How To Become a Data Analyst [2022 Career Guide]
Are you looking to become a data analyst? It’s an excellent career decision because the field is booming. The US Bureau of Labor Statistics has projected that data analyst roles will grow by 26% by 2026, putting it among the faster-growing industries in the country.
Based on the kinds of technical and soft skills you’ll need to acquire, the path to becoming a data analyst can be precisely charted. Keep reading to learn how you can become a data analyst and set yourself up for success in the field.
What Is a Data Analyst?
A data analyst manipulates and studies data, then communicates their findings to their organization to find opportunities and improvements. A data analyst holds the keys to data-driven business decisions, making it an extremely in-demand role.
The job of a data analyst begins with wrangling the troves of data that is available to the organization they work in. This data can come from various sources, including the company’s website, its social media platforms, its consumer applications, etc.
Much of this data tends to be unorganized and therefore not very usable in that form. Data analysts go about cleaning the data, which entails looking for errors, eliminating inaccurate data, and filling in missing data values based on heuristics.
It is only after the cleaning stage that data analysts move on to actually analyzing the data. This stage involves using various algorithms and statistical methods to unearth insights that can guide business decisions. In some cases, data analysts might go about the analysis to answer specific questions. Exploratory data analysis, on the other hand, is undertaken without a specific goal and follows whatever patterns might emerge from initial analyses.
Data analysts also need to be skilled at visualizing and presenting data. This is partly because visualizing data can sometimes lead to new insights that weren’t obvious from other forms of analysis. Visualizations are also important when it comes time to present analyses and defend new proposals, which analysts often have to do with various stakeholders in their organization.
What Does a Data Analyst Do?
A data analyst’s duties fall into three key areas.
Data mining: The analyst pulls information from primary and secondary sources.
Data cleaning: The analyst cleans the data of any errors or discrepancies and then looks for patterns, relationships, or trends in the data.
Data visualization: The analyst organizes the data so that it can inform business goals and strategies.
Become a Data Analyst in 8 Easy Steps
We’ve acquainted ourselves with data analysis as a profession and seen that it’s got great prospects. Now let’s find out how you can become a data analyst.
Learn Data Analytics Fundamentals
To become a data analyst, most people pursue a bachelor’s degree in statistics, math, computer science, or information technology (though it’s not required). However, an increasing number of people are entering data analytics after completing bootcamps and certifications. Many data analysts start with an online bootcamp or course to learn technical skills and languages such as SQL, Microsoft Excel, R, or Python.
You can also work your way up to your desired role by landing an entry-level role in IT, where you can learn about the organization and get comfortable with various interfaces. Working in this tangential role can begin to give you an understanding of how your company sources and processes data. You can then combine that practical understanding with skills that you pick up on your own, which can be through online courses and bootcamps.
Acquire Practical Experience by Working on Real-Time Projects
In order to land a job as a data analyst, you’ll need to build a portfolio of your own projects. Find freelance work, or team up with a friend, and apply your knowledge of data science, machine learning, and statistics. These projects will help you show proven success in the field and build your confidence when applying for jobs.
The most effective way to start off with personal projects is by picking a publicly available dataset and seeing what you can learn from it. There are several public data repositories like Data.gov and Datahub.io. You can find all kinds of datasets on these websites covering everything from healthcare to environment to wildlife.
Choose a dataset involving something you’re interested in and set about applying what you’ve learned as a data analyst. Apply different algorithms and statistical techniques to conduct exploratory data analysis and visualize your findings, which you can then compile into a report.
Not only does this serve to strengthen your practical understanding of the field of data analysis, but it’s also a nice addition to your resume if you’re looking for an entry-level role. The projects that you do, don’t have to be carried out in a corporate setting and recruiters appreciate when candidates show personal initiative.
Get Familiar With Data Analytics Tools
Data analysts use a variety of tools to guide their workflow and simplify their work. You should learn one or more of the following software to increase your chances of landing a data analyst job in 2022.
A powerful and popular tool for data visualizations.
A platform to source and process web traffic data.
A version control software where you can maintain repositories of your data science programs.
Amazon Web Services
The most popular cloud computing service online, which also includes a suite of analytics services.
A web-based tool used for data analysis and visualizations.
Find a Mentor
A mentor can provide you with guidance that’s based on your career goals. They can also field any questions you might have. Through them, you can learn about how people landed their current role, and the different paths you can take to get there. Talking with someone about their real-life experience can help you shape your ideal career trajectory.
You can go about looking for a mentor either in your own organization or outside of it. Within your organization, you can approach senior data analysts whose work you know about and appreciate. You can also identify data analysts outside your company on platforms like LinkedIn and approach them with a request to be mentored.
Make sure that you have a specific and detailed ask when you approach a potential mentor. Let them know where you stand in your career, where you want to be, and why you think they can help.
Also, come up with a proposal for how the mentorship can be structured. Do you want to have a phone call once a week? Talk over lunch once a month? Think about those details and have conversations with potential mentors to figure out a cadence that works for both of you.
Opt For a Course or Get Certified
Both online courses and certifications will help in your data analyst journey. Choose a course that suits your current level of proficiency in the field. If you’re just starting out, choose a beginner program that will teach you all of the basics. As you grow as a data analyst, you can choose certifications that hone in on particular tools or approaches to data analysis.
Don’t forget to pay attention to details outside of the course syllabus when you make your choice. Here are some additional things to keep in mind:
Always look up the credentials of an instructor to make sure that they have both experience as a data analyst and teaching it to others.
You want to choose a course structure that suits your learning style. Some courses provide learners with learning materials and they can go about completing those videos in whatever fashion they please. Other courses are more intensive and include working one-on-one with mentors and peers. Take some time to think about what kind of structure best suits your preferences.
