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Data analysts examine information using data analysis tools and help their teams develop insights and business strategies. You’ll need skills in math, statistics, communications, and working with tools designed to do data analytics and data visualization. Explore this high-demand career.
Learn the technical skills for data analyst career paths.
Develop your competencies in high-demand analysis tools.
Build communication, teamwork, and problem-solving skills.
How do I become a data analyst? A step-by-step guide
You can find data analytics jobs in all sorts of industries, and there’s more than one path toward securing your first job in this high-demand field. Whether you’re just getting started in the professional world or pivoting to a new career, here are some steps toward becoming a data analyst.
Learn more: What Does a Data Analyst Do? A Career Guide
- Make a learning plan.
If you’re new to the world of data analysis, you’ll want to start by developing some foundational knowledge in the field. Getting a broad overview of data analytics can help you decide whether this career is a good fit while equipping you with job-ready skills.
It used to be that most entry-level data analyst positions required a bachelor’s degree. While many positions still do require a degree, that’s beginning to change. While you can develop foundational knowledge and enhance your resume with a degree in math, computer science, or another related field, you can also learn what you need through alternative programs, like professional certificate programs, bootcamps, or self-study courses.
- Build your technical skills.
Getting a job in data analysis typically requires having a set of specific technical skills. Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired.
Statistics
R or Python programming
SQL (Structured Query Language)
Data visualization
Data cleaning and preparation
Take a look at some job listings for roles you’d like to apply for, and focus your learning on the specific programming languages or visualization tools listed as requirements.
In addition to these hard skills, hiring managers also look for workplace skills, like solid communication skills—you may be asked to present your findings to those without as much technical knowledge—problem solving ability, and domain knowledge in the industry you’d like to work.
- Work on projects with real data.
The best way to learn how to find value in data is to work with it in real world settings. Look for degree programs or courses that include hands-on projects using real data sets. You can also find a variety of free public data sets you can use to design your own projects.
Dig into climate data from the National Centers for Environmental Information, delve deeper into the news with data from BuzzFeed, or come up with solutions to looming challenges on Earth and beyond with NASA open data. These are just a few examples of the data out there. Pick a topic you’re interested in and find some data to practice on.
Tip: For more inspiration, check out Coursera’s library of data analysis Guided Projects—a series of guided, hands-on experiences you can complete in under two hours.
- Develop a portfolio of your work.
As you play around with data sets on the internet or complete hands-on assignments in your classes, be sure to save your best work for your portfolio. A portfolio demonstrates your skills to hiring managers. A strong portfolio can go a long way toward getting the job.
As you start to curate work for your portfolio, choose projects that demonstrate your ability to:
Scrape data from different sources
Clean and normalize raw data
Visualize your findings through graphs, charts, maps, and other visualizations
Draw actionable insights from data
If you’ve worked on any group projects through the course of your learning, consider including one of those as well. This shows that you’re able to work as part of a team.
If you’re not sure what to include in your portfolio (or need some inspiration for project ideas), spend some time browsing through other people’s portfolios to see what they’ve chosen to include.
Tip: Sign up for a GitHub account and start posting your projects and code to the site. It’s an excellent spot to network with a community of data analysts, show off your work, and possibly catch the eye of recruiters.
- Practice presenting your findings.
It can be easy to focus only on the technical aspects of data analysis, but don’t neglect your communication skills. A significant element of working as a data analyst is presenting your findings to decision makers and other stakeholders in the company. When you’re able to tell a story with the data, you can help your organization make data-driven decisions.
What is data-driven decision-making (DDDM)?
Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.
This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [1].
As you complete projects for your portfolio, practice presenting your findings. Think about what message you want to convey and what visuals you’ll use to support your message. Practice speaking slowly and making eye contact. Practice in front of the mirror or your classmates. Try recording yourself as you present so you can watch it back and look for areas to improve.
- Apply for an internship or entry-level job.
