We get it. You have a love for all things numbers, a slight obsession with spreadsheets, and you're always willing to learn new skills and keep your career prospects juicy.

You've heard the buzz about data analysts and are intrigued by their growing demand and popularity. 

Here we tell you what's causing the buzz and the top data analyst skills you'll need if you want to make that career jump and succeed as a data analyst. We also include steps on how to practise and build up your portfolio so that you get a head start for interviews.

What is a data analyst, and why are they in demand?

So much of our world is digital. There aren't many things left without an app or internet connection.

Every time we watch something, make a purchase, browse social media, and so on, we are creating data.

Imagine how much data is created daily by the billions of people going about their lives.

Yep, it's a LOT. (The current estimate stands at 1.145 trillion MB per day.)

This data is highly valuable for organisations to understand their customers or the marketplace.

But, the raw data is not ready to use as it needs to be organised and analysed for trends, patterns and ideas. That is the role of a data analyst, and that's why data analysts are in high demand.

In 2020 up to 2.72 million jobs required data skills, and 25% of business projects now use data analytics.

With the ongoing digital transformation, it seems likely that these numbers will only increase. A career in data analytics then, could be a smart move for you.

"Per LinkedIn, there has been a 650% increase in data science jobs since 2012. "
- Toward Data Science.

Here are the top data analyst skills you need to excel (pun intended)

1. Database Tools: SQL and Spreadsheets
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If you've done any tinkering with data in your spare time or a previous job, you are most likely aware that SQL is still definitely a necessary skill. Even though it's old, it's still the database language of choice for most businesses and doesn't look likely to change soon.

If this is the first time you've heard of SQL, you need to know it's a programming language used to manipulate databases.

Either way, it's a good idea to master SQL. It's a fundamental skill for data analysts and makes you much more likely to secure a job and perform in your role. 

The most popular SQL databases are MySQL with a 44.4% market share and Oracle with 18.7%. You can read more about the top database tools here.

And why not take a closer look at MySQL?

Spreadsheets:

You either love 'em or hate 'em - we're hoping you love 'em because they too are absolutely crucial if you want to work as a data analyst.

The more formulae and shortcuts you know, the easier your life becomes.

And if you're curious about other tools useful for data analysts, we've made a list here.

2. Programming languages

Did you know that programmers and data analysts have a lot in common?

A data analyst is a technical job, so it shouldn't be surprising that programming is a highly desirable skill. If you're a programmer looking to become a data analyst, you will have a massive advantage.

Programming languages suited to data, such as R and Python, make data crunching faster and way more efficient. It's worth putting in the time to keep your programming skills sharp as it gives you an edge that few people will have.

Why not start building a portfolio of relevant projects so that you're all set when you get your interview and you can prove you have the necessary data analyst skills?

If you've not programmed before, there are plenty of great resources to help beginners get started. 

We recommend starting with Python.

Did you know that the top 3 programming languages for data scientists in 2020 were Python, Javascript and Java? You can check out the full list here.

3. Spotting patterns/attention to detail

We believe this is one of the most crucial data analyst skills if you want to be above average.

The reason companies hire data analysts is to gain insight that will help them solve problems or grow their revenue. 

If you can spot things that are hard to notice or see beneficial patterns, you will be an asset to your company.

For example, if you work for a medical company, they might need your skills to understand a new disease: which age group is most resistant? Which lifestyle patterns help to build immunity? Which foods seem to increase the chances of having it? 

Feats like that make you a rock star!
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4. Data visualisation

Once you have spotted those patterns and details, you need to present your findings to your colleagues. They may not all be from a technical background and most likely won't want to look at the raw data.
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So the ability to create aesthetic and easy to understand graphs, charts and diagrams is a very important element of this role. 
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We highly recommend you start building your portfolio of different examples. Here is some beautiful inspiration. 

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5. Machine Learning

If you feel like machine learning comes up in every topic you read about, you're not alone. It is a bit of a craze right now but does have its place in data analysis.

Did you know:
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"Approximately 74% of all data scientists and top executives use machine learning tools for their performance analysis and reporting"?

And
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" 91.5% of the world's most important companies are already investing in machine learning, automation, and artificial intelligence."

