How to Become a Data Analyst

Corporate leaders once could confidently say that they relied on “gut feeling” or their own experience to make decisions. But those pronouncements obviously don’t fly in today’s data-driven world. Organizations of all kinds now depend on data analytics—and on the analysts who use data to provide critical insights needed to create winning business strategies. Data analysts collect, process and analyze data to extract insights and support decision-making so organizations can optimize operations and solve business problems.

If you are intrigued by the power of data science, read on to learn how to become a data analyst and make the most of opportunities in this in-demand field.

Globally, the big data analytics market was valued at $307.51 billion in 2023 and is only expected to grow.¹ Projections are showing a jump in market value to $924.39 billion by 2032.¹ This means the job outlook for data analysts in 2024 remains highly promising.

As businesses continue to emphasize data-driven decision-making across industries like finance, healthcare, technology and retail, the demand for skilled data analysts is expected to grow. The U.S. Bureau of Labor Statistics anticipates that data science roles will grow by 36% through 2033, which is much faster than the average for all occupations.² Data analysts also earn competitive salaries, with the median total pay (which includes bonuses, profit sharing, etc.) in the U.S. is $109k per year.³

Even with a continuing high level of demand, data analysts with the right combination of skills and knowledge are most likely to stand out in the job market.

If you are interested in becoming a data analyst, it’s helpful to understand the roles that exist in the field.

Data analysts are problem solvers. A data analyst provides insights that make data meaningful for an organization’s decision-makers. They translate the indecipherable—raw numbers and raw data—into functional, everyday language. They might also take undeveloped ideas and then use data science to develop new products and solutions.

Data analysts may help develop automated routines for extracting requested information from database systems (i.e., a customer satisfaction survey or raw sales data), often in partnership with data architects, data scientists and database developers. Data analysts might be asked to design and develop machine learning systems, taking problems from computer vision to natural language understanding.

Data analysts look for patterns and trends that can be used to make recommendations to management. Data analysts often must present these findings to stakeholders, who can use the information to develop next steps.⁴

Data analysts are just one set of professionals who work with data. Let’s explore the differences between data professionals, including data analysts, data scientists and data engineers.

• Data Analyst: Data analysts focus on collecting, cleaning and interpreting data to provide actionable insights. Their primary responsibilities include data visualization, creating dashboards, identifying trends and making recommendations based on data findings. They use tools like SQL, Excel, Tableau and Power BI for analysis and reporting⁴
• Data Scientist: While data scientists also analyze data, their role is more technical and research-oriented. They use advanced statistical models, machine learning algorithms, and coding skills (Python, R) to create predictive models and automate analysis. Data scientists often work with unstructured data and engage in exploratory data analysis to discover new insights⁵
• Data Engineer: Data engineers focus on the infrastructure side of data. They build and maintain the systems and pipelines that allow data to be collected, stored and made accessible to data analysts and scientists. This involves working with databases, cloud platforms and ETL (Extract, Transform, Load) processes, using tools like Hadoop, Spark and various data storage techniques

While all three roles overlap in dealing with data, the key difference lies in their focus: analysts interpret and present data, scientists model and predict, and engineers handle data architecture and flow.

The career path for a data analyst often involves moving into more specialized roles such as data scientist or data engineer. As analysts gain experience in handling more data sets and using advanced tools, they can transition into data science by learning more about machine learning, statistical modeling and coding. Some analysts may prefer the engineering route, focusing on data architecture and infrastructure.

• Transition to Data Scientist: This often requires learning additional skills in statistics, machine learning, and advanced programming languages like Python or R. Experience with modeling, hypothesis testing and deep learning can be crucial⁷
• Transition to Data Engineer: Data analysts looking to move into engineering typically gain expertise in databases, cloud platforms, and ETL processes. Learning tools like Apache Hadoop, Spark or cloud services (AWS, Azure) is essential for this transition⁸​

The job market for data analysts is vibrant, with growing demand across industries such as healthcare, finance, marketing and technology. Companies are increasingly leveraging data to drive strategic decisions, so skilled data analysts are essential. Whether you’re entering the field or seeking to advance your career, there are numerous opportunities for aspiring data analysts to explore across various sectors.

Data analyst interviews often include both behavioral and technical components. Here’s how to prepare:⁹

Behavioral Questions
Employers will ask about past experiences and problem-solving approaches. Examples include:

• Can you describe a time when you had to clean a large dataset?
• How did you use data to influence a business decision in a past role?
• How do you prioritize competing tasks and deadlines?

Technical Questions
Prepare for questions that assess your knowledge of analytical tools and statistical concepts, such as:

• What is the difference between a JOIN and a UNION in SQL?
• Explain the concept of regression analysis.
• How do you handle missing data in a dataset?

Technical Tests
Many companies include technical tests that evaluate your proficiency in data analysis tools like SQL, Excel and Python. To prepare:¹⁰

• SQL: Brush up on querying databases, especially skills like joins, subqueries and aggregations
• Data Manipulation: Be ready to clean, manipulate and visualize data. Practice tasks involving pivot tables in Excel or data wrangling in Python with libraries like Pandas
• Statistics: Expect questions on basic statistics and probability, including hypothesis testing and regression analysis

Mock interviews, coding challenges on platforms like LeetCode or HackerRank, and practicing with real-world datasets can help you feel more confident going into interviews.

