4 Skills You Need to Get Started in Data Analytics
4 Skills You Need to Get Started in Data Analytics

From smart power grids to self-driving cars, the world is increasingly driven by data. So it’s not surprising that data analysts are in high demand. According to O*NET, data analyst jobs are expected to surge by 36% in Washington state through 2031, as organizations of all kinds look to take advantage of the insights that data analytics can deliver.

Data analysts bring a powerful combination of technical chops and statistical knowledge to their role. The data analyst gathers raw data from a variety of sources and organizes it so it can be effectively analyzed to answer business questions. Then they manipulate the data, look for patterns, and create charts or graphs to communicate their insights back to the business.

If you're angling for a career in data analytics, you might be wondering just where to begin. Here are four key skills you'll need to get started.

1. Know Your Stats

For starters, data analysts need a good foundation in college-level statistics, according to Greg Weber, a Microsoft data scientist who teaches in the UW Certificate in Data Analytics: Techniques for Decision Making. Data analysts use mathematical formulas known as algorithms to look for patterns in the data, and they must have a grasp of essential statistical concepts like linear regression and standard deviation to be able to evaluate different algorithms and select one that best addresses the business question.

“You need to understand what each algorithm can do, so you can make sure that the answer you get is something that’s trustable.

Greg Weber, data scientist at Microsoft

“What does that F statistic do, and why? What does this P value mean? What is this R-squared thing telling me?” Weber said. “Data analysts have to learn those things. You need to understand what each algorithm can do, so you can make sure that the answer you get is something that’s trustable.”

2. Get Comfortable With Databases

Another skill data analysts need is some experience working with databases. As their name implies, databases are a top source of data, and data analysts have to be comfortable using basic SQL commands to wrangle, organize and “clean” data so it’s uniform and ready to analyze.

“When you give data to the algorithm, it’s trying to connect the dots,” Weber said. “If some rows have wildly different data — sort of apples and oranges — the answer might be a little less meaningful. When you feed cleaner, more consistent data into an algorithm, the algorithm will do a better job of giving its answer.”

▸ Learn This: Foundations of Databases & SQL Programming

3. Learn a Programming Language

Anyone who wants a career in data would be wise to learn at least one programming language suited for data analysis work, Weber said.

Why? Data analysts use programming to do everything from harvesting and manipulating data to running algorithms on it.

If you’ve got a background in computer science, Python is probably your go-to programming language given its ease and versatility, according to Weber. But if you don’t think of yourself as a programmer, learning the R language — though harder to master — is an excellent choice because of its powerful analytical and statistical capabilities.

“In R you can do the most analysis in the fewest lines of code,” Weber said. “It’s super compact and super focused and efficient.”

Yes, it’s possible to land a job in data without programming expertise if you can master “drag-and-drop” statistical software, Weber acknowledged. But, he said, the UW Certificate in Data Analytics: Techniques for Decision Making teaches R to people who don’t have previous programming experience — and that’s a big advantage for job seekers just starting out in the field.

▸ Learn This: Foundations of Programming (Python)                       

4. Visualize This

Just as important as the data analysis itself is being able to effectively distill the findings to stakeholders. For this, data analysts need some data visualization skills. Data viz tools let you turn rows and rows of data into meaningful graphs and charts. While R and Python both have extensive data visualization capabilities, there are also dedicated data visualization tools like Tableau and Power BI.

“Once you come up with the insight — what the data means in terms of the business — you need to be able to effectively communicate that back to the business,” Weber said.

▸  Learn This: Data Visualization Essentials With Power BIUW Certificate in Data Visualization With Tableau

Get Started Today

Ready to launch your data analytics career? The UW Certificate in Data Analytics: Techniques for Decision Making helps you build these must-have skills and many more. If you need help meeting the program's statistics or database admission requirements, you might consider taking Foundations of Databases & SQL Programming course first. 

Want to explore other options? Compare our data programs or browse all our programming and tech offerings.

For more career tips and industry trends, visit the News & Features section of our website, and subscribe to our email list. To learn more about UW Professional & Continuing Education certificates, specializations, degrees and courses, explore your options or contact us.

  Get our email newsletter with career tips, event invites and upcoming program info.       Sign Up Now