These days, you’d be hard-pressed to find an industry not increasingly relying on data to make faster, better-informed decisions. That’s why businesses across disciplines are in need of knowledgeable data analysts who can translate raw data into actionable insights. According to the U.S. Bureau of Labor Statistics (BLS), employment for data scientists is projected to grow 35% between 2025 and 2035 alone, which is considerably faster than the national average for all occupations.
So, what does the work of a data analyst actually look like, and what does it take to thrive in this type of role? Delve into job responsibilities, career paths, educational paths, and more below.
What Does a Data Analyst Actually Do?
Although it’s easy enough for businesses to collect raw data these days, being able to make sense of that data calls for specific skills and expertise.
Enter the data analyst, who is primarily tasked with transforming raw data into meaningful insights that businesses can then use to answer questions and inform decision-making. While the exact responsibilities of a data analyst may vary from one role to the next, common duties include:
- Cleaning, organizing, and preparing data for analysis
- Identifying patterns and trends in data
- Visualizing and presenting data in a way that’s easy for non-technical stakeholders to understand
Where Can a Data Analytics Career Take You?
Because data has become such a major part of decision-making across industries, strong analytical and data literacy skills can lead to a number of rewarding career opportunities in tech, retail, finance, and beyond.
Market Research Analyst
These professionals are responsible for studying consumer behavior and industry trends to help brands better understand their target audiences and strengthen decision-making. In their everyday work, market research analysts may work with consumer survey data, demographic data, and even historical sales data to make predictions about future trends.
Operations Research Analyst
Meanwhile, operations research analysts are tasked with helping businesses solve complex operational challenges — often as they relate to supply chain management and resource allocation. Leveraging their analytical skills and mathematical expertise, they can help businesses improve performance while aligning decisions with long-term objectives.
Financial Analyst
For those with an interest in business finance and analytics, a career as a financial analyst may be suitable. These professionals are primarily responsible for using data analysis to help businesses make more informed financial decisions. This may entail offering guidance on investment opportunities, budgeting, and other financial matters based on interpretations of financial data.
Data Scientist
The roles of “data analyst” and “data scientist” are often referenced interchangeably, yet there are slight differences. Most notably, whereas data analysts typically work with historical and current data, data scientists may build predictive models to assess the likelihood of future trends or make forecasting predictions.
The Technical Skills You Need to Get Started
If you’re interested in any of the above roles, you might wonder how to become a data analyst. Aspiring data analysts benefit from a handful of key technical skills before they enter the field. These include (but aren’t necessarily limited to):
- Structured Query Language (SQL) – This is one of the most valuable technical skills for data analysts because it is used by many organizations to filter, retrieve, and manipulate large amounts of data in relational databases.
- Python or R – Being proficient in common programming languages like Python and R empowers data analysts to automate repetitive tasks, clean data sets, and support predictive modeling in their everyday work.
- Data visualization – Data analysts must often use data visualization tools (like Tableau and Microsoft Power BI) to communicate and present their findings in a way that is easy and accessible for non-technical stakeholders to understand.
- Excel – This traditional software remains an essential business tool for data analysts, especially when it comes to creating pivot tables and organizing large amounts of data.
- Statistics – Last but not least, data analysts need to have a solid understanding of statistics and its related concepts — including probability, correlation, hypothesis testing, and distributions.
What Skills Do Employers Actually Want Beyond the Tools?
While the above technical skills are important in any data analyst role, the reality is that modern employers are increasingly seeking out professionals who can combine their technical expertise with strong interpersonal skills and other core competencies. More specifically, employers hiring data analysts are looking for such soft skills as:
- Communication – Being able to explain technical findings to non-technical stakeholders is an invaluable skill, especially when critical decisions need to be made.
- Analytical thinking – Data analysts must think critically and ask the right questions before diving into their data. This requires analytical thinking skills, which can help professionals pinpoint underlying business problems and determine how to address them.
- Attention to detail – When working with data, even the seemingly smallest of errors can prove detrimental. With this in mind, data analysts need to pay attention to every last detail to ensure accuracy in their work.
- Business acumen – Data analysts who have a true understanding of their clients’ organizational goals, needs, and other nuances can ensure that their work supports strategic objectives.
Education Paths: What Background Do You Need?
Do you need to go back to school to become a data analyst? Ultimately, this will depend on your current level of education and experience, as professionals can enter this field from a variety of backgrounds.
Perhaps the most “traditional” route to a career in data analytics is holding a bachelor’s degree in a quantitative or business-related field. However, non-degree routes (like bootcamps, professional certificates, and even self-study) may be a practical option for some.
For aspiring data analysts seeking to build solid, foundational skills while setting themselves up for more advanced or specialized roles, a graduate-level degree, such as a master’s degree in data analytics, can be an excellent choice. In this type of program, students build the essential business, leadership, communication, and technical competencies needed to thrive in the field. Likewise, these are the kinds of skills that may be essential to pursue more specialized leadership roles — including that of senior analyst, management, and consulting positions within the industry.
How to Build Experience Before Your First Job
Long before you apply for your first entry-level role in data analytics, you can establish valuable experience along with an application-ready portfolio that showcases your technical expertise and core competencies.
Consider using public datasets (like those available on Data.gov and the United States Census Bureau) to practice analyzing real-world data for yourself. These self-guided projects can be invaluable in a portfolio, as can hosting projects on GitHub to demonstrate your proficiency in popular industry tools.
As you assemble a professional portfolio, aim to include three to five project samples that demonstrate essential competencies such as:
- Data cleaning and preparation
- Exploratory data analysis
- Statistical interpretation of data
- Interactive dashboards and/or visualizations built in Tableau or Power BI
- SQL queries for manipulating or retrieving data
Finally, don’t underestimate the value of internships, consulting projects, volunteer work, and capstone experiences as part of a graduate-level degree program. Incorporating work from these undertakings into a professional portfolio can help you set yourself apart as you prepare to hit the job market.
How Is AI Changing the Data Analyst Role?
Another common question people have about entering the field of data analytics is how exactly artificial intelligence (AI) is affecting the industry as a whole. The reality is that, although AI has reshaped many aspects of data analytics, it is a far cry from replacing data analysts. While AI can automate many repetitive tasks and assist with pattern identification, there remains a need for human oversight. Knowledgeable and skilled data analysts still need to verify data quality, be on the lookout for potential biases, and ensure that recommendations ultimately align with long-term business goals and objectives.
In other words, it’s useful for data analysts to be proficient in AI tools, but the need for actual human oversight remains indisputable.
Is a Graduate Degree in Data Analytics Worth It?
Rather than asking whether a graduate degree is “worth it,” it may be more helpful to consider what kinds of new opportunities it could unlock. For working professionals who already have undergraduate degrees and experience, consider whether a master’s in data analytics could accelerate your career progression or open doors to new opportunities in senior roles or other specialized positions with potentially higher pay.
Consider, too, that many graduate-level programs these days are delivered in a convenient online format — offering greater flexibility for working adults to incorporate an advanced education into their busy lives.
Take the Next Step in Your Data Analytics Career
If you’re ready to advance your technical expertise, interpersonal skills, and business acumen, Johnson & Wales University (JWU) Online’s Master of Science in Data Analytics could be your next step. With career-ready coursework and a flexible online format, this program is thoughtfully designed for working professionals looking to elevate their careers or pivot into a new field entirely.
For more information about completing your degree online, complete the Request Info form, call 855-JWU-1881, or email [email protected]. You can also start your application for admission here.