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Almost every industry uses data to solve problems and learn about trends and developments. But raw data is not enough to work with: organizations need talented data analysts to extract meaning from the information they have to work with. If you are a solution-oriented person with the ability to find patterns, a career in data analytics may be for you.
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In this article, we will not only cover the above steps in more detail, but also explore what the career path to becoming a data analyst might look like, along with the skills required for it. . Each data analyst’s career path will look slightly different depending on their chosen industry and their specific role. 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 require a strong foundation in mathematics to be successful. You may need to review and, if necessary, brush up on your math skills before learning to become a data analyst. Check out the list below for some top areas of study!
As arguably 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 analyze large sets of data to obtain high-level insights. Common statistical concepts to know include probability distributions, statistical characteristics, and Bayesian statistics.
College-level algebra is often used in data analysis. In particular, linear algebra is necessary for any professional aiming to work with machine learning and/or AI, as most algorithms use it. Given the rise of machine learning and the integration of artificial intelligence into data science, these fundamentals are also likely to become increasingly important over time.
Like algebra, calculus is needed to work with AI and can be used to help teach pattern recognition to machines. Functions such as gradient descent or derivatives are among the most common functions found in data science.
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Discrete mathematics is the backbone of computer systems used in data analytics, so understanding it is a must. The study of discrete mathematics requires abstract thinking and knowledge of the reasoning that comes with mathematical thinking. Relevant areas of study include logic, testing, and data structures.
Regardless of career path, employers will expect a certain level of education from incoming data analysts. There are several ways for a student to fulfill these data analyst training requirements. While a college degree is most common, with a master’s in data science or a PhD for high-level data analysts, there are other paths available if you’re willing to put in the work and dedication. Data science often offers a faster turnaround on building core skills and greater flexibility for analytics professionals looking for an alternative to traditional education.
College degrees, whether earned online or in person, offer thorough 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 degree in mathematics, statistics, economics, or computer science can serve as a good starting point.
That said, some aspiring 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 may want to consider data analytics.
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Data analytics is a faster and often less expensive option compared to formal degrees. While fast-paced and rigorous, they help prepare you with job-ready skills and take three to six months to complete. Learning from one of these courses means acquiring the most sought-after data skills in the market and benefiting from a collaborative and supportive learning environment. Additionally, most offer flexible support options, such as online classes, for those who wish to continue working while learning to become data analysts.
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Neither path is strictly “right” — both have advantages for different lifestyles, skill levels, and learning preferences. For example, someone with existing experience looking for a fast-paced career may be better suited for the fast-paced job preparation it offers.
The often holistic nature of data analysis requires aspiring analysts to hone their skills as well. A data professional may be able to extract useful information from a large volume of data, but that means nothing if he cannot use it in a professional setting.
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No matter where you are in your career, you need to be able to explain your findings to a variety of audiences. Data analysts work with information that touches all levels of an organization, so it’s essential to be able to communicate results to colleagues with different levels of technical knowledge.
Data analysts must also be critical thinkers and problem solvers who can see all angles of a problem and find an appropriate solution. With this comes a thorough understanding of the organization, its needs and what can be done to improve operations. An inquisitive nature is also an ideal trait for those seeking data analyst careers; after all, at its core, this field is about questions and discovering information.
Although it may seem counterintuitive, soft data analyst skills can be learned. Some online courses can offer anything from a basic overview to an intensive study of business-related social skills. Seeking mentorship or feedback from a professional colleague can also give you a specific improvement trajectory for your skills.
Even if you already have a job as a data analyst, your education may not be over. As mentioned above, senior positions in this field may require a master’s degree or even a doctorate. These positions may also require three to six years of additional training. Of course, this added investment means a big return: having more experience and education can significantly increase your earning potential and growth (PDF, 1.9 MB) in your data analyst career.
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Of course, growth rates and requirements may vary depending on the industry. While a graduate or postgraduate degree can open up more opportunities for you, it may not be necessary when you’re in an entry-level role.
Well, once you’ve completed an educational program that fits your goals, learning preferences, and lifestyle, you can begin your job search. Data analysts are in demand in almost every industry imaginable, from healthcare and marketing to technology and big finance. Your job opportunities as a data professional are almost endless.
Whether you’ve completed projects as part of your education or elsewhere, you might consider compiling your best work into a portfolio. Update it often as you complete more work and include it in your applications – interested employers are often more compelled by the practical work you can do than any cover letter.
You also need to think about networking in the area of data analysis. Depending on your location, some cities may offer meetups for current industry professionals, and large conferences are a great way to learn about industry updates and make more contacts.
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You’ll also want to update your resume and online presence with the most recent information possible, including any data analyst training or certifications you’ve recently received. If you have the time, it may be worth combining all this information into a personal website to send to potential employers.
It can be intimidating to take your first steps into such a competitive industry, but there are plenty of opportunities for a skilled data analyst, even if you’re new to the workforce. Go into your job search with your desired salary goals, company culture, and industry in mind. Are you ready to get more data analyst certifications to stand out? Can you move if necessary? Being able to answer these questions decisively can be attractive 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 those when you apply. This is especially important given the breadth of experience required in the field of data analysis.
Most importantly, don’t let rejection discourage you! If you’re passionate about data analytics and have the education to do it well, chances are you’ll find an opportunity that fits your needs. Even if you feel underqualified for a position, apply anyway – every bit of practice can help you ace your next interview.
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While most data analysts share a common skill set, an analyst’s specific responsibilities and daily schedule can look different depending on their industry and specific role.
Although it is widely used to indicate a variety of data
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