Our Expertise
Data science and data literacy are multi-disciplinary fields that are rapidly evolving, expanding and cover a broad spectrum of skills & knowledge.
Elements builds on the strong expertise of Initiative for Analytics and Data Science Standards, IADSS, a leading industry standards organization.
IADSS is an independent non-profit initiative, aiming to present a framework for the knowledge and skills required in the world of data. It develops industry standards for data skills in full spectrum from data literacy to data scientists, through a collaborative process with businesses, academia, and data science community.
IADSS has since developed the most comprehensive framework for skills and knowledge in data science with input from 1000+ data science leaders and academicians.
Published at Harvard Data Science Review, the framework details the skills and knowledge that collectively define ‘data science’ as a field and provides much needed clarity for professional as well as organizations that are hiring, managing and training them.
Elements
Elements combine the extensive academic research and expertise of IADSS with a sharp focus on data skills. Each Element measures a specific data science expertise, and the questions created by our scientists extensively cover all the topics related to that specific expertise.
Our scientists mapped each data skills knowledge item in IADSS Framework to an Element and created a unique and innovative assessment approach. We worked with the experts and practitioners in the industry to assess the relevance and completeness of these Elements.
Elements covers an extensive range of data skills from basic data literacy to sophisticated data science.
Data literacy comprises of fundamental skills such as Critical Thinking, Ethics of Data; and technical skills including but not limited to Spreadsheets, Data Visualization in two levels with a total of 19 Elements including ChatGPT.
For data science, we have more than 20 Elements for assessing Data Scientists from Calculus to Deep Learning, from Data Structures and Algorithms to Natural Language Processing. Under these elements, there are more than 200 topics that further refine the individual skills that a Data Scientist or a Data Analyst should have.
In addition to our standardized skill-sets for Data Scientists and Data Analysts, you can also customize and pick a single skill or any combination that you would like. This allows you to build tests that are specific to your own internal role definitions. Other Elements use cases include measuring the effectiveness of training programs by testing individuals before and after training on the skills covered by the training program, or identifying gaps in skill-sets for upskilling programs.
Unique Tests
We built up a massive question library so that you can create a unique test for each applicant if you want to and to protect the integrity of the Elements testing IP.
NLP and Machine Learning
Elements uses Natural Language Processing and Machine Learning to generate new questions from an existing corpus curated by experts.
Expert Validation
Designing questions is our core competency. Each question is originally formulated and validated by different experts to offer the best questions fit for purpose.
Selected Elements
ChatGPT
Our ChatGPT element is a question answering system based on a Large Language Model(LLM) developed by OpenAI. LLMs took the world by storm in 2023 and there are literally thousands of start-ups founded with the sole purpose of employing LLMs for automating a variety of business tasks. As such, being able to employ ChatGPT -as being the most popular of LLMs known well by the general public- has become a requisite skill for data analysts and data scientists.
In data literacy, being able to employ ChatGPT has become a requisite skill for a variety of tasks in using data for better decisions. A new term, prompt engineering, was coined to represent the fine-tuning of the wording of conversation with ChatGPT so as to elicit the best response out of ChatGPT.
Critical Thinking
In data literacy, Critical Thinking covers the ability to analyze available facts with using data, observations, and arguments to form a judgment.
Transforming a business problem to a data problem and also identifying relevant data points or sets are the other topics a data citizen should be fluent in.
Deep Learning
We are living in an AI spring revived by the Deep Learning revolution that has been happening since 2012. Though classified as a sub-discipline of Machine Learning, it has acquired a life of its own and Deep Learning models have generated SOTA solutions in problems of computer vision, speech understanding and natural language processing. It is also competitive in tabular data as well.
The primary success of Deep Learning is end-to-end learning in multiple scenarios including unstructured complex data such as pictures, speech and text. End-to-end learning automatically extracts optimal features as a byproduct of learning and this has caused many breakthroughs in many complex problems.
Other Elements
Learn more about other Elements and their importance in data science and analytics.