Who can appear for the Chartered Data Scientist exam?

Age: 13 and over, with a parent's consent under 18. Experience: The candidate needs to have two years of relevant experience as a data scientist. Educational qualification: As data science is an open field, there is no educational criteria or background required to become a Chartered Data Scientist.

What if I do not have prior work experience?

A candidate can take the Chartered Data Scientist Exam even if the candidate does not carry the two years of the required experience. In this condition, the candidate will be given only the exam results and their charter remains on hold. Once they complete the two years of experience, they are awarded this charter. The candidate can inform the ADaSci through email at info@adasci.org once they complete the two years of experience. Although, the exam result also shows the knowledge of the candidate in the field of data science, and the same is acknowledged by the recruiters.

Can I appear for the exam if I am in the final year of graduation?

There are no education criteria to appear for the Chartered Data Scientist exam. You can appear at any point of time provided the age requirements.

Do I need to attend any training to become a Chartered Data Scientist?

You do not require to showcase any training to appear for the Chartered Data Scientist exam. It only requires two years of relevant work experience as a criteria.

When can i register for the CDS exam?

You can register for CDS exam at any time and schedule it at your convenience. The exam needs to be taken within one year of the date of the registration for the same.

What is the refund policy?

No refund is provided after registering for the exam.

Can i reschedule the exam?

The exam once scheduled cannot be rescheduled. However, showing a valid reason such as a personal emergency, it can be rescheduled for one time only.

Expert
Lifetime charter validity

The Chartered Data Scientist Designation (CDS™)

Achieve the highest distinction in the data science profession.

enrolled across ADaSci: 600+
exam questions: 150
proctored exam: 3 hours
examined: 10 domains

The Chartered Data Scientist (CDS™) Designation badge

From$249Enroll

Overview

What this credential certifies.

The Chartered Data Scientist (CDS™) designation is the highest professional distinction awarded in the field of data science and artificial intelligence.

At a glance

Format: Online
Content: Self-study
Exam: 150 multiple-choice questions
Duration: 3 hours
Pass mark: 70%
Delivery: Remote, proctored
Retake: $20 per attempt
Validity: Lifetime

Read the full overview

Conferred by the ADaSci , the CDS™ charter is a globally recognized mark of professional excellence, validating advanced technical expertise, applied analytics capability, multi-domain problem solving, and strict adherence to professional ethics.

The Chartered Data Scientist (CDS™) Designation

The Gold Standard Charter for Senior Data Scientists & AI Executives

Unlike short-term skills certificates or vendor-specific badges, CDS™ is a rigorous professional charter—combining a comprehensive 150-question computer-based examination with a mandatory 2-year full-time data science experience requirement. Holding the CDS™ designation signals to global employers, enterprise clients, and leadership teams that you possess the comprehensive, production-grade mastery required to lead data science and AI organizations.

2026 Market Intelligence: The Value of Charter Distinction

In an era flooded with fast-track bootcamps and unverified certificates, the CDS™ designation gives senior candidates a proctored, verifiable signal:

Career Leadership Signaling: CDS™ holders use the prestigious post-nominal CDS™ on LinkedIn, CVs, and executive signatures, differentiating themselves for Principal Data Scientist, VP of Analytics, and Chief AI Officer pathways.

The 10 Core Domains of the CDS™ Body of Knowledge

The CDS™ curriculum spans the full spectrum of advanced data science, advanced statistics, deep learning, and business deployment:

Section 1: Probability Theory, Statistics & Linear Algebra

Focus: Mathematical foundations of data science.
Skills: Hypothesis testing, Bayesian inference, vector spaces, matrix decomposition (SVD, PCA), and stochastic processes.

Section 2: Data Engineering, Databases & Lakehouses

Focus: Data architecture, storage, and retrieval foundations.
Skills: Relational SQL modeling, NoSQL systems, distributed storage, and open lakehouse formats (Apache Iceberg, Delta Lake).

Section 3: Exploratory Data Analysis & Feature Engineering

Focus: Uncovering statistical patterns and data preparation.
Skills: Dimensionality reduction, anomaly detection, missing value imputation, and feature transformation pipelines.

