How to Become a Data Analyst in 2021 (With or Without a Degree)

Almost every sector makes use of data to solve problems and gain insight into trends and developments. But raw data isn’t enough to work with — organizations need talented data analysts to extract meaning from the information they have to work with. If you’re a solutions-oriented individual with a knack for finding patterns, a career in data analysis may be for you.

The following steps may help you become a data analyst:

  1. Reviewing your fundamental math
  2. Getting an education
  3. Boosting your soft skills
  4. Considering further education
  5. Preparing for the job search
  6. Applying for jobs

In this article, we’ll not only cover the above steps in more detail, but we’ll also consider what a career path to becoming a data analyst might look like along with the skills required for it. Every data analyst’s career path will look slightly different depending on their chosen industry and specific role. However, the following steps are highly recommended for anyone who wishes to become a data analyst.

1. Reviewing Your Fundamental Math

As with any scientific career, data analysts require a strong grounding in mathematics to succeed. It may be necessary to review and, if necessary, improve your math skills before learning how to become a data analyst. Check out the list below for a few key areas of study!

Statistics

As arguably the most important area of expertise for a data scientist, the field of statistics underpins every aspect of data analysis. Having an understanding of statistics means being able to parse extensive data sets for high-level insights. Common statistical concepts to know include probability distributions, statistical features, and Bayesian statistics.

Algebra

College-level algebra is frequently used in data analytics. In particular, linear algebra is necessary for any professional who aims to work with machine learning and/or AI, as most algorithms make use of it. Given the upswing in machine learning and AI integration in data science, these fundamentals are likely to become increasingly important over time, too.

Calculus

Similar to algebra, calculus is necessary for working with AI, and can be used to help teach pattern recognition to machines. Functions like gradient descent or derivatives are among the most common found in data science.

Discrete mathematics

Discrete mathematics is the backbone of the computer systems used in data analytics, making understanding it a necessity. The study of discrete mathematics requires abstract thinking and knowledge of the reasoning that comes with mathematical thought. Relevant areas of study include logic, proofs, and data structures.

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2. Getting an Education

Regardless of career path, employers will expect a certain level of education from incoming data analysts. There are several ways for a learner to fulfill these data analyst education requirements. While a college degree is the most common — with a master’s in data science or doctoral degree expected for high-level data analysts — other paths are also available if you’re willing to put in the work and dedication. Data science bootcamps can often provide a quicker turnaround on building foundational skills and greater flexibility for analytics professionals seeking an alternative to a traditional education.

College degrees, whether achieved online or in-person, offer comprehensive coverage of data science and analytics principles. An increasing number of colleges and universities are offering courses that include specializations in fields such as analytics, data engineering, or artificial intelligence. However, for some entry-level positions, even a degree in mathematics, statistics, economics, or computer science can serve as a good starting point.

That said, some aspiring data analysts may not have the time, resources, or inclination to pursue a four-year degree and could be better served by an alternative educational route. If you’re wondering how to become a data analyst without a degree, you may want to consider a data analytics bootcamp.

A data analytics bootcamp is a faster and often less expensive option when compared with formal degrees. Though fast paced and rigorous, bootcamps help prepare you with job-ready skills and take as little as three to six months to complete. Learning from one of these courses means getting a grounding in the most in-demand data skills on the market and benefiting from a supportive and collaborative learning environment. In addition, most bootcamps offer flexible attendance options, such as online classes, for those who wish to keep working while they learn how to become a data analyst.

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Neither path is strictly the “correct” one — both have advantages for different lifestyles, skill levels, and learning preferences. For instance, someone with existing experience seeking a quick career pivot may be better suited for the rapid job readiness that a bootcamp provides.

In the end, the decision will hinge on what’s best for you and your situation.

3. Boosting Your Soft Skills

No one can get by on technical savvy alone.

The often holistic nature of data analysis requires aspiring analysts to hone their soft skills as well. A data professional may be able to extract useful information from a massive volume of data, but this means nothing if they are unable to make use of it in a professional environment.

No matter where you are in your career, you should be able to explain your findings to a variety of audiences. Data analysts work with information that impacts all levels of an organization, so being able to convey findings to colleagues who possess varying levels of technical knowledge is a must.

Data analysts should also be critical thinkers and problem solvers who can see all angles of a problem and find a fitting solution. With this comes a thorough understanding of the organization, its needs, and what can be done to improve operations. A curious nature is also an ideal trait for those seeking data analyst careers; after all, at its core, this field centers on asking questions and uncovering information.

Though it may seem counterintuitive, soft data analyst skills can be taught. Some online courses can provide everything from a basic overview to an intensive study of business-relevant soft skills. Seeking mentorship or feedback from a professional colleague can also give you a trajectory for improvement specific to your skills.

