Is Being a Data Analyst Right for You?

According to Lucidworks, most of the 7.5 septillion gigabytes of data generated every single day end up as dark data—information that’s collected but never analyzed.

By some estimates, half of all the data businesses collect becomes dark data; Lucidworks figures it’s actually closer to 90 percent. That’s a tremendous amount of information that could be driving business decisions on sales, logistics, marketing, and more.

One reason companies don’t use all this data is that they have neither the tools or the manpower to do so. The tools are evolving, which will make it possible to use more and more of this data in the future.

The people who wield those tools are called data analysts. These professionals figure out how to cut costs, produce more, and work more efficiently by reviewing and analyzing data. They then present their findings to business stakeholders in a simplified, easy-to-digest way.

Data analysts work in all sectors, from healthcare to marketing to retail to investment banking.

In every industry, they tackle four types of data analysis:

• Descriptive analytics: Used to determine what happened
• Diagnostic analytics: Used to explain why things happened
• Predictive analytics: Used to figure out what will probably happen
• Prescriptive analytics: Used to determine what actions to take

Some data analysts specialize in one type of analytics, but all data analysts are concerned with the same goal. Casey Pearson, marketing analyst at Delphic Digital, summed it up in an interview with the Rasmussen College technology blog when he said that analysts “hope to move our clients’ business forward based on their strategic goals.”

This article details how to become a data analyst and how much data analysts make. In it, we’ll cover:

• What does a data analyst do?
• How much money does a data analyst make?
• Education and training requirements for a data analyst
• Skills needed to become a data analyst
• Certifications for data analysts
• Data analysts versus data scientists
• Is this the right career for me?

What does a data analyst do?

Data analysts develop analysis strategies, gather, filter and clean data, analyze their results, and compile their findings into reports. What that work actually looks like can depend a lot on what kinds of data they’re playing with and what types of questions they’re trying to answer. They may also have to do research to find secondary data sources for comparison, maintain databases, and develop custom data collection systems.

When you become a data analyst, you will:

• Design and maintain data collection infrastructure: This is probably the most technical part of the job. You may be involved in database design, and that can mean coding or working closely with a web developer, data architect, or database developer.
• Mine data from primary and secondary sources: You’ll find ways to streamline data collection, and you may need to dig into historical data in addition to present-day data.
• Organize information in understandable formats: Raw data is seldom collected in a way that’s easy for people to interpret, or even for computers to read.
• Interpret data sets: Data analysts use statistical tools, software like Excel and Tableau, and programming languages like SQL to find trends, patterns, and other insights to drive business decisions.
• Prepare reports for executives and stakeholders: Data analysts present the relevant information and their findings in narratives that laypeople (both internally and outside of the organization) can understand. These may be generated weekly, monthly, or quarterly.
• Develop analytic dashboards for non-technical users
• Work closely with programmers and organizational leaders: You may be responsible for recommending beneficial system modifications and develop workable policies for data governance.

Depending on what industry or department you work in, your official title might be market research analyst, sales analyst, pricing analyst, financial analyst, operations analyst, marketing analyst, advertising analyst, or customer behavior analyst.

How much money does a data analyst make?

The average data analyst salary in the US is about $71,000 annually (based on Indeed.com surveys), but pay rates can vary widely by industry and by title. An junior data analyst might earn closer to $59,000, while a senior data analyst will probably earn about $86,000. The Bureau of Labor Statistics reports average annual income for business operations analysts, their closest category to data analysts, at $76,000.

Positions in major metro areas like Chicago, San Francisco, and New York tend to pay the most, and data analysts who have experience using a diverse set of techniques and software (e.g., SQL, Python, Tableau, Microsoft Power BI) usually make the most money, according to Dice.com.

Data analyst salaries tend to hit the ceiling over time. They typically increase over the first ten years of a career, but then max out. That’s why it’s not unusual for analysts to transition into data science or data engineering after five to ten years. They simply want to make more money.

