A data analyst is an important part of the workforce in any business. The role has gained popularity as businesses have increasingly adopted a Big Data approach to decision-making. As businesses become more focused on delivering maximum value from their data, the role of a data analyst will only become more crucial to daily business operations.
Whether you’re looking to launch a career in data analysis or considering adding a data analyst to your team, it’s important to understand a data analyst’s responsibilities, essential skills, education requirements, and career opportunities.
What is a data analyst?
A data analyst is a professional who explores and analyzes data to understand its context better. A data analyst’s job is to collect, organize, analyze, evaluate, and communicate information.
They use advanced statistical methods to identify trends within large datasets that can help organizations make smarter decisions. This might involve identifying patterns in customer behavior or using predictive analytics to forecast future outcomes based on current situations.
Data analyst responsibilities
Data analysts are responsible for exploring and analyzing data to find meaningful insights. A data analyst typically uses data to determine possible causes for trends or issues that require attention. This role requires extensive knowledge of statistics and an ability to interpret findings for stakeholders.
Data analysts can work in any industry—private businesses, government agencies, nonprofits, schools, and other kinds of organizations can benefit from data analysts’ contributions.
Here are some responsibilities of a data analyst:
- Collect and cleanse raw data
- Prepare spreadsheets, databases, and analytics models
- Explore data by performing complex analyses on top of reported data to find insights
- Write reports and presentations to translate findings for colleagues and stakeholders
- Make recommendations based on findings and provide detailed business strategy proposals so business leaders can easily understand them and act upon them
Essential skills for data analysts
There are different skills required for being able to analyze data than there are for simply working with data. Additionally, some skills are essential for data analysts to establish themselves as leaders within a broader team.
Technical skills
The following technical skills are required to be successful in the role of a data analyst.
Data visualization
A data analyst uses tools such as Tableau and Power BI to create visual representations of data. This can help organizations understand the impact of their data and explain it to key stakeholders when necessary.
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Data cleaning
Before any kind of analysis can be done on a dataset, it must be cleaned up first. This means data analysts should be able to remove any irrelevant information from the data so only relevant information remains for further use. Analysts should also be able to add more fields to the dataset depending on the business’s needs.
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MATLAB
Data analysts use MATLAB for mathematical computations and scientific research purposes. It’s used primarily by analysts working with large-scale datasets because it avoids excessive lag time between computing sessions.
Basic knowledge of programming language
A data analyst should have a basic knowledge of programming languages such as Python, R, and others. Analysts use these languages to clean up and analyze data.
SQL and NoSQL
Both Structured Query Language (SQL) and Not Only SQL (NoSQL) databases are widely used by data analysts, but they serve different purposes. SQL is typically used for structured data, while NoSQL is typically used for unstructured data. Knowing both languages ensures they can work with the widest range of data structures.
Machine learning
Machine learning is a branch of artificial intelligence involving computers that can learn to perform certain tasks based on past experiences. There are many different types of machine learning structures, including Bayesian networks, decision trees, and neural networks.
Linear algebra and calculus
Linear algebra and calculus help data analysts determine trends within their datasets. With these branches of mathematics, analysts can make predictions about future outcomes based on those trends.
Microsoft Excel
While not technically a tool for data analysis, Microsoft Excel is still an important tool for anyone interested in pursuing a career as a data analyst. Because spreadsheets are designed to handle formulas, charts, pivot tables, and other data functions, they’re great tools for manipulating datasets before running them through more advanced analytical software like MATLAB.
Non-technical skills
Soft skills are also important for data analysts looking to have a positive impact on their organizations.
Problem-solving
In any organization, there will always be problems that need solving. It’s the job of a data analyst to solve these problems by identifying patterns in available data and recommending courses of action. A good analyst can think logically and approach problems from multiple angles to find the best solution.
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Presentation skills
Data analysts should be able to present and communicate their analysis clearly, taking into account existing business knowledge and acceptable approaches for handling data. Analysts are often expected to present their findings and recommendations clearly so other team members can act on them.
Read more on TechRepublic: How to develop your public speaking skills and advance your career
Communication skills
To help others understand the implications of their findings, data analysts need strong communication skills. It’s not enough just to crunch data; they must be able to convey its meaning accurately and persuasively. This is especially important in collaborative situations and group discussions.
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Critical thinking
Analysts use data points, statistics, and trends to make decisions based on evidence rather than emotion or opinion. As such, critical thinking is a key skill for anyone who wants to work as a data analyst.
Research skills
Data analysis involves gathering relevant information from various sources and interpreting it appropriately. This means conducting research using search engines and databases as well as sensitive corporate documents.
Attention to detail
Data analysts often deal with large amounts of data, which requires meticulous attention to detail. They have to check figures and percentages carefully to ensure they add up properly and don’t contain errors or misleading information.
Data analyst education requirements
There are three main paths to becoming a data analyst:
College degree
The most common way to become a data analyst is by getting an undergraduate or graduate degree in business, statistics, computer science, mathematics, or analytics. A bachelor’s degree typically takes four years of full-time study.
Alternatively, aspiring analysts can earn their degrees online from an accredited university with a program that allows them to work full time while earning their degree.
Read more: What Are CIOs Looking for in Current IT Grads?
Online certification course
Another way to become a data analyst is by taking one of several available certification courses. Courses range from basic entry-level courses designed for students with little or no experience in data analysis to advanced certification programs designed for experienced professionals looking to advance their careers.
Read more: IT Certifications vs Degree — What Should You Do?
Self-study
Finally, some professionals choose to pursue self-study to become data analysts. This approach requires analysts to do all of their own research and teach themselves everything about data analysis.
It also requires learners to be disciplined enough to follow through with all aspects of their training, including reading books, watching videos, completing tutorials, and studying sample problems until they can understand how everything fits together.
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Data analyst salary
According to Glassdoor at the time of this writing, the average salary for a data analyst is $62,344 USD per year. The highest-paid data analysts earn over $100,000 USD per year, while the lowest-paid data analysts earn less than $40,000 USD per year.
Data scientist vs data analyst vs data engineer: How are they different?
Though the roles of data scientists, data analysts, and data engineers are often confused, they serve distinct purposes and are not interchangeable.
| Data scientist | Data analyst | Data engineer |
| Data science practitioners are statisticians that have advanced knowledge of machine learning, predictive modeling, data mining, and big data. A data scientist essentially bridges the gap between business people and engineers. | Data analysts are professionals who sort through data and information to help organizations make better business decisions. Data analysts take numbers and facts, convert them into actionable insights, and present them in an easily digestible form for non-technical audiences. | Data engineers are professionals who create and maintain data systems for companies. The role of a data engineer differs from that of a data scientist in that it involves more hands-on work, such as writing code to build databases and integrate them with existing applications. |
Read more: Data Analytics vs Data Science: What’s the Difference?
Data analyst career path
The data analyst role can evolve to become more focused on a specific area of data analytics such as machine learning or data visualization. Many data analysts also take on people management responsibilities as they advance in their careers.
Long term, they may pursue executive leadership positions such as chief data officer (CDO), chief technology officer (CTO), or chief information officer (CIO). CDOs often focus on areas such as business intelligence, while CTOs are focused on building scalable infrastructure for their organizations.
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