Python

Python is currently one of the most commonly used programming languages.

Having a solid understanding of how to use Python for data analytics will probably be required for many roles. Even if it’s not a required skill, knowing and understanding Python will give you an upper hand when showing future employers the value that you can bring to their companies.

If you’re ready to advance your programming language proficiency, learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications.

 SQL (Structured Query Language)

Working with data sources is a necessary aspect of data analytics.

Early in your career, you’ll need at least a basic understanding of SQL. SQL (pronounced sequel) is often a major component of these positions. When you go to interview, listen for hiring managers’ mentions of this programming language when asking about your work with databases.

The experience you’ll get in our SQL courses will give you a good foundation. Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way.

Knowing the basics of SQL will give you the confidence to navigate large databases, and to obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.

 Data Visualization Skills

Knowing how to visualize data and communicate results is a huge competitive edge for job seekers.

On the job market, these skillsets have high demand (and high pay)! Regardless of the career path you’re looking into, being able to visualize and communicate insights related to your company’s services and bottom line is a valuable skillset that will turn the heads of employers.

In this way, data scientists are a bit like data translators for other people in the organization that aren’t sure what conclusions to draw from their datasets.