How to Become a Data Analyst

Advancing Your Education

Earn a bachelor’s degree. Most entry-level data analyst jobs require at least a bachelor’s degree. To become a data analyst, you’ll want to earn a degree in a subject such as mathematics, statistics, economics, marketing, finance, or computer science.

Decide if you want to earn a master's or doctoral degree. Higher level data analyst jobs may require a master’s or doctoral degree, and they usually guarantee higher pay. If this is something you think you may be interested in, think about what kind of additional degree might be best for you and your career goals. Examples of higher degrees would be earning your master's in Data Science or Business Analytics.

Sign up for classes that target a specific subject. If you think you need some help with calculus or want to learn about coding, sign up for a class that will teach you skills needed to become a data analyst. These classes could be in person or online. When looking for classes, see if any local colleges or universities are offering a seminar or course in your desired subject. There also might be workshops that you can attend in your area.

Learning Necessary Skills

Master college-level algebra. Numbers are what a data analyst works with every day, so you want to make sure you’re comfortable with math. Having a firm understanding of college algebra is important; you should know how to do things such as interpret and graph different functions as well as work through real life word problems. Knowing multivariable calculus and linear algebra will help as well.

Understand statistics. To become a data analyst, you’ll need to be able to interpret data, which is where statistics comes in. Start with a foundation of high school- or college-level statistics, and then move on to more challenging information that might be required for the job. Mean, median, and mode, as well as standard deviation, are examples of the kinds of statistics concepts you would learn in high school or college. Having a strong grasp of both descriptive and inferential statistics will be helpful as well.

Work on your coding and programming abilities to be a more appealing candidate. While you don’t need to be an expert at coding or programming to start off as a data analyst, you should be comfortable doing it on a small level. Start by learning how to use programs such as Python, R, and Java first, and then work your way up to others. SQL programming is another that is common among data analysts. You can take courses online to learn coding and programming.

Develop strong communication and presentation skills. Once you’ve analyzed your data, you’ll need to be able to talk about it with others. Work on being able to explain complicated information in a way that makes non-data analysts understand your findings, and practice using programs that illustrate the data in a visually-helpful way. You should be able to communicate data visually as well as verbally. Understand how to use tools such as ggplot and matplotlib to illustrate your findings.

Familiarize yourself with Microsoft Excel. You’ll be organizing data and calculating numbers as a data analyst, so you need to be comfortable using Excel. There are many video tutorials online, as well as free sites, that will help teach you all you need to know about using Excel to its full potential.

Learn about machine learning. Teaching a computer to come up with predictions or decisions on its own after it has studied data, or machine learning, is important when dealing with data analysis. Look online to find courses you can take that will teach you all you need to know about machine learning, and some of them are even free. To understand machine learning, you'll need to have a foundation in programming and statistics. There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. An example of supervised learning is your email filtering your inbox and putting spam in its own folder. Supervised learning would be when Netflix suggests television shows or movies that you might like, and an example of reinforcement learning is a self-driving car and its ability to see and then adapt to its surroundings.

Gaining Work Experience

Look for industries that need data analysts. Focus your job search on industries that tend to need data analysts more than others. Marketing firms, tech companies, and financial institutions all tend to hire data analysts to help them interpret data and explain it in understandable terms. Check the websites of companies you're interested in to see if they're hiring, or do a general search online. If you already know someone who works in one of these fields, ask them if they know of anyone whose hiring.

Apply for an internship as a data analyst. Internships are a great way to get your foot in the door at great companies. Many data analyst internships will require you to be working towards your degree before applying. Depending on the industry, you’ll need to be familiar with Python, R, or SQL programming — knowing all three is even better. Many of these internships are unpaid or only for the summer, so check before applying so that you know all of the details.

Join a trade organization. Trade organizations are a great way to take advantage of resources such as workshops, networking opportunities, or online help centers. There are several organizations related to data analysis, such as TechAmerica or the Association for Computing Machinery. Do some online research to see if you’re interested in joining one. To join a trade organization, go online to their website to find the membership information. You may be able to sign up for a free membership that gives you access to a limited number of resources. There are usually different tiers of memberships that give you different perks depending on how much you pay.

Aim for entry-level jobs. Entry-level jobs will allow you to gain valuable knowledge and experience that you’ll need for higher level data analyst jobs. Entry-level jobs still pay very well and companies are always looking for people to fill positions such as Statistical Data Analyst or Business Analyst. Entry level jobs will most likely require a bachelor's degree, but not a master's or doctoral degree.

Interviewing for the Job

Write a professional resume and cover letter. Your resume and cover letter are the first glimpses a potential employer is going to see of you. Spend time articulating your skill set and work experience to show that you’re right for the job. Once you’re done, be sure to proofread your resume and cover letter so that there aren’t any mistakes.

Research the company before the interview. Doing research about the company beforehand allows you to go into the interview prepared to have a real discussion about the job. Go to the company’s website and read about the projects they’ve been working on or the programs that they use. If the company has social media, look at their account to read any updates they’ve posted.

Practice answering potential questions. Look online to find interview questions you could be asked. Practice your responses with a friend, or record yourself answering them to see if you can improve. Potential questions could be “How do you define big data?” or “Talk about problems data analysts sometimes run into during analysis."

Prepare to show your technical skills. Depending on the job, you may be asked to demonstrate your technical abilities. Find out what types of programs the company uses before the interview and be prepared to show that you’re able to use these programs in depth. Technical skills may include knowing how to code, program, or analyze data using different resources.

Think of questions you have for the interviewer. At the end of the interview, ask the interviewer questions such as “What types of projects will I typically be assigned to?" or "What program do you prefer be used for data visualization?“ Asking questions shows that you’re interested in the job and can make you a more memorable candidate.