After gaining some experience working with data and presenting your findings, it’s time to polish your resume and begin applying for entry-level data analysts jobs. Don’t be afraid to apply for positions you don’t feel 100-percent qualified for. Your skills, portfolio, and enthusiasm for a role can often matter more than if you check every bullet item in the qualifications list.
If you’re still in school, ask your university’s career services office about any internship opportunities. With an internship, you can start gaining real world experience for your resume and apply what you’re learning on the job.
- Consider certification or an advanced degree.
As you move through your career as a data analyst, consider how you’d like to advance and what other qualifications can help you get there. Certifications, like the Certified Analytics Professional or Cloudera Certified Associate Data Analyst, might help qualify you for more advanced positions at higher pay grades.
If you’re considering advancing into a role as a data scientist, you may need to earn a master’s degree in data science or a related field. Advanced degrees are not always required, but having one can open up more opportunities.
Home » STEM Career Paths » How to Become a Data Analyst in 2022
How to Become a Data Analyst in 2022
Here are five steps to consider if you’re interested in pursuing a career in data science:
Earn a bachelor’s degree in a field with an emphasis on statistical and analytical skills, such as math or computer science
Learn important data analytics skills
Consider certification
Get your first entry-level data analyst job
Earn a master’s degree in data analytics
What Does a Data Analyst Do?
A data analyst collects, processes and performs statistical analyses on large dataset. They discover how data can be used to answer questions and solve problems. With the development of computers and an ever increasing move toward technological intertwinement, data analysis has evolved. The development of the relational database gave a new breath to data analysts, which allowed analysts to use SQL (pronounced “sequel” or “s-q-l”) to retrieve data from databases.
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University of California, Berkeley
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Georgia Institute of Technology
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Typical Data Analyst Job Description
Most jobs in data analytics involve gathering and cleaning data to uncover trends and business insights. The day-to-day data analyst job varies depending on the industry or company or the type of data analytics you consider your specialty. Data analysts may be responsible for creating dashboards, designing and maintaining relationship databases and systems for different departments throughout their organization using business intelligence software, Tableau, and programming.
Most data analysts work with IT teams, management and/or data scientists to determine organizational goals. They mine and clean data from primary and secondary sources then analyze and interpret results using standard statistical tools and techniques. In most cases, they pinpoint trends, correlations and patterns in complex data sets and identify new opportunities for process improvement. Data analysts must also create reports on their findings and communicate next steps to key stakeholders.
Data Analyst Qualifications
Skills Required for Data Analysts
Programming Languages (R/SAS): data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand.
Strong and Effective Communication: Data analysts must clearly convey their findings — whether it’s to an audience of readers or a small team of executives making business decisions. Strong communication is the key to success.
Data Visualization: Effective data visualization takes trial and error. A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience.
Data Warehousing: Some data analysts work on the back-end. They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.
SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables and a data analyst pulls information from different tables to perform analysis.
Database Querying Languages: The most common querying language data analysts use is SQL and many variations of this language exist, including PostreSQL, T-SQL, PL/SQL (Procedural Language/SQL).
Data Mining, Cleaning and Munging: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.
Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.
Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not expected skill of typical data analyst jobs.
Data Analyst Responsibilities
A Day in the Life of a Data Analyst
The day-to-day for a data analyst depends on where they work and what tools they work with. Some data analysts don’t use programming languages and prefer statistical software and Excel. Depending on the problems they are trying to solve, some analysts perform regression analysis or create data visualizations. Experienced data analysts are sometimes considered “junior data scientists” or “data scientists in training.” In some cases, a data analyst/scientist could be writing queries or addressing standard requests in the morning and building custom solutions or experimenting with relational databases, Hadoop and NoSQL in the afternoon.
“A big part of my job is creating player projections for Fantasy Baseball. These power the default rankings in our draft rooms and inform my preseason and in-season rankings of players. Our readers and customers of our Fantasy product rely on the accuracy of these projections, so it’s important to have a sound statistical basis for making them.During the season, we have a high degree of interaction with our audience, as a large part of our responsibility is to respond to questions about player value and performance. Statistical analysis informs these recommendations, whether they are made through social media platforms, written and video content, or podcasts”
–Al Melchior, a Fantasy Sports Data Analyst for CBSSports.com
What tools do data analysts use?
