Assess Your Team’s Skills in Python, R and SQL With DataCamp Signal
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.
Guided learning consists of three steps: (1) assessing a learner’s skills; (2) providing them with next steps; (3) completing at least one recommended content item and repeating the cycle with another assessment as they make progress over time.
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:
- 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.