Most popular courses are heavily reviewed online. Check out these reviews to find out from previous learners what the course was like. There are some things that you simply can’t figure out from a description and syllabus, and reviews can fill in those gaps.
Some certifications and courses are considered industry standards and will show a recruiter that you’re skilled in a particular area.
Create a Killer Data Analytics Portfolio
Once you’ve finished your courses in data analysis and worked on some real-world projects, it’s time to build a portfolio. A portfolio will centralize information about your expertise and detail the projects that you’ve completed. Any experience you have in extracting data, processing it, and solving business problems is relevant.
Building a network is a useful exercise, to both understand the industry from insiders, and to land jobs. Talking to data analysts can help you understand the status of the industry and the kind of tools and approaches that are most widely used by companies. You should also build a network among CTOs and tech recruiters so that you can contact them when you’re ready for a job.
Apply for Jobs
Your portfolio and resume get your foot in the door when you’re applying for jobs. Customize your resume to reflect your projects and certifications in the data analysis field. When interviewing, read up on technical questions and career-related questions: Why do you want to work in data analysis? What kind of problem-solving approaches are you interested in?
Data Analyst Skills
Mathematics for Data Analysis
Data analysis is a mathematical approach to producing business insights. So you need to have a strong foundation in a mathematical area to succeed at the job. Specifically, you need to have a solid understanding of statistics and probability to work as a data analyst. Usually, expert data analysts also study discrete mathematics, algebra, and calculus.
Programming allows you to use your math skills in a scalable manner in a computing environment. R and Python are the top two open-source programming languages used to provide predictive analytics. Both are dynamic programs with new features often added. These features assist with data mining, engineering, application, and more. A data analyst should be proficient in at least one of these languages.
Data Mining and Cleaning
Data analysis requires data. Data mining is the process of sourcing the right data needed for the problem you’re trying to solve. When sourcing this data, you’ll realize that it isn’t usable right away. The process of correcting errors in the data that you’ve sourced—including duplicates, corrupted data, and incomplete entries—is called data cleaning.
A data warehouse is a centralized repository of data extracted from various sources. It is key to the data analysis process because all of your data will be stored in the warehouse. It also allows the data to be processed easily and mined for business insights.
Microsoft Excel & SQL
Microsoft Excel is a widely used jumpoff point for any data analyst. It’s an absolute must in becoming an analyst and is the beginning of understanding data technologies. Furthermore, it’s a light and quick tool for analytics. SQL is Microsoft Excel’s big brother. It updates and communicates with a database, and also accesses and manipulates data.
A good data analyst must know how to communicate with their audience. Written correspondence, presentation skills, and overall communication skills are fundamental to solving problems with a data set. An analyst must know how to tell a story with their data by forming relational databases or graphics, creating presentations, or writing reports.
Creative and Analytical Thinking
While math and programming are essential for data analysts, creativity in analysis sets you apart in the profession. A data analyst needs to think of creative solutions to problems concerning where data is sourced from, what patterns might emerge from datasets, and which business problems can be tackled using data analysis. All successful data analysts have the ability to combine creative and analytical thinking.
Strong Communication Skills
Data analysts are team players. They work with the business intelligence, development, and executive teams to produce insights that can spur business growth. You need to be able to turn your data-based insights into easily consumable reports and intuitive visualizations. For all those reasons, data analysts need to have good communication skills, including both interpersonal communication and presentation skills.
How Much Can You Make as a Data Analyst?
Entry-level Data Analyst
The average salary for an entry-level data analyst in the USA is $43,250, which comes out to about $21 per hour.
Mid-level Data Analyst
Mid-level data analysts make $65,500 on average, which comes out to about $31 per hour.
Senior Data Analyst
Senior data analysts can command salaries of $91,400 per year on average. The base pay for the role is estimated to be $78,000.
Data Analyst Job Description
A typical data analyst job description might ask for the following attributes, education, and experience:
Collaborative Nature and Leadership Skills
To be successful in data analytics, focus is crucial. Each organization should have a deep understanding of their goals and how data analysis can guide that. Data analysts work with IT, leadership, and data scientists to determine the answers they are looking for in the data.
Programming Language Expertise
Every job description calls for proficiency within a few specific programming languages, analytics tools, and database languages such as Microsoft Excel, SQL, R, and Python.
A data analyst uses statistical tools to interpret the data they have gathered, uncover relevant patterns or trends, and perform predictive analysis. They must also recognize relationships within the dataset and what each relationship could mean.
A data analyst will fix any coding errors, track any patterns or trends, and delete any irrelevant information to assist their team.
FAQs About Data Analysts
Do Data Analysts Code?
Coding is not necessarily a required skill for a data analyst. While a surface level understanding is very helpful when processing programming languages, coding is not a typical task for a data analyst. However, there are certain organizations that include coding in data analysts’ responsibilities.
Can You Learn Data Analytics on Your Own?
Yes, you can learn data analytics on your own. There are several resources available online, both paid and free, that can help you become proficient at data analysis. It is helpful to start with the mathematical foundations of data analysis, which includes concepts in statistics and probability. You can then move on to programming languages like Python and tools such as Tableau.
Who Can Opt for a Career in Data Analytics?
You don’t need to have a particular background or even a specific degree to work in data science or have a data analytics career. A lot of companies have begun to hire candidates on the basis of the courses and projects they’ve completed. So anyone can break into the data analytics industry as long as they’re willing to work on their critical thinking, mathematical, and programming skills.
How Will the Data Analytics Market Change in the Next 5 Years?
The prospects for the data analytics profession are overwhelmingly positive over the next five years. According to the US Bureau of Labor Statistics, jobs in data analysis will grow by 26% by 2026.