According to job portal Glassdoor, the average base pay for an entry data analyst in the United States in December 2021 is $69,517. The median annual income for a data analyst is $86,200, according to the US Bureau of Labor Statistics, while the midpoint salary for a data analyst is $106,500, according to human resources consulting firm Robert Half.
While the range varies, each of these compensation amounts is much greater than the US average yearly salary of $56,310 for all occupations.
As a data analyst, your income will be influenced by a number of things. Let’s look at a few of these factors in more detail.
To translate data into better data-driven business decisions, data analysts utilize mathematical and analytical methodologies. The demand for qualified data analysts to process and understand data is growing as the amount of data available to businesses grows. Data analysts are usually well compensated for their work.
You’ll learn how much data analysts make on average in this article, as well as how experience, industry, location, and job title might affect your data analyst compensation. We’ll also discuss some techniques to increase your earning potential if you’re interested in starting or advancing your career as a data analyst.
Factors that Influence Entry Level data analyst salaries
Your amount of experience is one of the most important aspects that might influence your income. In general, the longer you work as a data analyst, the higher your salary will be. Taking on a leadership position can help you make more money. According to Glassdoor, analytics managers in the United States make an average of $121,232, while analytics directors earn $147,147.
Data analytics may be used in almost every industry to make better business decisions. However, the industry in which you choose to work can affect your compensation. The industries with the most demand for data specialists are also the ones that pay the most on average.
According to The Quant Crunch, an IBM report on the demand for data science capabilities, finance and insurance, professional, scientific, and technical services, information technology, management, and manufacturing account for more than three quarters of data job vacancies.
Salary ranges for data analysts by location
The area in which you live might have a significant impact on how much money you can make as a data analyst. Working in a large metropolis such as San Francisco, New York, Boston, or Washington, DC is usually associated with a higher wage (as well as a higher cost of living). As more organizations employ a geographically scattered workforce (including remote workers), it’s becoming more typical for them to offer location-based salaries—pay that is dependent on where you work rather than your qualifications alone.
Future of Data analysts
As the amount of big data grows, so does the demand for talented data analysts who can turn it into useful insights for businesses. Data analyst employment is expected to expand 25% between 2020 and 2030, substantially faster than the average for all occupations, according to the US Bureau of Labor Statistics.
This is largely spurred by the increasing adoption of big data analytics by businesses. By 2025, more than 80% of businesses polled for the World Economic Forum’s Future of Jobs 2020 report claimed they would be employing big data. Data analysts and scientists topped the list of positions with rising demand across industries, according to the same research.
What is the role of a Data Analyst?
Data analysts figure out ways to use data to answer questions and solve problems. They look at what’s going on right now in order to spot trends and create predictions for the future. They act as investigators, deducing how things function and assisting in the deciphering of complex situations. It can be a fulfilling, creative, and hard career.
To calculate their statistics, data analysts often employ computer systems and computation tools. Data must be regulated, normalized, and calibrated in order for it to be extracted, used alone, or combined with other numbers while maintaining its integrity. Facts and figures are the beginning point, but comprehending what they represent and presenting the results in an engaging manner utilizing graphs, charts, tables, and graphics is crucial.
Not only must data analysts be able to analyze data, but they must also be able to report and explain what discrepancies in figures indicate when compared year to year or across departments. Data analysts are frequently asked to advise project managers and department heads on specific data points and how they might be modified or improved over time because they have the best understanding of why the numbers are the way they are.
How can I become a Data Analyst?
Make a study schedule.
If you’re new to the field of data analysis, you should begin by learning the basics. Getting a thorough understanding of data analytics will help you decide if this is the right career for you while also providing you with marketable abilities.
Most entry-level data analyst employment used to require a bachelor’s degree. While many jobs still require a bachelor’s degree, this is starting to change. While a degree in math, computer science, or another comparable field might help you gain basic knowledge and improve your CV, you can also learn what you need through alternative programs such as professional certificate programs, bootcamps, or self-study courses.
Develop your technical abilities.
Getting a job in data analysis usually necessitates a set of specialized technical abilities. These are some essential abilities you’ll likely need to get hired, whether you’re learning through a degree program, a professional credential, or on your own. Examine some job postings for positions you’d like to apply for, and concentrate your studies on the programming languages or visualization tools that are specified as requirements.
In addition to these hard capabilities, hiring managers look for soft skills such as strong communication skills—you might be expected to communicate your findings to those who don’t have as much technical expertise as you do—problem-solving ability, and domain knowledge in the field you want to work in.
Work on real-world initiatives.
Working with data in real-world contexts is the best approach to learn how to find value in it. Look for degree programs or classes that include real-world projects and data sets. You can also use a range of freely available public data sets to create your own projects.
