How to Become a Data Analyst and get a Job (with or Without a Degree)

A job as a data analyst may suit your interests in both mathematics and puzzle solving.

Data analysts assist businesses with decision-making by collecting, cleaning, and analyzing data. Given the high demand for professionals with these skills, here is one way to enter the field:

  • Obtain a basic education.
  • Sharpen your technical abilities.
  • Use actual facts to inform your projects.
  • Create a collection of your recent projects.
  • Work on your presentation skills.
  • Find a job as a data analyst with less experience.
  • Think about earning a credential or going back to school.

All right, let’s dissect those seven steps a bit.

How do I become a data analyst? A step-by-step guide

There is more than one way to get your foot in the door of the data analytics industry, which offers work opportunities in a wide variety of sectors. Here are some things you can do to become a data analyst, whether you’re a newbie to the workforce or are changing careers entirely.

Acquire a basic training.

If you’re just starting out in data analysis, it’s a good idea to brush up on the basics. In addition to providing you with marketable skills, a general introduction to data analytics will help you determine if this is the right career path for you.

A bachelor’s degree was once the standard for entry-level data analyst roles. The need for degrees is starting to decline, yet it is still necessary for many jobs. A bachelor’s degree in mathematics, computer science, or a closely related discipline can give you a leg up in the job market, but there are other ways to get the skills you need, such as through professional certificate programs, boot camps, or even just taking classes on your own.

Acquire more technical knowledge.

Data analysts usually need a certain set of technical abilities to land a position. You will most likely need these core competencies to land a job, regardless of whether you’re pursuing a degree, a professional certificate, or self-study.

Python and R for statistical analysis
System for Data Structures
Visualizing data
Eliminating and preparing data
Examine the job postings for positions that interest you, and educate yourself on the programming languages and visualization tools that are specifically requested.

Employers also value soft skills, such as the ability to communicate well (you might have to explain your results to people who don’t have as much technical knowledge), problem-solving prowess, and industry expertise.

Use actual data in your initiatives


Finding value in data is best learned by doing it in real-world contexts. Try to find a degree programme or a class that requires you to work with real data sets on a hands-on project. Additionally, there are numerous open-source data sets available for free that you can utilise while developing your own applications.

Explore climate data from the EPA, learn more about current events with BuzzFeed’s data, or use NASA’s open data to find answers to problems both on Earth and in the universe. The data available includes many examples like these. Determine what you’re passionate about and gather relevant data to put your skills to the test.

Advice: There is a wealth of data analysis resources available on Coursera if you need more ideas. You may finish each of the guided, hands-on activities in less than two hours with the help of Guided Projects.

Put together a set of samples of your work to showcase.

Keep an eye out for opportunities to build your portfolio as you experiment with online data sets or finish class projects that require hands-on effort. Portfolios are a great way to show potential employers what you can do. An impressive portfolio can greatly enhance one’s chances of landing the job.

When putting together your portfolio, make sure to include works that showcase your skills in the following areas:

Collect information via scraping various sources

Purify and standardise unprocessed data

Use graphs, charts, maps, and anything else you can think of to show your results.

Get useful information out of data

Think about including one from any group projects you may have worked on while studying. Your ability to collaborate with others is demonstrated here.

Looking at other people’s portfolios can give you a good idea of what to add in your own if you’re stuck for ideas or need a little inspiration for your own projects.
The best way to share your projects and code is to create an account on the popular code hosting website GitHub. It’s a great place to meet other data analysts, showcase your work, and maybe even attract recruiters.

Get some experience presenting your results.

Data analysis is a technical field, so it’s easy to lose sight of the importance of good communication skills. Presenting your results to decision makers and other company stakeholders is a big part of being a data analyst. Assisting your organisation in making data-driven decisions becomes easier when you can weave a narrative around the data.
Could you please explain data-driven decision-making (DDDM)?

Data-driven decision-making, or DDDM for short, is when a company’s top executives make long-term choices not based on gut feelings, but on hard evidence, statistics, and measurements.

Although it may seem apparent, not all organisations truly make use of data to their full potential. When it comes to customer acquisition, retention, and profitability, data-driven organisations really shine, says McKinsey Global Institute, a global management consulting firm [1].
Presenting your results should become second nature as you finish up assignments for your portfolio. Before you choose any images to accompany your message, give some thought to the message you wish to express. Speak more slowly and maintain eye contact. Get some practice in with a mirror or your fellow students. To find out where you may make improvements, try recording yourself as you present.

Secure an entry-level position as a data analyst

Applying for entry-level data analyst positions is the next logical step after getting some hands-on experience with data and presenting your findings. If you aren’t sure you’re qualified for a job, don’t be scared to apply nonetheless. Checking off every item on the qualifications list isn’t often as important as showcasing your abilities, portfolio, and passion for the work.

For information on internships while you’re a student, contact the career services office at your school. By participating in an internship, you may begin to build your résumé with relevant work experience while also putting your academic knowledge into practice.

A graduate degree or certification may be an option to consider

As a data analyst, you should think about your professional goals and the skills that will help you grow in your chosen field. Earning credentials such as Cloudera Certified Associate Data Analyst or Certified Analytics Professional could open doors to better jobs with greater salaries.
Advice: If you want to learn data science but also want to keep working (and making money), think about getting your degree online from a reputable school.

Students interested in data science can receive a Master of Applied Data Science (MADS) online from the University of Michigan’s School of Information by working on real-world projects. In this course, you will discover the power of data analysis in achieving your loftiest objectives.
A master’s degree in data science or a closely related discipline may be required for advancement into the profession of data scientist. Although it is not always necessary, getting an advanced degree can increase your career prospects.
Without a degree, what does it take to be a data analyst?

Data analysts can often get by without a degree. Employers are looking for data analysts, therefore it’s important to demonstrate that you have the necessary expertise. Make sure your portfolio showcases your finest work if you don’t have a degree.
Use Coursera as a springboard

Coursera offers a Google Data Analytics Professional Certificate if you want to become a data analyst but don’t want to go to college.

There is no need for a degree or expertise to learn how to clean and organize data using SQL and R, visualize with Tableau, and finish a case study for your portfolio. In the end, you’ll be able to apply for entry-level positions directly with Google and over 130 other US companies.
What it takes to start a career as a data analyst from scratch

To be considered for a data analyst position, many companies need candidates with prior expertise in dealing with data. You may begin building your resume right now, so it’s not all bad news. Information is ubiquitous.

Get some hands-on experience with data if you’re planning to make a career change to data analysis. Using real data sets, students work on projects as part of many degree programs, certificate courses, and online classes. Finding free data sets online (or creating your own) is another great way to practice data collection, cleaning, analysis, and visualization.
What is the typical number of years required to earn the certification as a data analyst?‎

Becoming a data analyst might need a significant amount of time, ranging from months to years. Your present skill level, the educational route you pursue, and the amount of time you devote each week to learning data analytics will all determine how long it takes.
I don’t have a degree, how can I work as a data analyst?‎

Although your prospects will be much improved with a degree in a related profession, the answer is yes. It is feasible to get employed with the correct combination of abilities and experience, even though a bachelor’s degree is listed as a requirement for many jobs. Take your time building your portfolio to prove your skills if you don’t have a degree (or one in a closely comparable area).
Is there a need for data analysts?‎

An increasing number of employers are looking for qualified candidates with experience in data analysis, according to the World Economic Forum’s Future of Jobs 2020 report [2]. The need to hire data analysts is high in several sectors, including the IT, energy, healthcare, financial services, and technology sectors.

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