Go here for more facts about machine learning and its impact.

HubSpot uses machine learning in its content management system. It helps them pitch better to prospective clients and improve customer service.

Call centres use machine learning in their conversation analytics to gain better insights.

Ever wonder how Netflix's recommendations get better over time? - or not if you're significant other shares your account - it uses machine learning to figure out your preferences. 

In other words, Machine Learning helps businesses delve deeper into their data and better serve their customers, so it's a high value skill if you want to work as a data analyst and deliver brilliant results.

The machine learning market is forecast to be worth $20.83 billion by 2024, so definitely worth your time and attention.

Also, if you are a little unsure about what machine learning actually is - below is a good video to start with:
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6. Presentation Skills

As a data analyst, you will regularly need to present your findings to different stakeholders in the company.

You'll need to use jargon-free language to explain your technical findings. That isn't easy, and if you have a talent for presenting complex topics in a simple yet engaging manner, you will be ahead of the game when applying for roles as a Data Analyst.

We've written an article about combining storytelling and data analysis to help make that presentation easier.

7. Communication
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Although the job title "Data Analyst" may make it sound like a lone wolf kind of role, this is very unlikely. You will be working with a team regularly and will often have to communicate your progress, your ideas, your problems and so on.

Your colleagues will be a diverse team of people, so good communication is crucial.

You might need to describe a complex problem or persuade the CEO that you've spotted something worth investing in. Each situation requires different types of communication styles.

You will need to be confident with all forms of communication, including writing, speaking and presenting with visuals.

It sounds straightforward, but in reality, very few people have a strong ability to take technical topics and explain them in a way that engages and educates the listener without boring or patronising them. 

If you are one of those few, you've got a headstart.

If you're not sure, why not start practising?

Dale Carnegie says:
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" 85% of your financial success in life comes from your personality, and your skills in communication, negotiation and leadership. Leaving only 15% of your career achievements rooted in technical knowledge". 

Here are some tips on communication that you can get started with straight away. And start nailing that 85%.

8. Critical Thinking & Problem Solving

As a data analyst, your role will be a lot more than processing numbers and creating charts in Excel. 

After processing the data and finding meaningful patterns, you need to think past the numbers.

What do the patterns mean for the business, and what should the stakeholders do?

Critical thinking is the ability to look at the data and ask questions, for example, what does the data indicate in terms of the companies goals? Have they accomplished what they wanted, or are there some areas for improvement?

If you work for an e-commerce company, you might need to figure out which ads generate the most traffic and sales.

Or, if you work in the property sector, you may be asked to forecast house prices for the next ten years.

Problem-solving is one of the most crucial data analyst skills.

So, how about testing and building up your critical thinking skills with these fun games.

And, if you'd like a more detailed explanation of what critical thinking is, check out the video below:

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9. Data Cleansing

As a data analyst, you'll have questions you need to answer and specific things to find in the data to help you problem-solve.

Initially, the data you need to analyse won't be ready to use.

Before you start, you will need to clean your data.

You will need to format it correctly and remove any data that is corrupt, duplicate, incorrect and so on.

This step is critical to the success of your processing. If you get it wrong, your results could be completely wrong as well - yikes, no pressure!

The hard thing is, the steps you take to clean the data could be different each time, but you can make your life easier by using a template, which is explained in detail here.

10. Math, Stats and Matlab

Data analysis is a numbers game, so you know Maths will be involved somehow.

You may be thinking: 'no problem, Excel does everything, right?'

Well, yes. But also no. You see, whilst you will have fancy tools to do all the maths processing, that can also be a problem, as there are so many available and they all work differently.

The more you know about how your tools work, the easier it will be to choose the right one for each problem. You're also more likely to spot any errors that the software creates - just like office printers, they all have their moments.

Matlab:

Matlab is powerful software widely used for its data processing ability. You may have come across it if you work in engineering, robotics or other similar fields.

It's definitely worth looking at, and we highly recommend adding it to your learning list.

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• Module 1: Data literacy 
• Module 2 - Analytics
• Module 3 - Experimentation
• Module 4 - Advanced analytics
• Module 5 - Data visualisation

• Module 6 - The skills of data teams

• Module 7  - Data Projects

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