Here are some other important tools data analysts use on the job:
Google Analytics (GA): GA helps analysts gain an understanding of customer data, including trends and areas of customer experience that need improvement on landing pages or calls to action (CTAs)
Tableau: Analysts use Tableau to aggregate and analyze data. They can create and share dashboards with different team members and create visualizations
Jupyter Notebook system: Jupyter notebooks make it simple for data analysts to test code. Non-technical folks prefer the simple design of jupyter notebooks because of its markdown feature
Github: Github is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming
AWS S3: AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large datasets
Data Analyst Job Outlook
Today’s data analysts should be prepared for a change. Analyst’s roles are increasingly becoming more complex. Experienced analysts use modeling and predictive analytics techniques to generate useful insights and actions. Then they have to explain what they’ve discovered to rooms of confused laymen. In other words, they have to transform themselves from data analysts into data scientists.
Market research analyst positions are expected to grow by 18% and management analyst positions are all expected to grow by 11%, which is much faster than the average job growth according to recent data from the Bureau of Labor Statistics. Because data analysts can fit in the majority of industries such as finance, healthcare, information, manufacturing, professional services, and retail – the growth of technology brings the growth of more analyst positions. We are collecting data at every turn, its organization, and implication of predictive analysis assists society in becoming a better version of itself.
Data Analyst Salary
Salary numbers are dependent on job responsibilities. A senior data analyst with the skills of a data scientist can command a high price.
Salaries for Data Analysts:
Average salary for entry-level data analysts: $83,750
Average salary for data analysts: $100,250
Average salary for senior data analysts: $118,750-$142,500
Interested in a different career? Learn more about bootcamps
If you’re interested in a career change or deepening your understanding of data analytics, you may want to consider a bootcamp. Tech bootcamps are fast-paced training in specific programming languages such as Python, R, or SQL that are offered in a variety of formats. Online coding bootcamps offer intensive learning experiences that mimic the real world and learn how to create projects from scratch.
If you’re interested in a career in analytics, attending a data analytics bootcamp may help prepare you for a new job opportunity. Data analytics bootcamps typically cover statistical analysis, analyzing data to uncover insights, using business intelligence software such as Tableau and other various tools data analysts may use on the job.
Another option is enrolling in a data science bootcamp. Data science bootcamps typically cover more advanced analytical concepts as well as machine learning, natural language processing and neural networks. If you’re unsure of which bootcamp to enroll in, consider your career goals and what you want to achieve in your existing role or new role.
Bootcamps can last a week to a few months depending on whether you enroll in a part-time or full-time program. You can choose an option that works with your schedule and learning goals. Some bootcamps offer prep courses and workshops to help ensure students’ success.
Data Analyst FAQs
What Should I Learn to Become a Data Analyst?
There are a variety of tools data analysts use day to day. Some data analysts use business intelligence software. Others may use programming languages and tools which have various statistical and visualization libraries such as, Python, R, Excel and Tableau. Other skills include:
Creative and analytical thinking
Communication
Database querying
Data mining
Data cleaning
Is Coding Required to be a Data Analyst?
Some data analysts are proficient in programming languages while others may use analytics software or Excel to analyze data and provide insights. Whether or not coding is required for a data analyst typically depends on the job or the employer. Employers may or may not list programming as a required skill for data analysts in job listings. It is important to look at the job description and consider your background before applying.
Is Being a Data Analyst a Good Career?
If you’re weighing your options between becoming a data analyst or data scientist, both occupations are projected to grow faster than average. Data analysts have a bright job outlook according to ONET OnLine (ONET). The projected growth for data scientists and data analysts is 8% between 2019 and 2029. Data analysts salaries typically depend on where they work and their industry. According to O*NET, data analysts earned an average annual salary of $94,280 in 2019.