Section 4: Supervised & Unsupervised Machine Learning

Focus: Core classical machine learning algorithms and ensemble methods.
Skills: Gradient boosting (XGBoost, LightGBM), SVMs, random forests, clustering algorithms, and model validation techniques.

Section 5: Neural Networks & Deep Learning

Focus: Deep learning architectures and modern optimization.
Skills: Backpropagation mechanics, CNNs, RNNs, Transformers, attention mechanisms, and transfer learning.

Section 6: Natural Language Processing & Large Language Models

Focus: Text processing, sequence modeling, and foundation models.
Skills: Tokenization, word embeddings, transformer architectures, prompt engineering, and LLM fine-tuning.

Section 7: Computer Vision & Multimodal Systems

Focus: Image analysis and visual perception systems.
Skills: Object detection (YOLO), image segmentation, generative adversarial networks (GANs), and vision-language models.

Section 8: Model Deployment, MLOps & Observability

Focus: Moving models from notebooks to production infrastructure.
Skills: Containerization (Docker), CI/CD pipelines, model monitoring, drift detection, and serving frameworks.

Section 9: Programming Frameworks for Data Science

Focus: Advanced numerical computation and production coding.
Skills: Mastery over Python, R, PyTorch, TensorFlow, Scikit-Learn, and distributed PySpark execution.

Section 10: Business Strategy, Ethics & Responsible AI

Focus: Translating algorithms to business ROI and enforcing ethical guardrails.
Skills: Model interpretability (SHAP, LIME), AI governance, EU AI Act compliance, and executive communication.

Prerequisites & Charter Requirements

Examination Format:

150 computer-based multiple-choice questions (3-hour duration, remote proctored).
Tests trade-off reasoning, mathematical understanding, and scenario-based problem-solving across all 10 domains. No negative marking.

Experience Requirement:

Candidates must demonstrate a minimum of 2 years of full-time work experience in data science, analytics, or AI engineering to be awarded the charter.
Note: You may take and pass the exam prior to completing the 2 years; your charter will be awarded upon verification of experience.

Charter Validity:

Lifetime Validity: The CDS™ designation does not expire, provided holders maintain ethical standards established by ADaSci.

Who Should Pursue the CDS™ Charter?

Senior Data Scientists & ML Engineers: Solidifying cross-domain mastery and securing recognition for promotion to Staff/Principal roles.

Analytics Managers & AI Directors: Validating technical depth while leading enterprise analytics transformations.

Transitioning Engineers & Quantitative Analysts: Establishing a globally accredited, elite credential to switch into high-tier data science positions.

Consultants & Advisory Leaders: Providing enterprise clients with third-party proof of top-tier analytical capability.

Enrollment & Member Pricing

Standard Charter Fee: $249 USD
ADaSci Member Price: $174.30 (₹16,799) for General members (30% off) or $124.50 (₹12,000) for Premium members (50% off)
Includes: Complete 10-Section Curriculum Access, 150-Question Proctored Exam Voucher, Lifetime Designation Licensing, and Verifiable LinkedIn Digital Credential.

Register for the CDS™ Examination Today

What you'll be able to do.

Every outcome maps to a module in the syllabus and a domain in the exam.

Higher visibility in hiring and promotion decisions thanks to a recognized credential.
Increased earning potential and job stability in a competitive data science market.
Access to an elite professional network through the ADaSci membership associated with certification.

The Chartered Data Scientist (CDS™) Designation credential badge — Ten domains in the body of knowledge

The body of knowledge.

