Skip to main content

Assess Your Team’s Skills in Python, R and SQL With DataCamp Signal

DataCamp Signal™ is an adaptive assessment tool that uses state-of-the-art techniques to give you a bird’s-eye view of your team’s skill level.
Feb 2020  · 4 min read

Data skills are essential for any organization to remain competitive in the 21st century, but without a scalable means of assessing skills, it can be difficult to know where you stand. DataCamp Signal™ solves this problem by helping you understand how skills are distributed within your organization—across departments, teams, and individuals.

A guided learning process

Signal uses state-of-the-art techniques from adaptive testing and psychometrics to help teams and individuals quickly gain a bird’s-eye view of their skill levels in Python and R. It achieves this through a guided learning process.

When a learner completes an assessment, they receive a score (0-200), a percentile (0%-100%), and an associated knowledge level (Beginner, Intermediate, Advanced) based on their performance. By diagnosing the learner’s most prominent strengths and skill gaps, Signal is able to provide a list of personalized learning recommendations to address their gaps. As learners improve their skills over time by completing the recommendations, they can retake the assessment to measure their progress.

How it works

Signal skill assessments are built on a century of research into test theory, with a focus on building statistical models to answer three key questions:

1. Which questions yield the most information about a learner?

Signal isn’t like other tests: it uses Item Response Theory (IRT) to estimate how difficult questions are, how effectively they separate beginners from experts, and ultimately to estimate an individual learner’s skill level. This means learners write actual code in addition to completing multiple-choice questions, and the difficulty of the assessment automatically adjusts based on performance.

2. How can we adaptively select better questions as an assessment progresses?

Signal uses Computerized Adaptive Testing (CAT) to estimate an assessment taker’s skill as they progress through an assessment. This means questions become increasingly more informative, so in under 10 minutes, Signal is able to obtain a high degree of information on a learner’s skill level.

3. How can we use data from many assessments to improve their overall effectiveness?

Finally, by using assessment data from thousands of DataCamp learners, Signal is able to refine its estimates of how difficult and discerning of skill level questions are. Signal uses this information to flag questions that may not be indicative of skill and to report a learner’s standing relative to other learners.

Measure the right skills

Signal focuses on core data skills to take the guesswork out of what your teams need to know. For example, we offer assessments that cover all aspects of the data science workflow in the two most popular data science technologies, Python and R, as well as an assessment in SQL:

  • Programming
  • Importing & Cleaning Data
  • Data Manipulation
  • Data Visualization
  • Machine Learning
  • Data Analysis
  • Understanding & Interpreting Data

Create a skills matrix for your organization

One of the techniques our customers find to be most useful is taking a broad inventory of their in-house data skills using what’s called a skills matrix. This comes in many different forms, but generally involves visualizing strengths and skill gaps at either the organization, department, team, or individual level.

Customers looking for a scalable and reliable way to evaluate data skills across their organization use Signal results as an input to their skills matrix. We’re currently laying the groundwork to automate this process so this information is available to Enterprise admins on demand.

Help your team develop personalized learning plans

Signal provides personalized course recommendations based on learners’ strengths and skill gaps, helping them make the most of the time they spend learning on DataCamp. You can explore detailed results to see how each learner’s Signal score was calculated, pinpoint where they made mistakes, and track progress over time.

To explore further, read our detailed white paper on how Signal is an effective and reliable tool for data skill assessment.


How to Become a Data Analyst in 2023: 5 Steps to Start Your Career

Learn how to become a data analyst and discover everything you need to know about launching your career, including the skills you need and how to learn them.
Elena Kosourova 's photo

Elena Kosourova

18 min

Data Analyst Interview Questions: How to Prepare for a Data Analyst Interview

If you are hunting for your first data analyst job or looking to move up in your career, use this guide to help prepare for your interview, practice some data analyst interview questions, and land your dream job.
Matt Crabtree's photo

Matt Crabtree

12 min

A Beginner's Guide to Predictive Analytics

In this article, we discuss what predictive analytics is, explore some examples of how it is used, and look at how it works.
Joleen Bothma's photo

Joleen Bothma

10 min

20 Data Analytics Projects for All Levels

Explore our list of data analytics projects for beginners, final-year students, and professionals. The list consists of guided/unguided projects and tutorials with source code.
Abid Ali Awan's photo

Abid Ali Awan

17 min

Gary Wolf- Dataframed 119.png

Data-Driven Thinking for Everyday Life

Gary Wolf talks about what The Quantified Self is, why self-tracking projects can be life-changing, how to get started with self-tracking, and how to connect with others.

Richie Cotton's photo

Richie Cotton

55 min

Top Techniques to Handle Missing Values Every Data Scientist Should Know

Explore various techniques to efficiently handle missing values and their implementations in Python.
Zoumana Keita 's photo

Zoumana Keita

15 min

See MoreSee More