Data analysts translate raw data into useful insights, and they are also responsible for gathering the data, organizing and analyzing it, and then presenting their findings.

Data analysts are in high demand, and there aren’t enough data analysts to fill all the positions. People with the right skills can fill these positions. A degree is helpful when applying for a job as a data analyst, but not all employers require one. Experience is more important.

Some of the experience includes using tools, such as Tableau and Microsoft Excel, and understanding languages like R, SQL, and Python.

Data analysts do a great deal of work with spreadsheets. (During interviews, it is common to receive a question or two designed to estimate your skills on working with Microsoft Excel.)

Data analysts are also mathematicians and statisticians. If you have a passion for mathematics and statistics, the work done by data analysts might be your niche. Multivariate calculus and linear algebra are often used in data analytics. Developing the mathematical and statistical skills needed for this position will help in solving complex business problems. (Modern computers and laptops can behave as calculators supporting mathematical and programming calculations.)

The amount of money a data analyst earns varies, and depends on a number of factors, such as the organization’s location, the amount of experience you have, and the industry (hospital, retail, service). In the United States, a beginning data analyst can expect to earn between $35,000 and $65,000 per year.

The Challenges

Adie Fridman is a data analyst at the Children’s Hospital of Philadelphia, and when asked what task she found difficult as a data analyst, she answered:

“Probably the initial state of the data. The first big challenge in working on a new project is locating the data. We have massive databases and, at least in the space that I work, not very many people have worked with this data previously, and I think that’s the case for a lot of people in new projects. So, you have to first track down how to be able to pull the data, which is usually a challenge. And then cleaning the data is, typically, an even bigger challenge. It’s never clean and usually it takes a little while to find a pattern and figure out the best way to clean it — to wrangle it. It’s pretty manual at first. You have to look at a bunch of patient charts and validate that what you’re looking at is what you think you’re looking at, and then figure out how to organize it in a way that you can actually work with.”

Transferable Skills

There are data analytics courses (ranging from free to expensive) that can be combined with what are called “transferable skills” — though some might also be referred to as personality traits. Some of the more useful transferable skills include:

• Good problem-solving skills
• Curiosity/an inquisitive nature
• The ability to perform research
• Good communication skills (especially in terms of explaining things)
• Teamwork and collaboration
• Attention to detail

Some of these traits may simply be a part of your basic personality, while other transferable skills may have been developed in school, at work, or simply by interacting with other people.

When your actual data analytics experience is minimal, these transferable skills can help to demonstrate your suitability for the job. Employers are currently placing an emphasis on soft skills, and these are worth listing on a resume or in a portfolio.

While data analysts spend much of their time alone on the computer performing data analytics, they must have the communication skills needed to present their findings to upper management.

Desired Data Analyst Technical Skills

Data analysts need to work with a variety of tools such as SharePoint, Microsoft Excel, Microsoft Access, and SQL databases. Many data analytics positions require experience with Python and SQL.

Experience with data wrangling and data organization are also valuable technical skills.

Data Analyst Training

If you already have some experience working with data analytics, you’ll have an advantage in getting job interviews. The more experience, the greater the chances of getting an interview — and the job.

If you have a gift for mathematics, but no data analytics experience, it’s time to get some, starting with some courses. As an entry-level analyst with no previous experience, there’s not much of a track record to impress potential employers with. Python is a very commonly used language, and I would recommend learning it first, followed by SQL. A few are listed below:

• Business Analytics in Action: A DATAVERSITY Training Center course that teaches practical skills in data analytics. It evaluates what analytics, machine learning, enterprise systems, data engineering, and quality data mean today.
• Data Analytics Short Course: CareerFoundry offers a free data analytics short course, which is ideal for a quick introduction to data analytics.
• Learn to Code for Data Analysis: OpenLearn is sponsored by the UK’s Open University, and is filled with content, ranging from Learn to Code for Data Analysis to Introducing Computing and IT to the Psychology of Cybercrime. .
• Online Data Science Courses: Harvard University has a number of free data analytics courses in their (and MIT’s) edX program. Their topics range from linear regression to data wrangling to machine learning. These courses tend to require some prior knowledge, but cover several specialized topics and provide more depth than most free courses.

Gaining Some Real Experience

If you have no hands-on project experience, it is worth taking the time to do some projects on your own. A data analyst should be able to work in different industries and domains, gathering data and providing useful information. CareerFoundry has nine suggestions for projects that can be done on your own, and provides links to a number of free tools.

Most of the suggested projects are fairly simple, but will definitely provide some experience. I recommend projects that sound interesting to you. These completed projects can be listed on your resume, and described in greater detail in the portfolio.

Portfolios and Resumes

Portfolios are becoming popular with employers. (I would recommend having a PDF resume that contains a link to your portfolio.) The resume provides a condensed version of your experience and education, and the portfolio shows an expanded version.

Currently, a portfolio is a website that communicates a person’s abilities and experience by showing off examples of their work. There are free websites available online.

As you create your data analytics resume and portfolio, remember the transferable skills. They should be listed on the resume. In the portfolio they should be expanded upon, preferably with examples. Spend some time thinking about, and identifying, your core soft and hard skills, and how they can be useful in data analytics.

For example, you might have teaching experience, and have become comfortable speaking to an audience and explaining things — very useful when making a presentation to upper management. Perhaps you worked with a small construction crew and understand teamwork and collaboration. Or maybe you worked in a coffee shop, proving you can work under pressure.

Working Onsite vs. Working Remotely

This is a job that can be worked remotely. Many employers prefer the more traditional model of having their employees work onsite, but not all do. There is a growing trend of allowing — or even preferring — remote workers. After all, if everyone is working remotely, the overhead costs of space in an office building disappear.

Working remotely requires a degree of self-discipline. Typically, it requires sitting in one location for an extended time and focusing on the work.

Some people prefer to work at home, with music, the news, or science shows providing background noise. Others prefer to work in coffee shops or libraries to avoid the distractions of working from home. Libraries are typically the quietest locations. 

You could even set yourself up as a remote independent contractor (though I strongly suggest getting at least a year’s worth of experience before taking this route). 

As a remote worker, employers may want proof you can manage your time effectively without supervision. This falls under the category of a transferable skill, and examples from other jobs can be used to show you have the self-discipline to work remotely.

Have you ever worked independently on a freelance project? Are there any projects you worked on that required meeting a deadline? Academic projects do count.

The ability to communicate remotely is also a concern for remote employers. Are there times when you have had to collaborate with others in different locations to complete a project? Emails are normally a good way to communicate. Phone and Zoom are also options.

Data Analysts Aren’t Working with Machine Learning

There is a high demand for data analysts, and the job itself requires a variety of skills — skills that may come from other life and work experiences. Rebecca Pope, in charge of Data Science and engineering for KPMG, stated:

“You don’t need to be an excellent statistician or a high-class mathematician to work in Data Science or analytics, nor do you need a lot of prior programming knowledge. There’s not much machine learning modeling or machine learning deployment in the role of the data analyst. If you want to be a data analyst, it will help if you understand how to use RapidMiner predictive analytics software and Postgresql, an open source relational database.”

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