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4. Considering Further Education

Even if you already have a job as a data analyst, your education may not be over. As previously mentioned, senior-level positions in the field may require a master’s degree or even a doctorate. Such positions may also require three to six years of additional learning. Of course, this added investment comes with a strong return — having more experience and education can significantly boost your earning potential and growth (PDF, 1.9 MB) in your data analyst career path.

Of course, growth rate and requirements may vary depending on the industry. While a graduate or postgraduate degree may open more opportunities for you, it may not be necessary when pursuing an entry-level role.

5. Preparing for the Job Search

Wondering how to get an entry-level data analyst job?

Well, once you’ve completed an educational program that fits your goals, learning preferences, and lifestyle, you can kick your job search into gear. Data analysts are in demand in almost every industry you can think of — from healthcare and marketing to big tech and finance. Your opportunities for employment as a data professional are near-endless.

A chart that shows the distribution of data scientists broken down by industry.

But before you start sending out applications, it’s worth laying some groundwork.

If you’ve completed projects as part of your education or elsewhere, you may want to consider compiling your best work in a portfolio. Update it frequently as you complete more work and include it in your applications — interested employers will often be more compelled by the practical work you can do than any cover letter.

You should also think about networking in the data analytics field. Depending on your location, some cities may offer meetups for current professionals in the industry, and large conferences are a great way to learn about industry updates and establish more contacts.

You’ll also want to update your resume and online presence with the most recent possible information, including any education or data analyst certifications you’ve recently received. If you have the time, it can be worth combining all of this information into a personal website to send to prospective employers.

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6. Applying for Jobs

It can be intimidating to take your first steps into such a competitive industry, but there are plenty of opportunities for a skilled data analyst, even if you’re new to the workforce! Go into your job hunt with goals in mind for your desired salary, company culture, and industry. Are you willing to earn more data analyst certifications to stand out? Can you relocate if needed? Being able to answer these questions decisively can be appealing to employers.

When searching, tailor your materials to the companies you apply for. Certain roles may place emphasis on specific skills, so don’t be afraid to highlight those when applying. This is especially important considering the breadth of expertise required in the field of data analytics.

Most importantly, don’t be deterred by rejection! If you’re passionate about data analytics and have the education to perform well, you’ll likely find an opportunity that fits your needs. Even if you feel underqualified for a position, apply anyway — any bit of practice can help you land the next interview.

What Do Data Analysts Do?

While most data analysts share a common set of proficiencies, an analyst’s specific responsibilities and daily schedule may look different depending on their industry and specific role.

What does a day at work look like for a data analyst?

Though used broadly to indicate an assortment of data science roles, “data analyst” is a job title often used for entry-level positions in the field. An analyst may spend their day accessing and cleaning data, finding ways to answer business questions and solve problems, and communicating their findings to other teams.

As you advance in your career as an analyst and gather more data analyst skills, you may end up advancing into a data scientist role. As a data scientist, you would work with algorithms to develop data-based predictions. Other data analyst careers include business analysis, which centers on turning insights into strategic action, or software engineering, which combines programming knowledge and data science to facilitate analytics-recommended changes to company software.

What tools do data analysts use?

Every aspiring data analyst will need to have command over a variety of industry-specific tools. Below, we’ve put together a brief data analyst skills checklist that analysts in training should include in their academic agenda.

Programming Languages

Most data analytics positions require a grounding in one or more industry-relevant programming languages. (These are explained in more detail in the next section.)

Examples:

  • Python
  • R
  • SQL/NoSQL

Data Visualization Tools

Useful for analysts and laymen alike, data visualization tools can help you organize the data you work with and impart your findings to others in your organization.

Examples:

  • Tableau
  • Google Charts
  • Datawrapper

Checklist of Skills Required to Become a Data Analyst

Though companies may seek out professionals with slightly different skill sets, particularly across different positions, several data analyst skills reign supreme among employers.

SQL and NoSQL

Despite being created in 1970, SQL remains one of the most popular database query languages in use today. Though other languages have since come into prominence in the data science field, SQL is still widely used enough to be an important tool in any analyst’s arsenal.

Learning NoSQL is also important. NoSQL subverts the usual relational setup on a SQL database, and as such, there is no standard NoSQL framework. This flexibility tends to make NoSQL databases better at handling massive data troves than their relational counterparts — and thus, more attractive to businesses that need to manage large amounts of data.

MATLAB

MATLAB is a programming language and computing environment that is increasingly favored among businesses for its facilitation of quick data cleaning, organization, visualization, and compatibility with any machine learning model. The efficiency gains provided by MATLAB make it sought after among those working with Big Data. As such, MATLAB proficiency can be a good addition to your resume.