Education and training requirements for a data analyst

You can land an entry-level data analyst job with nothing more than a bachelor’s degree. There are specialized undergraduate degree programs in analytics, though not many of them.

Schools that offer a BS in data analytics, or a similar degree, include:

• Mercy College
• Southern New Hampshire University
• Washington State University
• Webster University

A degree in math or statistics from a well-known college plus experience in programming, computer modeling, or predictive analytics—or a data science degree—will likely serve you just as well. Students in data analysis bachelor’s degree programs typically complete coursework in:

• Business Intelligence: This course would focus on techniques and tools for transforming raw data into meaningful and useful information for business analysis purposes. It includes learning about data warehousing, data analytics, and reporting techniques to help make informed business decisions.
• Database Design: This course teaches the principles of designing, implementing, and managing database systems. Students learn about data modeling, relational databases, SQL, and how to ensure the integrity and performance of databases.
• Data Mining: Data mining involves exploring large datasets to uncover hidden patterns, unknown correlations, and other useful information. This course would cover various data mining techniques like clustering, classification, regression, and association rules, and their application in real-world scenarios.
• Data Security: This course focuses on the methods and practices of securing data from unauthorized access and data breaches. Topics would include encryption, access control, network security, and compliance with legal and regulatory requirements.
• Data Visualization: This course teaches how to represent data in a visual context to make data analysis more accessible and understandable. It covers the design of dashboards, graphs, and other visual tools using software like Tableau or Power BI.
• Information Technology: This broader course covers the basics of IT, including hardware, software, networks, and databases. It is foundational for understanding how data systems work and how they are managed in a business context.
• Project Management: Essential for data analysts, this course focuses on skills needed to manage projects effectively, including planning, execution, monitoring, and closing projects. It also covers methodologies like Agile and Scrum.
• Regression Analysis: An important part of statistics, this course deals with understanding and identifying the relationship between variables. It is crucial for predictive modeling and forecasting in data analysis.
• Scripting: This course would cover programming (often in languages like Python or R) to write scripts that automate data analysis tasks, process large datasets, and integrate different data systems.
• Statistics: Fundamental for data analysis, this course covers statistical theories and methods. Topics include probability, hypothesis testing, descriptive and inferential statistics, which are essential for analyzing and interpreting data.

Earning an advanced degree isn’t necessary to become a data analyst, but it doesn’t hurt, and it may help you advance to a more senior position or a data scientist position later in your career. Master’s in data analytics programs are more common than bachelor’s degree programs.

Carnegie Mellon University offers a master’s program in data analytics, as does Tufts University. Boston University offers a fully online master’s in applied data analytics. These programs are usually designed for students with a strong STEM background. They typically include coursework focused on:

• Advanced Analytics: This course dives deeper into sophisticated analytical techniques beyond basic analytics. It covers advanced methods in predictive modeling, machine learning algorithms, and statistical techniques to analyze complex datasets and extract insights.
• Coding: A coding course for data analysts would typically focus on programming languages relevant to data analysis, like Python or R. It teaches how to write code for data manipulation, analysis, and visualization, as well as the basics of algorithm development.
• Data Governance Tools: This course would cover the principles and practices of data governance, including data quality, data management, and compliance with data protection laws. It involves training on tools and technologies used to ensure that data within an organization is accurate, accessible, consistent, and protected.
• Data Operations (DataOps): DataOps is a methodology for improving the quality and reducing the cycle time of data analytics. This course would focus on the practices, processes, and technologies for building and enhancing data pipelines to support the rapid, reliable, and secure management of data.
• Data Visualization: This course focuses on the graphical representation of data. It teaches how to effectively communicate information through visual elements like charts, graphs, and maps, using tools like Tableau, Power BI, or D3.js.
• Modeling Techniques: This course would explore various modeling techniques used in data analysis, such as linear and logistic regression, decision trees, and neural networks. It includes the application of these models to real-world data sets to predict and infer from data.
• Statistical Analysis: Essential for data analysts, this course covers statistical theories and methodologies for analyzing data. Topics include probability distributions, hypothesis testing, correlation analysis, and experimental design.
• Systems Architecture: This course covers the design and structure of software systems, including data storage, retrieval, and management. It’s important for understanding how data systems are built and how they can be optimized for efficient data analysis.

