics or brush up your skills.

Daily practice should get you to a good level of competence within a month for SQL and two months for Python.?

Spend time working with imperfect data?

One thing I wish I’d spent more time on is working with messy data.

Once I felt confident with the fundamentals, I was in for quite a shock when I saw what ‘real world’ data looked like!

Your job as a data analyst will be so much easier if you make it a habit to clean data from the beginning.

Here’s what I’d advise:

• Spend some time learning data cleaning and transformation with pandas
• Learn how to use regular expressions in Python data cleaning
• Ensure you can use SQL to query relational databases

And if you’re wondering where to find imperfect data?

Look all around you!

Use real-world datasets (you can find so many from a quick Google search) or create your own by surveying your friends and family on a topic you know is sure to generate differing opinions.?

Turn your practice into a portfolio

This stage is crucial if you’re looking to jump into a data role with no prior experience.?

I usually advise having around 3-5 strong projects in your data portfolio. If you’ve studied data science at university or through a short course, you probably have a good starting point to build off. But if not, don’t worry.

Everything you do as practice can constitute a valuable part of your portfolio. Turn your practice data analysis into eye-catching visuals that show potential employers your skills. Even better if you can find a way to get involved with real-world projects. The DataDNA challenges we run are a great way to do this. You can find out more by joining the DataDNA group here.?

Reach out, ask questions and network!

Let’s be honest - the data industry is a lot more competitive today than it was when I joined.?

But I’ve mentored so many people and they all had one thing in common - they were proactive in their networking and willing to go the extra mile to land a job or gain any advice necessary.?

Here’s what I’d recommend:

• Connect with not just recruiters on LinkedIn but also people working in the companies you’d like to join. Ask for 15 minutes of their time or send a question through. You’ll find most are more willing to help than you’d think!
• Attend in-person events like job fairs, meetups or data challenges. While you might not land a job directly, you never know where a connection made in-person could take you in the future.?
• Focus on smaller companies or startups to begin with. Sure, we’d all like to begin our career at Apple or Microsoft but realistically speaking, your first job is most likely not going to be your dream job.

With these tips, I’m confident you’ll be well on the way to securing your first job in the data industry!

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