modules: 10
lessons: 150
of video: 37.6 h
proctored exam: 3 hours

- Video 1: Counting and probability Theory47 min
- Video 2: Statistical Tests30 min
- Video 3: Advanced Statistics13 min
- Video 4: Linear Algebra24 min
- Reference Reading MaterialsReading
- Course AssessmentReading
- Video Tutorial347 min
- Getting started with Data Engineering and DatabasesReading
- Links to Python NotebooksReading
- Course AssessmentReading
- Exploratory Data Analysis5 min
- Exploratory Data Analysis in Python10 min
- Descriptive Statistics4 min
- Descriptive Statistics in Python11 min
- Exploratory Data AnalysisReading
- Links to Python NotebooksReading
- Course AssessmentReading
- Supervised and Unsupervised Learning: Hands-on34 min
- Supervised and Unsupervised Learning: Intuition11 min
- Simple Linear Regression: Intuition17 min
- Simple Linear Regression: Hands-on13 min
- Random Forest Regression: Intuition8 min
- Random Forest Regression: Hands-on11 min
- Decision Tree Regression: Intuition5 min
- Decision Tree Regression: Hands-on7 min
- AdaBoost: Intuition7 min
- AdaBoost: Hands-on5 min
- ARIMA: Intuition10 min
- ARIMA: Hands-on8 min
- Decision Tree Classification: Intuition7 min
- Decision Tree Classification: Hands-on8 min
- Exponential Smoothing: Intuition12 min
- Exponential Smoothing: Hands-on12 min
- Gradient Boosting: Intuition6 min
- Gradient Boosting: Hands-on8 min
- Hierarchical Clustering: Intuition6 min
- Hierarchical Clustering: Hands-on5 min
- KNN Regression: Intuition5 min
- KNN Regression: Hands-on8 min
- KNN Classification: Intuition7 min
- KNN Classification: Hands-on6 min
- KMeans: Intuition13 min
- KMeans: Hands-on5 min
- Lasso Ridge ElasticNet: Intuition11 min
- Lasso Ridge ElasticNet: Hands-on14 min
- Logistic Regression: Intuition9 min
- Logistic Regression: Hands-on10 min
- Multiple Linear Regression: Intuition9 min
- Multiple Linear Regression: Hands-on11 min
- Moving Average: Intuition8 min
- Moving Average: Hands-on7 min
- Naive Bayes Classification: Intuition8 min
- Naive Bayes Classification: Hands-on6 min
- Principal Component Analysis: Intuition5 min
- Principal Component Analysis: Hands-on8 min
- Polynomial Regression: Intuition4 min
- Polynomial Regression: Hands-on5 min
- Random Forest Classification: Intuition7 min
- Random Forest Classification: Hands-on7 min
- SARIMA: Intuition4 min
- SARIMA: Hands-on5 min
- Support Vector Machine Regression: Intuition4 min
- Support Vector Machine Regression: Hands-on8 min
- Support Vector Machine Classification: Intuition6 min
- Support Vector Machine Classification: Hands-on8 min
- XGBoost: Intuition6 min
- XGBoost: Hands-on6 min
- Getting started with Supervised and Unsupervised LearningReading
- Links to Python NotebooksReading
- Course AssessmentReading
- Video 1: Introduction and Overviews5 min
- Video 2: Deep Learning and ANNs82 min
- Video 5: Convolutional Neural Network for Image Classification65 min
- Video 6: Recurrent Neural Network and LSTM Model22 min
- Video 7: Sequence Modeling25 min
- Video 8: Autoencoders and Image Reconstruction33 min
- Video 9: GAN for Fake Image Generation25 min
- Video 10: Neural Style Transfer41 min
- Video 11: Real-Time Object Detection in Images and Videos16 min
- Python NotebooksReading
- Important Readings: Neural Net and Deep LearningReading
- Gradient DescentReading
- Implementation in PythonReading
- Video tutorial on Gradient Descent8 min
- Hands-on implementations explained10 min
- Code For PracticeReading
- Hands-on implementation of neural networkReading
- Video Tutorial on Artificial Neural Networks11 min
- Video Tutorial on Implementation8 min
- Code For PracticeReading
- Neural Network Optimizers Implementation in PythonReading
- Neural Network Optimizers and its Different TypesReading
- Video Tutorial on Neural Network Optimizers8 min
- Video Tutorial on Implementation6 min
- Code For PracticeReading
- Backpropagation in PythonReading
- The Backpropagation AlgorithmReading
- Video Tutorial on Backpropagation5 min
- Video Tutorial on Implementation9 min
- Code For PracticeReading
- Neural Network Classification in PythonReading
- Neural NetworksReading
- Video Tutorial on Neural Network for Classification4 min
- Understanding The Implementation6 min
- Codes for PracticeReading
- Hands-on implementation in PythonReading
- Video Tutorial on Neural Network Regression8 min
- Understanding The Implementation3 min
- Codes for PracticeReading
- Complete Guide to Regularization of Neural NetworksReading
- Hands-on implementation in PythonReading
- Video tutorial on Regularization of Neural Networks6 min
- Understanding The Implementation4 min
- Codes for PracticeReading
- Course AssessmentReading
- Video 1: Introduction and Overviews5 min
- Video 3: Text Classification92 min
- Video 4: Word Embedding28 min
- Video 5: Sequence Modelling60 min
- Video 6: LSTM-RNN37 min
- Video 7: Transformers14 min
- Implementing NLP Techniques in PythonReading
- Important Reading in NLPReading
- Course AssessmentReading
- Video 1: Introduction to Computer Vision55 min
- Video 2: Introduction to Deep Learning18 min
- Video 3: Convolutional Neural Networks89 min
- Video 4: Multiclass classification and image recognition70 min
- Video 5: Transfer Learning50 min
- Video 6: Real-time object detection15 min
- Video 7: Neural Style Transfer6 min
- Video 8: Generative Adversarial Networks8 min
- Important Reading in Computer VisionReading
- Course AssessmentReading
- Challenges Deploying Machine Learning Models32 min
- Machine Learning Lifecycle48 min
- Model Compression Before Deployment58 min
- Model Deployment Cloud and Edge88 min
- Tools for Machine Learning Tracking103 min
- Deployment and Model ManagementReading
- Python NotebooksReading
- Course AssessmentReading
- Intuition15 min
- Hands-on25 min
- Basics of Programming Frameworks for Data ScienceReading
- Python NotebooksReading
- Course AssessmentReading
- Video: Business and Data Science19 min
- Important Reading: Business and Data ScienceReading
- Course AssessmentReading

