Become a Data Analyst

Data analysts are in high demand, and they earn good money. If you have an interest in the field and want to get started, here’s how to become a data analyst.

1. Get a degree in statistics or business analytics

The most common path to becoming a data analyst is through an undergraduate degree in statistics or business analytics. These programs will teach you the fundamentals of math, business practices, computer programming and more. You can also choose to get an MBA with an emphasis on business analytics or a master’s degree in data science.

2. Get experience through internships or part-time work

Data analysts usually start their careers as interns or junior employees at companies where they work on projects related to data analysis. You can find these types of opportunities by searching job listings online or asking your professors if they know anyone who could help you get started with your career search when you graduate from college (or even before).

3. Find out more about this career path by reading books like “Data Analytics: A Guide For Beginners” by William Coggeshall; “How To Think Like A Data Scientist” by O’Reilly Media; “Data Science For Business: What You Need To Know About Data Mining And Big Data Analytics”

How To Become 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 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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Case Western Reserve University info

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.

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Columbia University info

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.

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University of California, Berkeley info

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.

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Georgia Institute of Technology info

Georgia Tech Data Science and Analytics Boot Camp

Expand your skill set and grow as a data scientist. Georgia Tech Data Science and Analytics Boot Camp covers the skills needed to analyze and solve complex data analytics and visualization problems.

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University of Texas at Austin info

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.

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University of Southern California info

USC Viterbi Data Analytics Boot Camp Affiliated with Trilogy Education Services

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.

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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.comExternal link:open_in_new

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%External link:open_in_new and management analyst positions are all expected to grow by 14%External link:open_in_new, 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 analystsExternal link:open_in_new: $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 outlookExternal link:open_in_new according to O*NET OnLine (O*NET). The projected growth for data analysts is 15%External link:open_in_new 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.