8 Data Analyst Skills Employers are Hiring for in 2024

Introduction

From our extensive research, including interviews with data analysts, data scientists, and hiring managers, we've identified the key skills that are essential for a data analyst role.

In this updated guide, we also look into the evolving role of generative AI in data analytics. It is not just a buzzword; it's revolutionizing the field, opening up new opportunities for data analysts to enhance their capabilities and productivity.

What skills does a Data Analyst need to get hired?

Because the data science tools you will use vary depending on the role, the company, or the industry, we're going to focus on skills and not on common tools like Python, R, SQL, Excel, Tableau, Power BI, etc. 

Here’s what you’ll need to do as a data analyst (not how to do it).

The top 8 data analyst skills are:

• Data cleaning and preparation
• Data analysis and exploration
• Statistical knowledge
• Creating data visualizations
• Creating dashboards and reports
• Writing and communication
• Domain knowledge
• Problem solving

And here’s an explanation of each . . .

1: Data Cleaning and Preparation

Research shows that data cleaning and preparation will consist of about 80% of the work of most data professionals. That means this skill is vital.

A data analyst will commonly need to retrieve data from one or more sources and prepare it for numerical and categorical analysis. Data cleaning also involves resolving missing and inconsistent data that may affect analysis.

In data analytics, data cleaning isn’t always exciting, but preparing data can be fun and challenging when treated as a problem-solving exercise.

2: Data Analysis and Exploration

It might sound strange to mention “data analysis” in a list of required data analyst skills, but analysis as a specific skill is necessary.

Fundamentally, data analysis involves taking a business question or a need and analyzing relevant data to develop an answer to that question.

Another form of data analysis is exploration. Data exploration involves looking for interesting trends or relationships in the data that could bring value to a business.

A business question might guide exploration, but it also might be relatively unguided. By looking to find patterns in the data, you may stumble across an opportunity for the business to decrease costs or increase growth.

3: Statistical Knowledge

Probability and statistics are important data analyst skills. This knowledge will guide your analysis and exploration and help you decipher the data.

Additionally, understanding statistics will also help you ensure your analysis is valid, and it will help you avoid common fallacies and logical errors.

The exact level of statistical knowledge necessary will vary depending on the demands of your particular role and the data you’re working with. 

For example, if your company relies on probabilistic analysis, you’ll want a much more rigorous understanding of those areas than you would otherwise need.

4: Creating Data Visualizations

Data visualizations clarify data trends and patterns. Humans are visual creatures — that means most people will understand a chart or a graph more quickly than they will understand a spreadsheet.

This means creating clean, visually compelling charts that will help others understand your discoveries. It also means avoiding things that are either difficult to interpret (like pie charts) or that can be misleading (like manipulating axis values).

Visualizations can also be an important part of your data exploration. Sometimes, there are things that you can see visually in the data that can hide when you look only at the numbers.

It’s very rare to find a data science role that doesn’t require data visualization, which makes it a key data analyst skill.

5: Creating Dashboards and Reports

As a data analyst, you’ll need to empower others to use data to make key decisions. By building dashboards and reports, you’ll give others access to important data by removing technical barriers.

This might take the form of a simple chart and a table with date filters, or it might be a sophisticated dashboard containing hundreds of interactive data points.

Job descriptions and requirements can vary from position to position, but almost every data analyst job is going to involve producing reports on your findings or building dashboards to showcase them.

6: Writing and Communication

The ability to communicate in multiple formats is another key data analyst skill. Writing, speaking, explaining, and listening are all communication skills that will help you succeed in any data analytics role. 

Communication is key when collaborating with your colleagues. For example, in a kickoff meeting with business stakeholders, careful listening skills help you understand the analyses they require.

Similarly, during your project, you may need to be able to explain a complex topic to non-technical teammates.

Written communication is also incredibly important — especially when you’re writing a summary of your analysis or explaining a discovery in your data exploration.

Communicating clearly and directly is a skill that will advance your career in data. It may be a “soft” skill, but don’t underestimate it.

The best analytical skills in the world are worthless if you can’t explain what they mean and if you can’t convince your colleagues to act on your discoveries.

7: Domain Knowledge

Domain knowledge is understanding topics that are specific to the industry and company that you work for. 

For example, if you’re working for a company with an online store, you might need to understand the nuances of e-commerce.

By contrast, if you’re analyzing data about mechanical systems, you might need to understand how those specific systems work.

No matter where you work, if you don’t understand what you’re analyzing, it’ll make executing your job significantly more difficult.

This is certainly something that you can learn on the job. However, if you know a specific industry or area you’d like to work in, then building as much understanding as you can up front will make you a more attractive job applicant — and a more effective employee once you get the job.

8: Problem-Solving

As a data analyst, you’re going to run up against problems, bugs, and roadblocks every day. Being able to problem-solve your way out of them is another key skill that will be valuable as a data analyst. 

Here are a few potential scenarios: 

• You might need to research a quirk of some software or coding language that you’re using.

• Your company might have resource constraints that force you to innovate how you approach a problem.

• The data you’re using might be incomplete.

• You might need to perform some “good enough” analysis to meet a looming deadline.

Whatever the circumstances, strong problem-solving skills are going to be an incredible asset for any data analyst.

Will AI Replace Data Analysts?

The rise of AI has sparked debates about its impact on various professions, including data analysis. While the role will transform over the coming years, those with foundational skills in problem solving, analytical judgement, and insight generation will be in demand.

Generative AI will take on more and more routine tasks like data cleaning and preprocessing, and assist in complex tasks such as statistical analysis and visualization. It cannot replicate the creativity and strategic thinking that data analysts bring to their work. Those who embrace AI as a is a tool to automate mundane tasks will thrive.

Why Data Analysts should learn to work with Generative AI?

AI tools offer data analysts several advantages:

• Efficiency in Routine Tasks: AI can handle time-consuming tasks, enabling analysts to concentrate on interpreting results and providing strategic recommendations.

• Enhanced Analytical Capabilities: AI tools can process vast datasets quickly, uncovering insights that might be missed by manual analysis.

• Opportunities for Innovation: The use of AI in data analytics is creating new roles and opportunities, such as “AI Data Analyst” and “AI Strategy Consultant,” offering exciting career paths for those who adapt.

Getting Hired as a Data Analyst

In summary, becoming a successful data analyst requires mastering a diverse set of skills, from data preparation to strategic problem-solving. However, in today's rapidly evolving field, embracing the power of AI is equally crucial. AI is a game-changer, enhancing your efficiency and analytical prowess, and opening doors to innovative roles in data analytics.

Learning data analysis and AI doesn't need to be difficult. Completing these courses will not only enhance your resume but also your portfolio, setting you apart in the competitive job market.