The majority of companies today realize the value of a data-driven business strategy and are in need of talented individuals to provide insight into the constant stream of collected. information.

Essential Skills for Data Analysts

Statistics for Data Analytics

SQL

Almost every business requires someone who knows SQL, whether it’s to manage and store data, connect multiple databases (like the ones Amazon uses to suggest alternatives you might like,) or develop or update database architecture entirely. Thousands of job posts requiring SQL abilities are submitted each month, with a median compensation of well over $75,000 for someone with expert SQL knowledge. While even non-techies can benefit from knowing SQL, understanding SQL is the first step toward working with Big Data.

SQL, or Structured Query Language, is the ubiquitous industry-standard database language and is possibly the most important skill for data analysts to know. The language is often thought of as the “graduated” version of Excel; it is able to handle large datasets that Excel simply can’t.

Microsoft Excel

When you think of Excel, the first thing that comes to mind is likely a spreadsheet, but there’s a lot more analysis power under the hood of this tool.
Stressing the importance of Microsoft Excel skills almost seems laughable when one considers the significantly more advanced technology data analysts have at their disposal. To borrow a quote from Irish business writer Anne Walsh, “Mention Excel to techies, and it’s often dismissed with a sniff.”

Critical Thinking

Using data to find answers to your questions means figuring out what to ask in the first place, which can often be quite tricky. To succeed as an analyst, you have to think like an analyst. It is the role of a data analyst to uncover and synthesize connections that are not always so clear. While this ability is innate to a certain extent, there are a number of tips you can try to help improve your critical thinking skills.

R or Python–Statistical Programming

Anything Excel can do, R or Python can do better—and 10 times faster. Like SQL, R and Python can handle what Excel can’t. They are powerful statistical programming languages used to perform advanced analyses and predictive analytics on big data sets. And they’re both industry standard. To truly work as a data analyst, you’ll need to go beyond SQL and master at least one of these languages.

Data Visualization

As the term suggests, data visualization is a person’s ability to present data findings via graphics or other illustrations. The purpose of this is simple: It facilitates a better understanding of data-driven insights, even for those who aren’t trained in data analysis. With data visualization, data analysts can help a business’s decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance. This capability empowers you — the data analyst — to gain a better understanding of a company’s situation, convey useful insights to team leaders, and even shape company decision-making for the better.

Machine Learning

As artificial intelligence and predictive analytics are two of the hottest topics in the field of data science, an understanding of machine learning has been identified as a key component of an analyst’s toolkit.

Power BI.