You typically don’t need to have earned a bachelor’s degree in data analysis or have worked as a data analyst to be accepted into these programs. Classes include hands-on learning opportunities suitable for those new to the profession.

Skills needed to become a data analyst

You may not learn everything you need to know to become a data analyst in a college program, but it’s possible to fill the gaps online, or from books. Sites like Udemy (aff.) have courses in programming, data sorting, database management, and tools like Hadoop. There are even beginner data analytics courses on the web that cost just a few hundred dollars. These are no substitute for a college degree, but if you earned a bachelor’s degree in math or economics, they provide a way to get started in data analytics.

Skills that will come in handy when your goal is to become a data analyst include:

• Excel (there’s no getting around the need for advanced Excel chops)
• SQL (Structured Query Language lets you create and retrieve information from databases)
• Python (even basic coding skills will boost your hireability, and Python is useful for data analysis)
• Apache Hadoop (good for manipulating large data sets across multiple machines)
• Apache Spark (a framework for writing fast programs)
• R (another useful programming language for analysts)
• Data visualization
• Data manipulation

Certifications for data analysts

Data analytics is a diverse field. There are several certifications for data analysts.

• The Associate Certified Analytics Professional (aCAP) designation (for entry-level analytics professionals)
• The Certified Analytics Professional (CAP) credential
• Columbia University’s Certification of Professional Achievement in Data Sciences
• SAS data science certifications
• Microsoft’s MCSE: Data Management and Analytics certification (you’ll need to pass one of 12 MCSE elective exams covering business intelligence, analytics, machine learning, and other topics first)
• Oracle’s Business Intelligence (BI) certifications
• Cloudera’s CCP Data Engineering certification

It can be tough in any industry to gauge what certifications will be the most valuable now and in the future. You can get a better sense of which certifications to prioritize by reaching out to working data analysts. Ask them which ones they’ve found to be the most beneficial.

Data analysts versus data scientists

Data analysts don’t usually play the same role at an organization as data scientists, though they both work with information. The dividing line between these two roles is getting muddied as the tools grow more sophisticated. Employers may cause further confusion by expecting their data analysts to do data science work, and vice versa.

There is definitely some overlap in the work that data scientists and data analysts typically do, but there are also key differences. Data scientists can interpret data and identify trends just like analysts, but they also have coding chops, machine learning expertise, and mathematical modeling expertise. What they do is much more technical and hands-on. They are also more likely to have advanced degrees—in fact, it’s not unusual for a data analyst to advance into a data scientist position after earning a master’s degree in data analytics and one or more relevant programming languages. It’s also worth noting that a senior data analyst may do work that’s indistinguishable from that of a data scientist.

Is becoming a data analyst right for you?

To answer this question, think about your passions and your personality. Do you love puzzles? Do you possess solid problem-solving skills? Are you a logical thinker? Do you prefer working on unstructured problems? Can you lose yourself in research or in a really juicy data set? On top of all that, do you like telling stories?

When you become a data analyst, your job will not only be to deconstruct, sort, and analyze information, but also to turn your findings into a fluid narrative that can convince stakeholders to take action to solve a problem. The ability to tell a compelling story may actually be the most crucial part of this job.

If you answered yes to all the questions above, this career might be a good fit. Just keep the potential salary cap in mind. What sounds like good money now may not feel like good money later. You can launch your career in data as an analyst—and spend your days bringing dark data into the light—while taking steps to gain the knowledge and skills you’ll need to transition into a data science role that pays a lot more.

(Updated on January 8, 2024)

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