How to Become a Data Analyst

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 datasets. They discover how data can be used to answer questions and solve problems. With new and advanced computers and a trend toward greater technological integration, the field of data analysis continues to evolve. The development of the relational database gave a new breath to data analysis, which allowed professionals in the field to use SQL (pronounced “sequel” or “s-q-l”) to retrieve data from databases.


Case Western Reserve University


CWRU Data Analytics Boot Camp

CWRU Data Analytics Boot Camp is a rigorous, part-time program that prepares students with the fundamental skills for data analytics and visualization. Through hands-on, in-person instruction, you’ll cover a wide range of topics and graduate ready to apply your skills in the workforce.

Columbia University


Columbia Engineering Data Analytics Boot Camp

Are you ready to become a data-driven professional? Columbia Engineering Data Analytics Boot Camp is a challenging, part-time bootcamp that equips learners with the specialized skills for data analytics and visualization through hands-on, in-person classes.

University of California, Berkeley


Berkeley Data Analytics Boot Camp

Turn data into actionable insights. Berkeley Data Analytics Boot Camp is a dynamic, part-time program that covers the in-demand tools and technologies for data analytics and visualization through rigorous, project-based classes.


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 duties vary depending on the industry, company or the type of data analytics you consider your specialty. Data analysts may be responsible for creating dashboards, as well as 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 staff 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: Data analysts should demonstrate curiosity and creativity, as they are considered 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 can enhance a company’s understanding of various issues.
  • 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: Data analysts understand what types of graphs to use, how to scale visualizations and know which charts to use depending on their audience. Effective data visualization takes trial and error. 
  • Data warehousing: Data analysts may 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: Data analysts are well versed in using SQL databases, which 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: Data analysts rely on database querying languages to carry out a host of tasks. 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: Data analysts must use other tools to gather unstructured data when it isn’t neatly stored in a database. 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 certain data analyst roles might not require proficiency in machine learning.

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

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: Jupyter Notebook makes it simple for data analysts to test code. Non-technical folks prefer the simple design of Jupyter Notebook because of its markdown feature.
  • GitHub: GitHub is a platform for sharing and building technical projects, and 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 change as the field is becoming more complex. Experienced analysts use modeling and predictive analytics techniques to generate useful insights and actions. They then have to explain what they’ve discovered to colleagues and stakeholders who may not have expertise in this domain.

Market research analyst positions are expected to grow 13% from 2022 to 2032 and management analyst positions are expected to grow 10% during the same time period. This is much faster than the average job growth rate for all occupations, according to the U.S. Bureau of Labor Statistics (BLS). Data analysts can find opportunities in a wide array of industries, including finance, healthcare, information, manufacturing, professional services and retail. The expansion of technology corresponds with the need for analyst positions in such sectors. We are collecting data at every turn, and its organization and use in predictive analysis can contribute to the improvement of society as a whole.

Data Analyst Salary

Salary frequently hinges on job responsibilities, along with various other factors. A senior data analyst with the skills of a data scientist may have the potential for a higher-than-average earning potential.


University of Texas at Austin


The Data Analysis & Visualization Boot Camp at Texas McCombs

The Data Analysis and Visualization Boot Camp at Texas McCombs puts the student experience first, teaching the knowledge and skills to conduct data analysis on a wide array of real-world problems. Students dive into a comprehensive curriculum, learning how to collect, analyze, and visualize big data.

University of Southern California


USC Viterbi Data Analytics Boot Camp

Expand your skill set and grow as a data analyst. This program covers the specialized skills to be successful in the field of data in 24 weeks.


Interested in a Different Career? Learn More about Boot Camps

If you’re interested in a career change or want to deepen your understanding of data analytics, you may want to consider a boot camp. Tech boot camps are fast-paced training in specific programming languages such as Python, R or SQL that are offered in a variety of formats. Online coding boot camps offer intensive learning experiences that mimic the real world, with students learning how to create projects from scratch.

If you’re interested in a career in analytics, enrolling in a data analytics boot camp may help prepare you for a new job opportunity. Data analytics boot camps typically cover statistical analysis, analyzing data to uncover insights and using business intelligence software such as Tableau and other tools commonly used by data analysts.

Another option is a data science boot camp. Data science boot camps typically cover more advanced analytical concepts as well as machine learning, natural language processing and neural networks. If you’re unsure of which boot camp to enroll in, consider your career goals and what you want to achieve in your existing or new role.

Boot camps can last a week to a few months depending on whether you opt for part- or full-time enrollment. You can choose an option that works with your schedule and learning goals. Some boot camps offer prep courses and workshops to help ensure students’ success.

Data Analyst FAQ

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 that 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 and 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 can come with benefits. Business intelligence analysts have a positive job outlook according to O*NET OnLine. From 2021 to 2031, employment of these professionals is expected to grow 11% or higher. Employment of data scientists is also projected to grow 11% or higher during the same time period.

Data analysts’ salaries typically depend on where they work and their industry.

Last updated September 2023.

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.