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Almost every field uses data to solve problems and gain insight into trends and developments. But raw data is not enough to act on – organizations need data analysts capable of extracting meaning from actionable information. If you’re a solution-oriented person with a knack for spotting patterns, a career in data analysis may be for you.
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In this article, we will not only cover the above steps in more detail, but also consider what a career path to becoming a data analyst looks like along with the skills required. Each data analyst’s career path will look slightly different depending on the industry and specific role they choose. However, the following steps are highly recommended for anyone who wants to become a data analyst.
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As with any scientific career, data analysts need a strong foundation in mathematics to succeed. Before learning how to become a data analyst you may need to review and if necessary brush up on your math skills. Check out the list below for some important areas of study!
As the most important area of expertise for a data scientist, the field of statistics underpins all aspects of data analysis. Understanding statistics means being able to parse vast data sets for high-level insights. General statistical concepts to know include probability distributions, statistical features, and Bayesian statistics.
College-level algebra is frequently used in data analytics. In particular, any professional aiming to work with machine learning and/or AI needs linear algebra, as most algorithms use it. Given the rise in machine learning and the integration of AI in data science, these fundamentals will become more important over time.
Similar to algebra, calculus is necessary for working with AI and can be used to help teach machines pattern recognition. Functions such as gradient descent or derivatives are the most common in data science.
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Discrete mathematics is the backbone of computer systems used in data analytics, making understanding it a necessity. Distinctive mathematical learning requires abstract thinking and knowledge of the logic involved in mathematical thinking. Relevant areas of study include logic, evidence, and data structures.
Regardless of career path, employers 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 most common — a master’s degree in data science or a doctoral degree is expected for high-level data analysts — other paths are available if you’re willing to put in the work and dedication. Data science can often provide more flexibility and build foundational skills for analytics professionals looking for an alternative to traditional education.
College degrees, whether earned online or in person, offer comprehensive coverage of data science and analytics principles. A growing number of colleges and universities offer courses that include specializations in areas such as analytics, data engineering, or artificial intelligence. However, for some entry-level positions, even a bachelor’s degree in mathematics, statistics, economics, or computer science can serve as a good starting point.
Some data analysts may not have the time, resources, or inclination to pursue a four-year degree and may be better served by an alternative educational path. If you’re wondering how to become a data analyst without a degree, you might want to consider a data analytics major.
Data Analyst Vs. Data Scientist: Which Should You Pursue?
Data analytics is a faster and often less expensive option compared to formal degrees. Although fast-paced and rigorous, job-ready skills help you prepare and take three to six months to complete. Studying on one of these courses means gaining the most in-demand data skills in the market and benefiting from a supportive and collaborative learning environment. Additionally, most offer flexible attendance options such as online classes for those who want to continue working while learning how to become a data analyst.
I want to… Which best describes your goal? Start a new career Change career path proactively Start/grow current career Start/grow business Don’t say Other Start a new career Change my career path My career Become an entrepreneur Recent graduate, first job Prefer not to say other
Neither path is strictly “correct”—both have advantages for different lifestyles, skill levels, and learning preferences. For example, a quick job offer by someone with existing experience who is looking for a quick career pivot may be a better fit.
The often comprehensive nature of data analysis requires aspiring analysts to develop their soft skills as well. A data professional may be able to extract useful information from a large amount of data, but that doesn’t mean they can’t use it in a professional environment.
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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 they can communicate findings to colleagues with different levels of technical knowledge.
Data analysts must be critical thinkers and problem solvers who can see all angles of a problem and find an appropriate solution. With this comes a comprehensive understanding of the organization, its needs and what can be done to improve operations. An inquisitive nature is an ideal trait for data analyst job seekers; After all, at its core, the field focuses on asking questions and finding information.
Although it may seem counterintuitive, soft data analyst skills can be taught. Some online courses can provide everything from a basic overview to intensive study of business-related soft skills. Seeking mentorship or feedback from a professional colleague can also provide a path to improving your skills.
Even if you already have a job as a data analyst, your education may not be over. As previously mentioned, senior-level positions in this field may require a master’s degree or doctorate. Such positions may require three to six years of additional study. Of course, this added investment comes with a strong return – more experience and education can significantly increase your earning potential and growth (PDF, 1.9 MB) in your data analyst career path.
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Of course, growth rates and requirements may vary depending on the industry. An undergraduate or graduate degree may open up more opportunities for you and may not be necessary when pursuing an entry-level role.
Well, once you’ve completed an educational program that fits your goals, learning preferences, and lifestyle, you can turn to your job search. Data analysts are in demand in almost every industry you can think of – from healthcare and marketing to big tech and finance. As a data professional, your career opportunities are endless.
If you have completed projects as part of your education or elsewhere, you may want to consider compiling your best work into a portfolio. Update it frequently as you complete more work and include it in your applications—the practical work you can do will be more compelling to employers 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 larger conferences are a great way to learn about industry updates and make more contacts.
Building A Career In Data Science And Analytics: The Ultimate Guide
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’s worth combining all of this information into a personal website to send to prospective employers.
Taking your first steps into such a competitive industry can be intimidating, but even if you’re new to the workforce there are plenty of opportunities to become a skilled data analyst! Go into your job hunt with your desired salary, company culture, and industry goals in mind. Are you ready to earn more data analyst certifications to stand out? Can I relocate if needed? Being able to answer these questions decisively will appeal to employers.
When searching, tailor your materials to the companies you are applying to. Some roles may emphasize specific skills, so don’t be afraid to highlight them when applying. This is especially important given the breadth of expertise required in the field of data analytics.
Most importantly, don’t shy away from rejection! If you are interested in data analytics and have the education to do well, you will find an opportunity that suits your needs. Even if you don’t feel qualified for a position, apply anyway – any practice will help you land the next interview.
Data Analyst Job Description
While most data analysts share a common set of skills, an analyst’s specific responsibilities and daily schedule can look different depending on their industry and specific role.
Although used broadly to refer to a collection of data
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