7 In-Demand Data Analyst Skills to Get You Hired in 2023

Each year, there is more demand for data analysts and scientists than there are people with the right skills to fill those roles. In fact, several data-focused careers were listed on the 2022 LinkedIn Jobs on the Rise list, including machine learning engineers and data engineers [1]. Whether as a long-term profession or a stepping stone to senior data-related positions, careers in data analytics offer a fast-paced and creative environment that is applicable to many industries.

But, what skills are the most in demand in the world of data? Let’s take a look at top data analyst skills, according to the Central Digital and Data Office [2].

1. Analysis and synthesis of data

Key skills for any data analyst, data analysis and synthesis allow you to organise, classify, and examine the findings from data. This can help organisations make informed decisions in several industries, including health care, business, higher education, marketing, and more.

Data analysis involves several quantitative analysis techniques, such as:

• Statistical testing

• Multivariate modelling

• Identifying trends

• Visualising distributions

• Policy analysis

Data synthesis brings together the findings from data analysis techniques to organise results and put them together into an easily understandable format. Following synthesis, data analysts ask questions such as:

• What areas need more investigation?

• Do the findings make sense?

• What steps should be taken following these results?

• Is the research consistent with existing findings?

2. Statistical methods and programmes

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, analyse, and visualise 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).

Learn your first programming language: If you’ve never written code before, Python for Everybody from the University of Michigan is a good place to start. After writing your first simple programs, you can start to build more complex programs used to collect, clean, analyse, and visualise data.

3. Maths and statistics skills

Statistics refers to the field of math and science concerned with collecting, analysing, interpreting, and presenting data. That might sound familiar—it closely matches the description of what a data analyst does.

Having strong statistical skills can help you avoid biases during analysis, identify trends that may be easily missed, and ensure the insights found accurately represent what the data is showing. While having a background in a quantitative field can help you build this basis, taking relevant courses can act as a beneficial refresher to make sure you are up-to-date on the latest technology and statistical methods available.

4. Data management

Data management refers to the practices of collecting, organising, and storing data in a way that is efficient, secure, and cost-effective. While some organisations will have roles dedicated to data management—data architects and engineers, database administrators, and information security analysts—data analysts often manage data in some capacity.

Different companies use different data management systems. However, SQL (Structured Query Language) is a common tool for data analysts to navigate and manage large quantities of data. Knowing this language can set you up for success in big data projects, and open opportunities for positions that require higher-level data management skills.

5. Data visualisation

Gleaning insights from data is only one part of the data analysis process. Another fundamental part is telling a story with those insights to help inform better business decisions. That’s where data visualisation comes in. As a data analyst, you can use charts, graphs, maps, and other visual representations of data to help present your findings in an easy-to-understand way.

Improving your data visualisation skills often means learning visualisation software. While there are several options to consider, most data visualisation softwares will allow you to make dashboards, data models, visualisations, and business intelligence reports. We recommend learning one of these industry-standard tools to take your data visualisation skills to the next level:

• Tableau

• Microsoft Power BI

• QlikView

• Datawrapper

• D3.js

• Google charts

• Excel

6. Project management

While data analytics might seem far from project management, having project management skills can give you the tools to communicate effectively across teams, understand how to drive a project to completion, and improve organisational skills to heighten your productivity.

Many companies thrive off of dynamic team environments, and being able to effectively work with professionals from varying backgrounds can help expand your ability to work on complex projects and network across disciplines. After all, data analysis is often only one step in a multi-stage process, and building your skills to work throughout the project lifecycle can open doors and allow you to continue growing within your professional career.

7. 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.

While data analysts aren’t generally expected to have a mastery of machine learning, 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. The UK has actively begun implementing AI technologies into its emerging technological strategies, including the development of the National Artificial Intelligence (AI) Strategy. Based on this, it is likely that the need for data professionals with machine-learning skills will grow over the coming years.

Tips for learning data analysis skills

Data analysts leverage these and other technical skills to help inform decisions at their organisations. Putting in the time and effort to learn these skills can set you up for a successful career as a data analyst. Here are a few quick tips for getting started:

• Set aside time to regularly work on your skills

• Learn from your mistakes

• Practise with real data projects

• Join an online data community

• Build your skills bit by bit

How to include data analyst skills on your CV

As you add new skills to your data analyst toolbox, be sure to update them on your CV as well. CVs should help show you are a good fit for the role and align your experiences with what hiring managers are looking for. Include a “skills” section with a bulleted list of around five of your top data skills. If you list a skill on your CV, be prepared to discuss it in your interview.

It’s also a good idea to incorporate your skills in context. When you include data analysis projects or previous roles, try to include a sentence on how you used a particular skill to complete a task (e.g. “Ran logistic regression on large data sets using R” or “used Microsoft Power BI to visualise product sales over time”).

Get started with Coursera

Prepare yourself for a career in data analytics by building job-ready skills with the Google Data Analytics Professional Certificate on Coursera. Learn how to clean and organise data with SQL and R, visualise with Tableau, and complete a case study for your portfolio—no prior experience or degree required. Upon completion, you can start applying for entry-level jobs in data analytics, or you can continue building your skills towards more advanced roles using data analytics skills.

Frequently asked questions (FAQ)