Data Science Training Program

Data science training has to be top of mind for any learning and development team. Dubbed “the sexiest job of the 21^st century,” this emerging field has become a necessity as businesses try to sift through an overabundance of data.

In 2010, former Google CEO Eric Schmidt famously said that every two days we create as much data as we did from the dawn of time through 2003. And since then, the data explosion has only continued. Every minute:

• Americans use over 2.5 billion gigabytes of data
• Instagram users post 46,750 photos
• Over 15 million text messages are sent
• Google executes 3.6 million searches

Given such massive data usage, it’s no surprise that data science and its variations are the fastest-growing jobs in the United States. Unfortunately, data scientist supply hasn’t kept up with demand.

When talent development leaders implement solid data science training programs, they can help their organizations overcome the talent shortage and create competitive advantages from within.

What is Data Science?

Key Skills to Focus on with Data Science Courses

Data Science Training - Key Learning Paths

Before you can help your organization build a data science training program, you have to understand what data science actually is.

Since the title “data scientist” was coined in 2008, the term has been used to cover a wide range of functions. But at the core, data science is the use of various tools, algorithms, and techniques to identify hidden patterns in large volumes of data.

Unlike traditional data analysis, data science aims to go beyond insight discovery to proactively make decisions and predictions based on informed historical data patterns.

The job of a data scientist is to make use of techniques like predictive and prescriptive analytics as well as machine learning to make sense of both structured and unstructured data. There’s just one mistake that talent development leaders have to avoid when building a data science training program—confusing this discipline with machine learning.

The key to unlocking the value of eLearning is giving your workforce a guided set of courses to hone their skills rather than a massive library of disconnected courses. 

That’s why the LinkedIn Learning library is organized with learning paths that give employees the perfect sets of courses to advance their careers.

The following learning paths can help you build an effective data science training program:

• Become a Data Scientist: Since data science is still so new, you may have employees that are just starting out with the official discipline. Whether they’re working in IT or just have an interest in entering the field, this set of courses will help your internal talent build a foundation to succeed with data science and deliver value for your organization.
• Become a Data Science Team Member: Data scientists of all levels don’t work in vacuums. By working together, your workforce will get more out of an abundance of data. Here, anyone in your organization can learn the basic mission of data science and find out how to impact a data science team as a collaborator.
• Get Ahead in Data Science: Once employees have gained the foundational skills for data science, they have to build on those skills to generate more business value. This learning path focuses on advanced statistics and data mining and offers insight into the growing fields of open data and blockchain.
• Master Python for Data Science: Python is an essential coding language for data scientists and this learning path can build competency quickly. Rather than focusing on Python as a general object-oriented language, these courses zero in on its role in the data science stack.
• Master R for Data Science: R has become the most popular data-science-specific language. This learning path teaches new data scientists all the skills necessary to build a coding resume and gain expertise with tools like Excel and Tableau.
• Advance Your Skills in the Hadoop/NoSQL Data Science Stack: Tools based on the Hadoop and NoSQL stack are becoming essential pieces of any data science career. Giving your workforce the resources necessary to learn tools like Kafka, HBase, Hive, and Cassandra will result in more effective data engineering.

The data science field may be new, but it’s growing rapidly and changing constantly. It’s not enough to take a one-and-done approach to data science training—you want a continuous approach to education to maintain competitive advantages.

These learning paths just scratch the surface of the LinkedIn Learning library for data science courses. Check out the full catalog of courses and learning paths to see what you can use to improve your data science team.