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CertNexus Certification — Data Science Practitioner

Certified Data Science
Practitioner™

A vendor-neutral, high-stakes certification designed for data professionals, programmers, and analysts to validate knowledge and skills in data science.

Exam Code: DSP-210
90 Questions · 120 Min
Pearson VUE
Certified Data Science Practitioner
90
Exam Items
6
Core Domains
72%
Pass Score
35%Data Science Job Growth
2022–2032 (U.S. BLS)
$6T+Global Analytics
Market Value (2025)
500K+Open Data Science
Positions Globally
ISO/IEC17024:2012
Accredited Exam
Data Science Career Paths

Roles You Can Pursue
After CDSP Certification

Data science is consistently one of the fastest-growing fields globally. CDSP holders qualify for practitioner, engineering, and leadership roles across industries.

Data Scientist
Data Analyst
Data Engineer
Data Architect
Database Administrator
Business Analyst
Data & Analytics Manager
Business Intelligence Analyst
Marketing Analyst
BI Developer
Product Owner
Machine Learning Engineer
Software Engineer
Analytics Consultant
Data Science Careers
Exam Specification

CDSP Exam Details

This exam certifies that the successful candidate has the knowledge, skills, and abilities to collect, wrangle, and explore data sets — applying statistical models and AI algorithms to extract and communicate knowledge and insights.

Target Candidate

Professionals across different industries seeking to demonstrate the ability to gain insights and build predictive models from data.

Exam Code
DSP-210
Launch Date
September 2024
Duration
120 Minutes
Includes candidate agreement + Pearson VUE tutorial time
Passing Score
72% / 69%
Depending on exam form (statistically equated)
Number of Items
90 Questions
75 count towards final score; Multiple Choice / Multiple Response
Exam Options
Pearson VUE
In-person test center or Pearson OnVUE online proctoring
Why Get CDSP Certified
Why Get Certified

Why the CDSP™ Sets You
Apart in Data Science

Organizations have access to large and ever-increasing quantities of data. A CDSP can make sense of this information, capturing and applying it to improve organizational practices and performance.

Prove Your Skills
Validate your ability to extract key insights from large datasets and drive your organization forward.
Lead Data Teams
Confirm capabilities of existing team members and those you are considering onboarding for data roles.
Vendor-Neutral
Cross-industry recognition applicable across Python, R, SQL, cloud platforms, and any data stack.
Career Advancement
Data science is the #1 fastest-growing field. CDSP sets you apart when seeking new roles or advancing.
Zetlan Data Science Training

Hands-On Data Science
Training for CDSP

Data can be very powerful, but only if handled and applied properly. Zetlan's CDSP training empowers you to use data to understand where your organization is heading and meet critical goals with informed decisions.

Live Instructor-Led Training
Expert-guided sessions with real-world data science projects and lab exercises
Recorded Sessions
Access class recordings to revise at your own pace, anytime
Technical Support
Dedicated support via Call and WhatsApp on all working days
Exam Preparation
Mock tests, practice questions, and exam strategy included
Data Science Training
Course Curriculum

CDSP Exam Domains

Six core domains covering the complete data science workflow — from problem scoping through model deployment and communicating findings.

Identify the project scope — objectives, metrics/KPIs, stakeholder requirements, and deliverables
Identify project limitations including time, technical, resource, data constraints, and risks
Understand stakeholder challenges — terminology, data privacy, security, and governance policies
Classify a question into a known data science problem — identify data sources and select modeling type
Gather relevant datasets — read data, research third-party data availability, collect open-source data
Clean datasets — identify/eliminate irregularities, parse data, check for corruption, correct formats, deduplicate
Merge datasets from different sources and apply problem-specific transformations (word embeddings, image representations)
Load data into databases, DataFrames, CSV files, visualization tools, and API endpoints
Examine data — generate summary statistics, examine feature types, visualize distributions, identify outliers and correlations
Preprocess data — identify missing values, decide on imputing methods, normalize, standardize, or scale data
Carry out feature engineering — encode categorical data, bin features, split features, convert dates, apply feature reduction
Prepare datasets for modelling — decide train/test/validation proportions and split data appropriately
Build training models — define algorithms to try, train model, tune hyperparameters
Evaluate models — define evaluation metrics, compare model outputs, select best performing model, store for operational use
Test hypotheses — design A/B tests, define success criteria, evaluate test results
Test pipelines — put model into production, ensure operational functionality, monitor pipeline performance over time
Implement model in a basic web application for demonstration (POC implementation)
Derive insights from findings — identify features that drive outcomes, explainability, variable importance
Show model results and generate lift or gain charts for stakeholder reporting
Start Your Data Science Journey

Ready to Become a Certified Data Science Practitioner?

Zetlan Technologies delivers expert-led CDSP training with hands-on labs, live sessions, and dedicated exam support.

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