Featuring Insights From Matt Brems
Read: 3 Minutes
Our Data-Driven World
We live in a world of data — swimming in statistics, numbers, information — and the amount of data seems to be growing faster than we can keep up. More people are using data points to make decisions large and small. From which restaurant has the highest Yelp rating to which city has the lowest rates of COVID-19, using data to navigate everyday life is now the norm. Indeed, the pandemic has only increased our reliance on data. We have come to expect this tsunami of data to explain, and in some cases solve, many of the most vexing problems faced by society today. But finding key insights takes careful analysis of a staggering amount of data. No small feat.
It’s true that more data is released than ever before. In the U.S., there are currently over 290,000 datasets on data.gov alone. Clearly, there’s a growing need for data analysts and the data analytics tools that help us understand these numbers. From small businesses to the highest levels of governments, decisions turn on interpretations of data. Big data can have big consequences.
So how do data analysts find the insights lurking in a database? And what are the best tools to analyze all those numbers? Read on to discover the best data analytics tools in the market.
Data scientist and GA instructor since 2016, Matt Brems currently runs a data science consultancy called BetaVector. We asked him to share his go-to data analysis tools. “People who want to analyze data use many different tools; I like to break these down into three different types,” he says.
Let’s get to it.
Type #1: Tabular Data Tools
Data analysts need to get data out of databases and analyze that information. And to do that, they use tabular data tools. According to Brems, the most important ones to know are Microsoft Excel, Google Sheets, and SQL, or Structured Query Language. Generally considered the best data analysis tool for research, SQL is the most common qualification found in job descriptions for a data analyst.
“Most data that data analysts analyze comes in the form of a table, called tabular data. This just means that data is organized into rows and columns, like a spreadsheet. Most data analysts will use a spreadsheet tool like Microsoft Excel or Google Sheets. When working with significant amounts of data (large tables, many tables, or both), organizations will often use a database. In order to interact with most databases, SQL is by far the language of choice.”
Type #2: Programming Language Tools
Proficiency in a few programming tools, while not a prerequisite for basic data analysis, can give analysts the ability to perform a wide variety of tasks. While the needed programming language tools will vary from company to company and even from job to job, having this skill set as a data analyst is clearly an advantage for job seekers.
“Python and R are the most common programming language tools in data analysis, though Stata and SAS are also used in some industries. These tools can be used to perform automation, statistical modeling, forecasting, and visualization.”
Type #3: Data Visualization Tools
Since data analysts are frequently tasked with presenting results to stakeholders, a good data visualization tool is essential. Brems recommends Tableau and Microsoft PowerBI.
“While you can visualize data using programming languages, Tableau and PowerBI are two standalone tools that are used almost exclusively for the purposes of building static data visualizations and dashboards.”
A Note on Research
When it comes to research, the most common data analytics tool is SQL — no surprise there. But once you get into more niche industries, that can vary, says Brems.
“In academia, R is probably the most prevalent data analysis tool, though Python is quickly gaining popularity. SAS and Stata are often used in specific industries, though their popularity is diminishing. (R and Python are open source tools, which means, among other things, that they are free.)”
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