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Investigate climate data from the National Centers for Environmental Information, dig deeper into the news with BuzzFeed data, or use NASA open data to find answers to emerging concerns on Earth and beyond. These are only a few instances of the information available. Choose a topic that interests you and get some data to experiment with.
Create an online portfolio of your work.
Save your best work for your portfolio as you experiment with data sets on the internet or complete hands-on projects in your classes. To hiring managers, a portfolio illustrates your abilities. A great portfolio can help you land your dream job. Consider include one of your group projects as well if you’ve worked on any during your education. This demonstrates your ability to work as part of a group.
Spend some time looking through other people’s portfolios to see what they’ve chosen to include if you’re not sure what to add in yours (or need some project ideas).
Practice giving a presentation of your findings.
It’s easy to get caught up in the technicalities of data research, but don’t forget about your communication skills. Presenting your findings to decision makers and other stakeholders in the firm is an important part of working as a data analyst. You can assist your organization in making data-driven decisions if you can convey a story with the facts. Practice presenting your findings as you finish projects for your portfolio. Consider the message you want to express as well as the graphics you’ll employ to support it. Slow down your speech and make eye contact. Practice in front of a mirror or with a group of friends. Try recording yourself giving a presentation so you can review it later and see where you can improve.
Apply for an internship or a position as an entry-level employee.
It’s time to polish your resume and start applying for entry-level data analyst positions after you’ve gained some experience dealing with data and presenting your findings. Don’t be scared to apply for jobs for which you aren’t quite qualified. Your qualifications, portfolio, and excitement for a position might frequently be more important than checking every bullet item on the qualifications list.
If you’re still in school, inquire about internship opportunities at your university’s career services office. An internship allows you to begin obtaining real-world experience for your resume while also allowing you to use what you’ve learned on the job.
Consider obtaining a certification or obtaining an advanced degree.
Consider how you’d like to improve as a data analyst and what other certifications you’ll need to get there as you go. Certifications such as the Certified Analytics Professional or the Cloudera Certified Associate Data Analyst might help you qualify for more advanced jobs with greater pay.
If you want to work as a data scientist, you’ll probably need to have a master’s degree in data science or a similar discipline. Advanced degrees aren’t always required, but they can help you get a better job.
For this type of profession, a university education is required. Most entry-level positions require a bachelor’s degree. The majority of data analysts will have a degree in mathematics, finance, statistics, economics, or computer science.
Strong mathematical and analytical abilities are required. Many of the highest-paid and most successful analysts have master’s or doctoral degrees, which provide them with additional experience and, in most cases, higher income.
Universities with the Best Big Data Analytics Programs
The Massachusetts Institute of Technology (MIT) is a public research university
The MIT Sloan School of Management, which was founded in 1914, is ranked first in the QS Business Masters Rankings. It is an excellent choice for high-tech professionals (such as engineers, mathematicians, and computer programmers) who want to take their skills to the next level and develop a career in data analytics. It focuses on systems, analytics, and human-centered data science.
Carnegie Mellon University is a public research university in Pittsburgh, Pennsylvania.
CMU’s Heinz College provides a master’s program in information technology that permits students to specialize in Business Intelligence and Data Analytics. It is appropriate for early-career IT professionals with around 3 years of experience, with leadership experience preferable. As an online course, students have access to all of the same courses and resources as on-campus students without having to leave their desks.
The University of Chicago is a public research university in Chicago,
The University of Chicago’s Graham School offers a Master of Science in Analytics program. This program is open to students who are serious about pursuing a career in analytics, whether they are in their early or mid-career. Students will learn how to evaluate large amounts of data and generate business insights.
Austin’s University of Texas
Students will obtain skills in statistical analysis, data mining, natural language processing, and machine learning as part of this curriculum, which will help them get insights into industries such as finance, marketing, and supply chain management. Students can also take advantage of fantastic networking opportunities with graduates from Walmart, Deloitte Consulting, and McKinsey who are industry leaders.
Northwestern University is a private university in Evanston
An 8-month practicum project, a 3-month summer internship with prominent organizations, and a 10-week capstone project are included in this MSIA curriculum. Throughout the curriculum, students will be exposed to enough industry to get practical experience.
Northwestern University offers customized sessions on SAS, SPSS, Cognos, Tableau, and other programming languages to assist students prepare.
Cornell University is located in Ithaca, New York
The M.P.S. the program consists of two semesters of statistical applications, computing, and consulting courses, as well as electives from the Department of Statistical Science and a large-scale data-analysis project at the end of the program (instead of a thesis or exam.) After graduation, students will be able to master statistical theory and develop skill in using statistical software.