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Data analysis is the process of applying statistical analysis and logical techniques to extract information from data. When carried out carefully and systematically, the results of data analysis can be an invaluable complement to qualitative research in producing actionable insights for decision-making.
If that sounds a lot like data science, you’re right! It’s a closely related field, but there are important differences. Data scientists typically come from computer science and programming backgrounds and rely on coding skills to build algorithms and analytic models to automate the processing of data at scale. Data analysts typically have backgrounds in mathematics and statistics, and frequently apply these analytic techniques to answer specific business problems - for example, a financial analyst at an investment bank.
Data analysts don’t do as much coding as data scientists, but it’s still important to know your way around certain programming languages. In particular, SQL (Structured Query Language) is the industry standard for navigating large databases, and statistical programming languages like R or Python are essential for performing advanced analyses on this data.
According to Glassdoor, the average median annual salary for a data analyst was $69,291 as of November 2019. Of course, because data analysis is in demand across a wide range of industries, the salaries of two data scientists with similar job descriptions might be quite different depending on whether they're working with a small startup or a global hedge fund. Around this median, Glassdoor found data analyst salaries as high as $105,000 and as low as $48,000.
As with data science, online courses are a great way to learn data analysis skills, and Coursera offers Professional Certificates, MasterTrack certificates, Specializations, Guided Projects, and courses in data analysis from top universities like Duke University, University of Michigan and companies like IBM and pwc.
As you gain experience as a data analyst, you may encounter opportunities to advance your career in a few different directions. Depending on your goals and interests, you may progress into data science, management, consulting, or a more specialized data role. Read more about the data analyst career paths and salaries in this article.
People with strong math and statistical skills are best suited for roles in data analysis. A data analyst is responsible for collecting data and performing statistical analyses on a large dataset, so it’s important that people in data analysis roles are organized, detail-oriented, and able to work smart on tight deadlines. In addition to high-level math skills, a data analyst should be familiar with various programming languages and have the ability to analyze and summarize datasets.
Many data analysts work on Wall Street or with hedge funds to help investors and big banks make financial decisions for their portfolios and clients. These data analysts are responsible for collecting and analyzing huge amounts of financial data for colleagues and clients. Common career paths for someone in data analysis also include working in health care or insurance companies.
It’s important for anyone studying data analysis to have strong math skills, so learners may consider topics that cover inferential statistics, probability and data, and data science for math skills. A data analyst also needs to be familiar with computer programming, so topics that examine applied data science with Python are a must. For learners interested in how to do data analysis in a team setting, topics in managing data analysis and building a data science team may help you realize your team’s potential and offer managing and planning tips.
An investment bank is the most common place that hires someone with a background in data analysis. Financial institutions often hire data analysts for a management or leadership track. The government hires people with a background in data analysis to collect and interpret data. Health care companies including insurance companies hire data analysts to manage data from insurance companies, billing claims, and patient satisfaction surveys.
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