Data analysts collect, process and perform statistical analyses of data. Their skills may not be as advanced as data scientists (e.g. they may not be able to create new algorithms), but their goals are the same – to discover how data can be used to answer questions and solve problems.
What is a Data Analyst?
Analyzing data begins with its roots in statistics which, itself, stems into a long history into the period of pyramid building in Egypt. In some other later, but still early forms, data analysis can be seen in censuses, taxing, and other governmental roles across the world.
With the development of computers and an ever increasing move toward technological intertwinement, data analysis began to evolve. Early data analysts use tabulating machines to count data from punch cards. In 1980, the development of the relational database gave new breath to data analysts, which allowed them to use Sequel (SQL) to retrieve data from databases.
Today, data analysts can be found in a wide array of industries utilizing programming languages and statistics to pull, sort and present data in many forms in the benefit of the organization, people, and/or company.
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Data Analyst Responsibilities
Depending on their level of expertise, data analysts may:
- Work with IT teams, management and/or data scientists to determine organizational goals
- Mine data from primary and secondary sources
- Clean and prune data to discard irrelevant information
- Analyze and interpret results using standard statistical tools and techniques
- Pinpoint trends, correlations and patterns in complicated data sets
- Identify new opportunities for process improvement
- Provide concise data reports and clear data visualizations for management
- Design, create and maintain relational databases and data systems
- Triage code problems and data-related issues
Data analysts are sometimes called “junior data scientists” or “data scientists in training.” Instead of being free to create their own big data projects, they may be limited to tackling specific business tasks using existing tools, systems and data sets.
However, there are plenty of companies who don’t make a clear distinction between the two roles. 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.
An Interview with a Real Data Analyst
We got in touch with Al Melchior, a Fantasy Sports Data Analyst for CBSSports.com, to learn more about the work being done by data analysts. Read on to find out how data analysis is used to create fantasy sports player rankings, the tools he uses on the job, and what types of people make the best data analysts.
A: 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.
A: My title is Data Analyst, but on the surface, it may be hard to tell the difference between my job and that of my colleagues who have the title of Fantasy Writer. My colleagues are making increasing use of sophisticated analytical tools and methods. Since I began at CBSSports.com more than five years ago, I have been asked to bring the results of my analyses to an increasingly broader range of forums. Initially, I produced projections, created data visualizations and wrote columns. Now I also contribute to videos, a daily podcast and an active Twitter account.I’m not sure there is a significant difference now between my job and work process and those of my colleagues who are “writers.” We are all data journalists to some degree, and the lines between analyst and writer are getting blurred.
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. An entry-level data analyst may be looking at around $54,000 per year, with basic technical tools .
Salaries for Data Analysts
Average Salary for Data Analysts – Glassdoor: $67,377 per year
Average Salary for Data Analysts – PayScale: $59,335 per year
Total Pay Range: $42,000 – $83,000
Salaries for Senior Data Analysts
Average Salary for Senior Data Analysts – Glassdoor
Average Salary: $83,794 per year
Average Salary for Senior Data Analysts – PayScale
Median Salary: $79,024 per year
Total Pay Range: $59,000 – $106,000
Note: Salary information from Glassdoor and PayScale was retrieved as of July 2019.
Data Analyst Qualifications
What Skills Are Required to Become a Data Analyst?
- Statistical Programming
- Programming Languages (R/SAS)
- Creative and Analytical Thinking
- Strong and Effective Communication
- Data Visualization
- Data Warehousing and Business Intelligence Platforms
- SQL Databases
- Database Querying Languages
- Data Mining, Cleaning and Munging
- Advanced Microsoft Excel
- Machine Learning
Higher education degree in math, statistics, computer science, information management, finance or economics.
Most candidates for entry-level jobs will need a bachelor’s degree in math, statistics computer science, information management, finance or economics. All of these subjects place a heavy emphasis on statistical and analytical skills.
Consider additional analytical certifications.
There are scores of big data certifications available from independent organizations and specific companies (e.g. SAS). When in doubt, ask your mentors for advice, check job listing requirements and consult lists like the Big Data Certifications to determine which ones will help advance your career.
We take a closer look at this qualification in our section on Data Scientist Certifications.
Authorized by the non-profit Data Management Association International (DAMA), the CDMP credential is offered at four levels: associate, practitioner, master and fellow. To become an associate CDMP, candidates must have at least 6 months experience in their data role and a strong knowledge of DMBOK principles. Candidates who have more than 5 years of data management experience can begin their path to fellow CDMP as a practitioner. After 10 years and proven skill expansion and contribution to the data management profession, practitioners can apply to become a master CDMP. CDMP Fellows are extensive experience within the data management field and make consistent contributions to the field as a speaker, publisher of research, workshop presenter and other similar means.
If you’re new to SAS programming or SAS certification, this is one credential to consider. Sponsored by SAS, the certification exam tests candidates on their ability to import and export raw data files, manipulate and transform data, combine SAS data sets and identify and correct data, syntax and programming logic errors.
Jobs Similar to Data Analyst
“Data Analyst” is an umbrella term. In many cases, Market Analysts, Quantitative Analysts, Operations Analysts and other similar field-specific positions can be found under its shade. You’ll also see a good deal of job crossover with Business Intelligence Analysts, Data Warehouse Analysts and Business Systems Analysts.
As we’ve noted, senior data analysts are close siblings to Data Scientists and Analytics Managers. At the upper levels of management, there may be no clear distinction between the 3 roles.
Analysts who grow tired of analyzing may wish to investigate data construction jobs such as:
Data Analyst Job Outlook
Today’s data analysts should be prepared for a change. Self-service business intelligence software and automation is replacing many of the regular tasks that – in the past – technical experts would have been required to handle.
On the other hand, statistical gurus clean, filter and convert billions of diverse data points. They employ complex 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.
Financial analysts, market research analysts and management analyst positions are all expected to grow faster than the average job growth according to recent data from the Bureau of Labor Statistics. Because data analysts can fit in 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 Professional Organizations
Data analysts organizations provide opportunities for extended learning or specialized education and development in new trends or facets of the tech world. Members of these organizations may meet to discuss these trends, the future of data analytics and work on projects.
- Data Management Association International (DAMA)
- Data Science Association
- Digital Analytics Association (DAA)
- International Institute for Analytics (IIA)
- International Machine Learning Society (IMLS)
- Institute for Operations Research and the Management Sciences (INFORMS)
- Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD)