Is Data Analytics Hard to Learn?

Because the skills needed to perform Data Analyst jobs can be highly technically demanding, data analysis can sometimes be more challenging to learn than other fields in technology. But with those challenges come rewards; with the current global shortage of data professionals, the role of Data Analyst is in demand, meaning higher pay and better job security. A 2017 IBM study forecasted that demand would rise by another 28 percent by 2020, an increase of 364,000 jobs (to 2.7 million), further heightening that demand.

Can I Learn Data Analysis on My Own?

Yes, you can learn the fundamentals of data analysis on your own. As data analysis leans heavily on very focused knowledge of a discrete range of programming languages, as well as a range of easily developed soft skills, committed professionals with a knack for scripting can develop job-ready data skills over a relative short period of time.

The basics of SQL, for instance, can take as little as two to three weeks for a beginner to pick up. Programming languages are a bit more complex. Someone with a good grasp of programming fundamentals can usually pick up the basics of Python in about eight weeks. To learn R, a person with programming experience may need only a week of part-time study, while a beginner will likely need closer to three weeks of full-time study.

Of course, none of these skills are used in isolation. To learn all of the above at a beginner level—as well as a battery of other skills every Data Analyst should know—typically takes at least 12 to 14 weeks. The fundamentals you can acquire in that time will equip you to enter the field and begin working right away; to become fully proficient at a grandmaster level (something that can really only be done on the job) may take years.

It’s also worth mentioning that acquiring this knowledge isn’t one-and-done; as the industry continues to evolve, Data Analysts will likewise need to stay committed to ongoing learning to stay on top of changes and updates to languages, tools, and trends.