In the dynamic world of data analytics, the role of a data analyst is multifaceted, demanding a diverse set of skills. 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 artificial intelligence (AI) in data analytics. AI is not just a buzzword; it's revolutionizing the field, opening up new opportunities for data analysts to enhance their capabilities and productivity.
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’swhatyou’ll need to do as a data analyst (nothowto 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 8
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.
Interested in this skill? Check out our beginner-friendly data cleaning courses.
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.
Interested in this skill? Check out our list of data analyst courses.
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.
Interested in this data analytics skill? Check out our probability and statistics courses.
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 thatdoesn’trequire data visualization, which makes it a key data analyst skill.
Interested in this skill? Check out our data visualization courses.
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.
Interested in this skill? Learn how to create dashboards and reports with our Power BI courses.
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 circ*mstances, 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. However, history shows that technological advancements typically transform jobs rather than replace them entirely. In the field of data analytics, AI is set to be a tool that enhances, not replaces, the capabilities of data analysts.
AI can automate routine tasks like data cleaning and preprocessing, and assist in complex tasks such as statistical analysis and visualization. But it cannot replicate human creativity, strategic thinking, and the personal touch that data analysts bring to their work. Thus, rather than being a threat, AI is a powerful ally that can free analysts from mundane tasks, allowing them to focus on high-value activities that require critical thinking and domain expertise.
Similarly, there may be skills some companies will require that aren’t on this list. Our focus here was to find the set of skills thatmostdata analyst roles require in order to build the very best data analyst learning paths for students.
Why Data Analysts Should Embrace AI?
Embracing AI offers 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.
The integration of AI in data analytics is not just inevitable; it's already happening. By embracing AI, data analysts can enhance their skills, increase their value to organizations, and secure their place in an evolving job market. The future of data analysis is one where AI and human expertise collaborate to drive innovation and strategic decision-making.
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.
At Dataquest, we're committed to equipping you for this future. Our courses are designed to provide you with the essential skills for a data analyst role and to introduce you to the exciting opportunities AI offers. Specifically, our "Junior Data Analyst" and "Machine Learning in Python" courses are tailored to help you navigate the intersection of data analysis and AI effectively.
Specifically, our "Junior Data Analyst" and "Machine Learning in Python" courses are tailored to help you navigate the intersection of data analysis and AI effectively.
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.
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