Data Science skills testing made easy

QuantHub offers computer adaptive assessments for recruiters and team leaders seeking tools to test and baseline candidates and existing teams. Our comprehensive case study exams test beyond keyword knowledge and challenge candidates to synthesize business cases with real world data.

Identify And Grow The Right Talent

Building a strong advanced analytics team isn’t about finding data science and data engineering unicorns, but about bringing together a diverse team with unique skill sets. QuantHub assessments give granular insights into the data skills candidates do (and don’t) have.

Create Custom Assessments By Role

Measure conceptual understanding and hands-on proficiency. QuantHub
assessments are designed to test your
highest-level data scientists and data
engineers as well as functional analysts and entry-level data roles.

Receive Clear Results For Easy Decisions

QuantHub reports show both breadth
and depth of the skills assessed. This
gives each candidate a Score that shows you the candidate’s fit to the actual
job. Hiring managers get a clear view
into who has the strongest data skills overall, not just in one area.

Validate Final Candidates With Real-world Data Challenges

Benchmark your team against industry and identify skills gaps

Save Countless Hours Screening Candidates

Process thousands of applications simultaneously and focus on top performers for culture and fit.

80%

of corporate strategies explicitly mention data as a necessary driver of business value.
-Gartner

The industry's most comprehensive assessment for advanced analytics skills

Trusted by Recruiters and Analytics Team Leaders at the World’s Top Companies

Data Skill Assessments

  • Advanced Modeling
  • Applying Classification and Clustering
  • Applying natural language processing
  • Applying time series analysis
  • Data exploration and description
  • Hypothesis testing and inferential statistics
  • Regression and predictive modeling
  • Sampling techniques
  • Applications of Deep Learning
  • Applying Natural Language with Processing Deep Learning
  • Building Deep Learning Architectures
  • Executing Deep Learning
  • Framing Deep Learning
  • Introduction to Deep Learning
  • Preparing Data for Deep Learning
  • Deploying Machine Learning
  • Executing Machine Learning
  • Machine Learning Data
  • Exploring Data with SQL
  • Working with SQL
  • Wrangling Data with SQL
  • Exploratory Data Analysis with Python
  • Modeling with Python
  • Working with Python
  • Wrangling Data with Python
  • Applying OLAP/OLTP and Implementing Databases 
  • Data Structure and Formatting
  • Describing Data Essentials
  • Feature Engineering
  • Integrating and Managing Data
  • Managing Processing and Workflow
  • Selecting and working with databases and warehouses 
  • Exploring and Analyzing Data
  • Exploring Data Quality and Structure
  • Applying Data Storytelling 
  • Comparing Methods 
  • Describing Data Essentials 
  • Designing Charts 
  • Identifying Data and Methods 
  • Interpreting Visuals 
  • Selecting Visuals 
  • Using Data
  • Exploring Data with Spreadsheets
  • Working with Spreadsheets
  • Wrangling Data with Spreadsheets
  • Exploring Data with PySpark
  • Modeling with PySpark
  • Wrangling Data with PySpark
  • Analyzing Data with Power BI
  • Reporting with Power BI
  • Applying Data Storytelling
  • Building Data Storytelling Narratives
  • Choosing Data Storytelling Elements
  • Connecting Through Data Storytelling
  • Defining Data Storytelling Audience/Purpose
  • Designing Charts
  • Designing Visual Narrative Text
  • Designing Visual Narrative
  • Gathering Data for Storytelling
  • Incorporating Data in Data Storytelling
  • Interpreting Charts
  • Presenting Data Stories
  • Selecting Visuals
  • Structuring Data Storytelling Narratives
  • Applying AI in Organizations
  • Designing Human-Centered AI Products
  • Evaluating AI Product Performance
  • Introduction to Boosting Productivity with AI
  • Prioritizing AI Initiatives
  • Applying Data Ethics
  • Applying Data to Decision-Making
  • Citizen’s Guide to SQL
  • Collecting Data for Statistical Analysis
  • Defining Data Roles
  • Designing a Statistical Study
  • Designing Surveys
  • Establishing Data Governance
  • Establishing Organizational Data Strategy
  • Framing Data Problems
  • Identifying Chart Types
  • Interpreting Metrics
  • Interpreting Statistical Results
  • Interpreting Statistics
  • Introduction to Data Wrangling
  • Introduction to Deep Learning
  • Introduction to Reading Charts
  • Introduction to Visual Literacy
  • Managing Data Innovation
  • Managing Data Projects
  • Protecting Your Data
  • Selecting Data Tools
  • Shaping Organizational Data Culture
  • Uncovering the Story in Data
  • Working with Dashboards

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