4 Data Science Skills Everyone Needs to Know

Imagine trying to work in a modern business without basic computer skills. Companies no longer have to add “Know how to use a word processor and a spreadsheet” to job descriptions because it’s a given these days. That’s exactly the way to start thinking about data science skills. Data science is everywhere, just like smartphones or business credit cards, which have become so commonplace that competing in the new economy without them is downright difficult. To conduct a simple conversation around strategy using a common language, everyone in your team needs to know what you are talking about. An introduction to data science can benefit everyone from the CEO to the marketing team to customer support. Take a look at these four essential data science skills that deserve your immediate attention.

A Basic Understanding of How to Use Analytics

Data scientists tend to love analytics because there’s a wealth of information just waiting to be mined. The problem is that the information doesn’t always filter down to the people best positioned to use it. At the very least, managers should be able to specify which metrics they want to set up to get the answers they need. Competitors are already using data to make better decisions; businesses that don’t use data are practically flying blind.

Creating Graphics With Data Visualization Tools

When you rattle off numbers, get ready for blank stares. To communicate with urgency and motivational impact, you need to represent your results graphically. Even if you don’t plan on ever presenting numbers, you should know how to read different types of graphs, pie charts, scatter plots and other common visualizations. Look through these simple data visualization tools. A few come with advanced capabilities if you bring some development experience and others are perfect for non-technical users.

Applying Concepts in Statistics

Can you recognize the difference between correlation and causation? If your company introduces a new service and sees recurring revenue take a dive, you need to know how the two are related, if at all. You’ll need a fuller understanding of other factors in the market that impacted your revenue and what competitors are doing; otherwise, you may respond in the wrong way and make the problem worse. Review this slide deck by Moz founder Rand Fishkin on why statistical training for marketers is so critical.

Interpreting Results From Predictive Behavioral Targeting

Never before has it been so easy to “Know your customer.” Customer engagement levels have a direct impact on brand reputation, repeat business, referrals and profitability, which is why data scientists collect data from many sources. This approach lets them discover the strongest influences on customers and how engaged they are with content. It all starts with intelligent use of behavioral targeting tools, which only one-quarter of companies understand. Use this time to press your advantage and boost engagement the right way. Two More Skills on the Horizon Many specialized fields within the larger data science discipline are quickly becoming essential for basic business decision-making. Two good examples are Bayesian statistical models of confidence that help you compare alternatives, and machine learning tools that automate administrative tasks. Fortunately, the elements of data science are being built into next generation software so more people can use them with less advanced training. Don’t wait for a data scientist to explain it all to you. Prepare yourself now for increasingly turbulent and hyper-competitive markets. For more information, contact us today or visit our website.