R

R is a programming language with structure and syntax designed to support analysis. It includes several common data organization commands by default and appeals to larger businesses for its ability to handle large or complex stores of data.

Python

Python is a more general-purpose programming language that is a must-learn for its versatility. Many of Python’s libraries are relevant for the use of artificial intelligence, and as such, makes the language increasingly important to a data analytics industry that relies on AI. Some ancillary programs for Python include Pandas and NumPy.

Data visualization

As previously mentioned, data visualization helps bridge the gap between the technical silo of data analytics and non-technical business stakeholders. Being data literate means being able to share complex ideas with business decision-makers and have them understand at a glance. There’s an element of storytelling with data visualization — you need to be able to take your insights and create an actionable narrative for others in your organization.

Data cleaning

Not all data is valuable — or even accurate. Before they can use gathered data, analysts must sift through and “clean” it to ensure that only accurate, reliable information is under consideration. Even with a sophisticated algorithm, the intervention of a data analyst is necessary to make proper use of a machine learning system. Many data analysts spend a significant portion of their time cleaning data.

Machine learning

Even if you don’t specialize in working with machine learning and AI, understanding it is important given its outsized presence in the data science industry. A familiarity with AI-relevant tools and concepts can give you an edge in a job search.

Career Paths in Data Analysis, Visualization, and Science

Not sure where you want to take your career in data analytics? Any of the following jobs are options for someone who has recently completed a data analytics college program or bootcamp.

  • Data Analyst: Data analysis is a common, entry-level role responsible for processing large amounts of information to generate insights, often through the use of assorted analytical tools.
  • Data Scientist: Data scientists hone machine learning systems and algorithms to model recent developments and predict new ones.
  • Business Analyst: A business analyst makes use of data to answer questions about business strategy, often visualizing data through tools such as Excel and Powerpoint.
  • Software Engineer: This role centers around the intersection of data science and programming. Software engineers design new software systems and maintain or improve existing ones.

How to get an entry-level data analyst job

Your first job in data analytics will depend heavily on your background. If you’re entering the field with experience in programming, a job as a software engineer might make the most sense. Alternatively, previous experience in business operations and planning may make you better suited to a role as a business analyst.

Whatever your path, it’s important to research what employers are looking for and tailor your learning in a direction that will allow you to thrive professionally. Though a wide range of skills is necessary for data analytics, specializing empowers you to better compete in your field of choice.

Once you’ve tailored your skills to your general career path, you can start looking for specific jobs. To find opportunities, comb through job boards like Indeed or ZipRecruiter and reach out to your network. You may also want to spend some time updating your resume and portfolio to ensure that potential employers receive an up-to-date snapshot of your skills and applicable work experience.

Whatever you do, don’t get discouraged! Job searches take time and effort; with enough of both, you can find and land your dream job.

Data analysis certifications

Certifications offer a method for data analysis professionals to build their resumes and expand their skill sets. A good certification is a vote of confidence for your skills in a certain industry or even with a certain vendor. Some common certifications include:

While bootcamps don’t offer certifications on their own, they can provide the skills necessary to pursue them later on.

How Much Do Data Analysts Make? Job Outlook and Salaries

Given both the prevailing need for data analysts to interpret the data processed by analytics systems and the increased popularity of Big Data, the average data analyst salary and job outlook is good (PDF, 3.9 MB).

How much does a data analyst make?

According to the Bureau of Labor Statistics, the median pay for a market research analyst was $63,790 per year in 2019, with the market expected to grow by 18% by 2029, much faster than average. The outlook is even better for an operations research analyst, with a median pay of $84,810 per year in 2019 and an expected 25% growth by 2029.

A graph that shows the projected job growth for market research analysts from 2019–2029

California, Texas, Virginia, New York, and Florida stand out as burgeoning states for careers in data analytics. In particular, New York claims the highest mean salary for operations research analysts, at $104,890 per year. In addition, recent trends toward remote work make data analytics a flexible career to break into regardless of your geographic location.

Should You Become a Data Analyst?

Starting as a data analyst means getting your foot in the door of one of the fastest-growing industries, with ample opportunities and multiple points of entry. If you have a head for numbers, a love of problem-solving, and a knack for critical thinking, data analytics could be an ideal career for you.

Columbia Engineering Data Analytics Boot Camp is an excellent alternative for those who are not seeking a data analytics or data science degree and are interested in an accelerated, real-world learning experience. Whether you’re just starting work or thinking about changing careers, our boot camp is a great starting point to build your skills and empower you to find your own path forward.

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