5 In-Demand Data Analyst Skills to Get Hired

Transitioning to a career in data analytics can mean stable employment in a Top-paying industry once you have the right skill set.

But what skills are the most in-demand in the world of data? These seven trending data science skills represent those with the most searches and enrollments by Coursera’s community of 65 million global learners. To prepare for a new career in the high-growth field of data analysis, start by developing these skills.   

Let’s take a closer look at what they are and how you can start learning them.

Microsoft Excel

While I have bashed Excel a bit in this article for scalable, repeatable analysis it still very much has its place for data analysts. 42% of job openings still require data analysts to know Excel (Adavanced).

The primary reason for this is that Excel is the tool most often used by business leaders for any quantitative analysis. It’s the common numerical language across the entirety of a company.

For me the biggest use case of Excel was when I wanted to create interactive models or tools for my non-technical colleagues. When I wanted to empower the team to change assumptions in a given analysis and see the outcome, I used Excel.

Excel is also really good for quick and dirty analysis. The best way to learn Excel is to get your hands dirty. There are thousands of resources online to learn Excel for beginners and advanced users since it’s a ubiquitous tool in business.

SQL

Structured Query Language, or SQL, is the standard language used to communicate with databases. Knowing SQL lets you update, organize, and query data stored in relational databases, as well as modify data structures (schema). 

Nearly all job interviews for data analyst positions have a technical component where you’ll be asked to write SQL queries. When I leveled up my SQL skills I was able to ace these interviews and increase my compensation by over 100%.

More than just helping you get a great data analyst job, SQL is also a great introduction to the world of programming. To get data from a database in the format you need for analysis you’ll need to think logically and thoroughly about the queries you write.

Since almost all data analysts will need to use SQL to access data from a company’s database, it’s arguably the most important skill to learn to get a job. In fact, it’s common for data analyst interviews to include a technical screening with SQL. 

Luckily, SQL is one of the easier languages to learn.

Statistical Programming

Statistical programming languages, like R or Python, enable you to perform advanced analyses in ways that Excel cannot. Being able to write programs in these languages means that you can clean, analyze, and visualize large data sets more efficiently.

Both languages are open source, and it’s a good idea to learn at least one of them. There’s some debate over which language is better for data analysis. Either language can accomplish similar data science tasks. While R was designed specifically for analytics, Python is the more popular of the two and tends to be an easier language to learn (especially if it’s your first).

Machine Learning

Machine learning, a branch of artificial intelligence (AI), has become one of the most important developments in data science. This skill focuses on building algorithms designed to find patterns in big data sets, improving their accuracy over time.

The more data a machine learning algorithm processes, the “smarter” it becomes, allowing for more accurate predictions. 

Data analysts aren’t generally expected to have a mastery of machine learning. But developing your machine learning skills could give you a competitive advantage and set you on a course for a future career as a data scientist.

Business Intelligence Tools

There are an uncountable number of business intelligence tools on the market right now. The good thing about BI tools though is that once you know one, you can quickly learn others. Which one should you focus on learning?

Tableau & Power BI was the BI tool of choice for 46% of the data analyst job openings in our dataset. Tableau has free training lessons and it’s easy to get started so this is what I recommend you learn first.

What do BI tools actually do? Simply put they make scalable data visualizations accessible to non-technical business stakeholders. Once an analyst creates a data visualization in a BI tool the underlying data can automatically be refreshed so anyone in a company can get real-time insights into the metrics they care about.

To get the most utility out of BI tools an analyst will need to use SQL. I want to make the point again, if you don’t know SQL that’s undoubtedly the skill you should learn first!

From my experience if you become really good with the BI tool your company uses then your perceived (and actual) value within the company will skyrocket. Good organizations know that data should be at the heart of all decisions they have to make and if you are the one that can make that data accessible and usable then you become an asset to the organization.

If you know SQL and a BI tool like Tableau & Power BI you’ll be able to succeed in many data analyst roles. These are the two major requirements for most companies looking to hire a data analyst. If you know these two skills then you’re well on your way to a successful data analyst career.

If you want to take the next major step in your data analyst journey, you’ll need to learn a programming language.