Top 4 Skillsets For Data Analytics

I’m asked repeatedly by students and professionals, what skills are really needed to get into analytics? In speaking with executives and colleagues, it became apparent that beyond the interpersonal and critical reasoning skills, there were 4 technical skills that set candidates apart. Consider each technical skill like tools in a toolbox, you don’t necessarily need to know everything about it, but the more you know in any area, the higher your competitive advantage over your counterparts.

(The top four skills in reverse order.)

4. Programming Language

Having a programming language like Python or Java helps you handle some critical aspects of data retrieval and data cleansing that might not otherwise be easily handled in other tools. Moreover, understanding fundamentals of programming provide the analyst with understanding logical flow and how to separate data in a manner that is useful. If you know one language you can pretty much pick up any other language.

Some Popular Languages For Analytics: Python, Java, R, PHP, .NET

3. Statistical Tool

There is no doubt that statistical tools are a core ingredient in the tool box. However, with statistical tools, the need to understand statistical methods is required. If you’re going to do just basic descriptives, then these tools might not be the right tool, analogous to using a hammer to put in a screw in a wall. Ideally, analysts should be able to do things like regressions, and ANOVA tests, but you can still use these tools to do things without those statistical techniques. Many of these tools have great libraries to give you better visualization and charts that can’t be produced in spreadsheets, although there might be plug-ins for spreadsheets that will do them. Other tools that focus on visualization are great, but again its using the right tool for the right job that counts.

Some Popular Tools : R, SAS

Consider each technical skill like tools in a toolbox, you don’t necessarily need to know everything about it, but the more you know in any area, the higher your competitive advantage over your counterparts.

2. Database

Without databases there are practically no analytics! Everything we collect now is stored somewhere. Retrieving that data is not always easy and can be a very complex and inaccurate process. With terabytes of data at our disposal, analysts should possess a firm grasp of how to manipulate data in databases. Understanding basic SQL queries and table structure in many cases would be sufficient, but those analysts who want to do more, or work with more complex data, will need to learn non relational databases such as MongoDB, which store the data differently. Non relational databases are excellent for unstructured (non tabular) information such as data that comes from social media sites like Facebook.

Popular Tools: MySQL, Oracle, MSSQL Server – Any SQL based database

1. Excel

It shouldn’t be a surprise that this is the number one tool that analysts need to master. Anyone interested in business analytics should make this a primary tool in their toolbox. Excel is a great tool to layout data in tabular formats, and with the built-in functions anyone can do a great amount of analysis. Its’ charting capabilities are easy to use and the more advanced data functions are very useful in ordering and categorizing data. Lookup functions, pivot tables, analysis modules and statistical functions, make this the most versatile and ubiquitous tool in an analyst’s arsenal.

Put these four skills to use and you can find so many opportunties. Master these skills and sky is the limit.

Alexander Pelaez, Ph.D., is a President of Five Element Analytics, an analytics consulting firm. He has served as a senior executive to a number of firms in healthcare, retail and media. He is also a professor of Information Systems and Business Analytics at Hofstra University.