To become a data analyst, you must understand the complex technical and business skills needed for the job. Those who seek to become a data analyst must be familiar with unstructured data dumps, machine learning, data visualization tools and current analytics tools, but the Online Master of Science in Business Analytics (MSBA) from William & Mary also emphasizes that data analysts must be able to interpret the complex information gathered through these means and effectively communicate the implications for the organization’s bottom line.

Data analysts use a range of business intelligence and analytics tools for data mining, statistical analysis, database management and reporting. Data analysts are typically expected to be proficient in software like Excel and programming languages like SQL, R, SAS and Python.

Search LinkedIn and Indeed for “data analyst” and read the job descriptions to understand the skills employers want when hiring data analysts. You’ll also get an idea of which companies across many industries hire data analysts.

Technical skills are key, but when strategically thinking about becoming a data analyst, it is crucial to consider some of the “soft skills” employers seek.

What are soft skills? Communication and presentation skills are especially important for data analysts. Since data analysts often work as part of a team, it is imperative to have good organizational and collaboration skills. Your people skills will come into play as you work with executives and others who may not be as technologically savvy. You might need to be creative and patient in explaining problems and solutions in non-technical terms.

Becoming a data analyst almost always requires at least a bachelor’s degree. Successful data analyst job candidates must show significant credentials and quantitative skills, including statistics, probability and algebra, as well as business acumen.

Although a master’s degree is not usually required, many employers prefer candidates with advanced degrees. Master’s programs cover deeper topics like machine learning, artificial intelligence and complex statistical methods. This can be especially useful for those looking to transition into more specialized roles like data scientist or data engineer. Entering a master’s program also allows you to complete an internship. As with many fields, possessing an advanced degree can help you attain a salary at or near the top of the expected salary range for data analysts. William & Mary’s Online MSBA is a step toward a more lucrative career.

In addition to earning an advanced degree, certifications can be a strategic way to stand out in the competitive data analytics field. They demonstrate expertise in specific tools, platforms or methodologies and can also serve as evidence of continuous learning. Some popular certifications include:¹¹

• Certified Analytics Professional (CAP): Validates broad analytics expertise across various industries
• Google Data Analytics Professional Certificate: Focuses on foundational data analytics skills using Google tools
• Microsoft Certified Power BI Data Analyst Associate: Demonstrates proficiency in using Power BI to create data-driven solutions
• SAS Certified Data Scientist: Ideal for those focusing on statistical software like SAS for data analysis and modeling

These certifications enhance job prospects and contribute to career growth, potentially opening doors to higher-paying and more advanced roles.

As data continues to play a central role in decision-making across industries, several trends are shaping the future of data analysis. The integration of advanced technologies like artificial intelligence (AI), real-time data processing, and a shift towards democratizing data are expected to redefine how data analysts work and what skills they will need to thrive in the data analyst jobs of the future.

Artificial intelligence is rapidly transforming the data analysis landscape. AI-driven analytics automates many aspects of data collection, processing and interpretation, enabling businesses to derive insights faster and more accurately. With tools like machine learning (ML) and natural language processing (NLP), AI can identify patterns and correlations in large datasets that might otherwise go unnoticed. For example, AI-powered platforms like AutoML allow even non-technical users to build predictive models with minimal coding knowledge, while advanced algorithms streamline data cleaning and preparation.¹²

The rise of augmented analytics—a subset of AI-driven analytics—further empowers data analysts by automating routine tasks like report generation and trend identification, allowing them to focus on more strategic data analysis projects. As a result, data analysts are likely to become more involved in overseeing AI systems and interpreting their outputs.

As organizations move towards real-time decision-making, the demand for real-time data analysis is growing. In contrast to traditional methods that involve analyzing historical data, real-time analytics involves processing and analyzing data as it is generated. This is particularly critical in industries like finance (for fraud detection), telecommunications (for network optimization) and e-commerce (for personalized customer experiences).

The rise of the Internet of Things (IoT), with its constant stream of sensor data, further drives the need for real-time analytics. Data analysts will need to work closely with streaming data technologies like Apache Kafka, Spark Streaming and cloud-based platforms (e.g., AWS Lambda) to handle massive amounts of real-time data efficiently.¹³

Data democratization refers to making data accessible to a broader range of employees within an organization, enabling them to analyze and act on data insights without relying solely on technical experts. As tools become more user-friendly, platforms like self-service BI (business intelligence) and AI-based analytics are enabling non-technical users to run queries, visualize data and generate reports.

For data analysts, this shift means they may spend more time guiding others on how to use data effectively and ensuring the data integrity of democratization efforts. As companies adopt this approach, data analysts will also take on more strategic and oversight roles, ensuring data quality and helping interpret insights generated by non-technical staff.¹³

Data science and analytics are driving the future of business. William & Mary’s Online Master of Science in Business Analytics (MSBA) blends the essential elements of business expertise with a robust portfolio of data science offerings. Learn more about how this degree gives you a competitive advantage in the workplace and lifelong connections to William & Mary’s unmatched alumni network.

Our rigorous curriculum covers business acumen, applied math, computing technology and effective communication. During your program, you’ll learn from top experts, and you can tap into our extensive network of business leaders. Learn more about the application requirements to get started.

Schedule a call with one of our admissions outreach advisors today to learn more about the value of the Online MSBA.