If you are interested in pursuing a career in data analytics but don’t have a college degree in this field, there are still job options available. This article will explore how you can begin a career as a Data Analyst, even without a four-year degree in this field.
Can You Really Become a Data Analyst Without a Degree?
In the past, most entry-level job openings in data analytics required applicants to have at least a bachelor’s degree. However, this is no longer the case. Currently, the job market for data specialists is accessible to newcomers, even those who may not have prior industry experience. This is because of the rapid growth in the data market, a shortage in data talent, and the fact that those working in data analytics must have a variety of transferable skills that can be gained from other industries or life experiences, such as an eye for detail, curiosity, teamwork, and communication.
While some positions still expect candidates to have a degree in data analytics or a related field, there are alternative ways to gain the necessary educational foundation to prepare you to work with big data. The following are some steps to pursue if you are looking for a job in data analytics but don’t have a degree in this field.
Step #1: Pursue an Educational Foundation
Even if you don’t elect to get a degree in data analytics at a college or university, developing an overview of what the field entails is a great first step toward learning if data analytics is right for you. This may involve enrolling in self-study courses or bootcamps in data analytics or a related field. Unlike university study, which can cost significantly more and also require more of a time commitment, electing instead to take industry-relevant coursework in statistics, math, or computer programming can help you decide if data analytics is a good career path for you while at the same time imparting a skill-set that will prepare you to enter the workforce.
Step #2: Develop Technical Skills
No matter which industry you’d like to work in as a Data Analyst, technical training with tools, applications, and software is essential, as is a knowledge of various programming languages. The following are some of the technical skills and tools employers seek out in prospective employees:
- Microsoft Excelis a must-have skill for anyone working with large datasets. This spreadsheet application helps Data Analysts to organize data, perform advanced calculations, graph findings, and share results with others. Data Analysts use Excel because it has a straightforward, simple-to-use interface that is easy to learn and provides a convenient way to sort through large sets of data.
- Tableauis a popular program for data visualization. It helps users transform the insights gathered from raw data into stunning and accessible visualizations that can be shared with others. Tableau includes features like real-time analysis and data blending.
- SQL, or structured query language, is a popular programming language for creating and managing the data housed in relational database management systems.
- Pythonis a general-purpose programming language that Data Analysts often use. It offers a robust library that enables users to create data models from scratch. Python is also helpful for creating data visualizations, often used in data analytics.
- R is a data mining tool that Data Analysts can use to execute various tasks, like manipulating or visualizing data. R provides animated graphs, advanced optimization features, and a robust user interface, all of which help execute complex statistical calculations.
Step #3: Gain Real-World Experience
If you are new to data analytics, it’s essential to accumulate real-world experience. This can take many forms. Some elect to sign up for internships so that they can gain training in data analytics. Others opt to create small projects on their own, which can display skills and be included in a professional portfolio. Some examples of small projects include collecting and cleaning a messy dataset and performing exploratory data analysis on a dataset.
Step #4: Create a Portfolio
A portfolio displaying your best work is a great way to wow employers. A strong portfolio showcases your data analytics skills and provides a snapshot of your personality and design abilities. Online portfolios are growing increasingly popular, as they are a convenient, accessible platform for highlighting your expertise and can be easily shared with others.
If you are putting together a portfolio in data analytics, here are a few tips:
- Tell a story. A strong narrative can transform data from numbers into actionable insights. Weaving narratives will play an integral role once you work as a Data Analyst, so why not start with your portfolio? Adding a narrative thread to your portfolio helps the audience form connections between various projects you’ve worked on and understand their significance. It also demonstrates that you can articulate data findings in a clear, succinct manner.
- Add visuals. Data visualization is another crucial aspect of working as a Data Analyst. This is why adding visual components to your portfolio is a smart way to show employers that you know how to engage your audience and incorporate images, charts, or graphs when necessary to share findings.
- Feature an “About me” page. In addition to displaying quality work that showcases the data skills employers value, an “About Me” page can add a personal flair to a portfolio. It provides a way to briefly introduce yourself and offer insights into why you are interested in working in data analytics.
- Focus the portfolio on pieces that showcase industry-relevant skills. A strong portfolio showcases the services you can offer, the skills you bring to the table, and the training you have that makes you a strong candidate. It’s a good idea to further focus your portfolio’s content on timely and universal business skills, such as creating KPI dashboards or optimizing sales funnels.
Step #5: Consider Data Analytics Certification or Study in a Related Field
Enrolling in certificate study is an excellent alternative to a traditional four-year or longer degree. Most certificate programs take less than a year to complete. They are typically offered in one course or a set of related courses on data analytic topics like Python or data visualization. In addition, because most certificates are currently available in the online format, they provide a flexible learning environment for those who must balance their education with other commitments, such as family or work. Often these certificates are formatted around recorded video content and uploaded course materials, which allow students to study on their own time and then receive feedback from instructors as it is completed.
Noble Desktop’s Data Analytics Certificate is one such option. This rigorous course of study provides students with many industry-relevant skills, such as data visualization, statistical analysis, and database training.
Another option for aspiring Data Analysts to consider is to study a field related to data analytics at a college or university. Several other fields teach the skills, tools, and programs that are often used by Data Analysts, such as a degree in math, computer science, business, or statistics.
Step #6: Apply for Entry-Level Positions
Whether you studied data analytics in college, earned a certificate, or are self-taught, you’ll need to start with an entry-level position in this field at some point. Because jobs in this industry remain in high demand, the good news is that if you’ve followed the steps mentioned above, you are in a good position to secure an entry-level position in data analytics.
Showing prospective employers that you are confident in your abilities and ready to put them to use is an important component in applying for positions in data analytics, especially if you don’t have a traditional degree in this field. This is why having a solid portfolio that showcases your skills, along with a strong resume, is a must.
Learn More About Data Analytics by Enrolling in Hands-On Classes
Data analytics is currently one of the most in-demand professions in the U.S. If you’re looking to learn more about how to analyze and visualize data, Noble Desktop’s Python for Data Science Bootcamp is a great place to start. This intensive, 30-hour course covers core Python skills that are useful for the data sciences, such as an overview of the various data types and how to create data visualizations. Noble also offers an 18-hour SQL Bootcamp in which students learn how to filter data, write SQL queries, and gather insights from data.
For those looking to learn specifically about data analytics, courses such as the Data Analytics Certificate or Data Analytics Technologies Bootcamp are available in-person in NYC, as well as in the live online format. These rigorous learning options cover core data analysis tools like SQL, Excel, and Tableau, among others.
If you’re looking for learning options close to home, you can also search for live online and in-person data analytic courses with the help of Noble’s Classes Near Me tool. Over 340 courses are currently available by Noble and other top educational providers in topics like data visualization and data analytics, among others.