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:

1. Earn a bachelor’s degree in a field with an emphasis on statistical and analytical skills, such as math or computer science
2. Learn important data analytics skills
3. Consider certification
4. Get your first entry-level data analyst job
5. 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.

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.

–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 22% and management analyst positions are all expected to grow by 14%, 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 O*NET OnLine (O*NET). The projected growth for data analysts is 15% between 2020 and 2030. Data analysts salaries typically depend on where they work and their industry. According to O*NET, data analysts earned an average annual salary of $98,230 in 2020.

Last updated: April 2022

This page includes information from O*NET OnLine by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA.