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.

Data analysts also rely on more typical business programs. While Microsoft Excel isn’t as powerful as SQL, R, and Python, it can get the job done when working with relatively smaller datasets, and may be the best (and cheapest) tool for the job for early-stage lean startups. Data visualization and presentation skills are also a huge part of the job, which typically requires learning new programs like Tableau as well as mastery of standard business software like Excel and Powerpoint.

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.

Many courses are shared between data science and data analysis, such as introductions to data science and data science programming with SQL, Python, and R. There are also courses more specific to data analysis, such as data analysis and interpretation skills, Excel skills for business, exploratory data analysis, and a variety of courses on statistics and probability.