Earned, never granted.

One computer-based exam across ten domains, with no negative marking; the charter follows once your experience is verified.

questions: 150
time limit: 3 hours
per retake: $20

70%

pass mark

See how ADaSci exams work

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Instant access to the curriculum and materials.
Learn at your pace
On your own schedule, alongside work.
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Claim your credential
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Pricing

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150-question charter examination
Ten domains, no negative marking
Charter on verified experience
Verifiable credential and badge

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Cohort plan

Train and certify a whole team with one plan.

Custom

Volume pricing for five or more seats
Private cohort and team dashboard
Live sessions with an instructor
Invoicing and procurement support

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Members

Member pricing

Members pay less on every credential, course and event.

30–50% off

List price$249
General$174.30
Premium$124.50

When membership pays off

General ($99 a year): you pay $174.30 (30% off). Premium ($299 a year): you pay $124.50 (50% off).

General ($99 a year) covers its fee from your 2nd credential at this price; Premium ($299 a year) from your 3rd.

Taking more than one credential, or buying for a team? See the break-even

What's included (4)

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Community, events and the journal
Upgrade any time

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Prices in USD; switch to INR above.Exam retakes cost $20.Refunds follow our terms of service.

In their words.

“This vendor-neutral certification goes beyond the hype and dives deep into what's actually shaping the future of AI: autonomous, multi-agent systems that can reason, collaborate, and act.”

Roberto Tovar ArellanoDigital & Data Tech BP Manager @ GSK

“This comprehensive program from ADaSci gave me a solid foundation to become an Agentic AI Engineer/Architect. The curriculum perfectly balances theory with practical, hands-on implementation.”

Tushar RohillaSenior Software Engineer, Kearney

“Thrilled to share my Certificate of Achievement for completing the "AI for Business Impact Program"! This training has equipped me with practical AI skills to drive efficiency in finance and accounting processes.”

Sukhmeet SinghFinance & Accounts Executive | SAP FICO

“A clear takeaway: As enterprises move toward agentic and semi-autonomous AI systems, the differentiator will not be access to models or data — but how well autonomy is architected, governed, and controlled.”

Narasimha MurthyDirector – Data Engineering | AI/ML & GenAI Adoption

“Experience alone isn't enough to stay effective in this field. This was a valuable opportunity to revisit how we think about designing autonomous systems with the right balance of independence and control.”

John PlataisEnterprise AI Executive | VP AI & Machine Learning

“I just completed the ADaSci – Certified Agentic AI System Architect certification. AI technology and communication networks have grown exponentially. The time has finally come for concrete, scalable and resilient systems.”

Filippo QuartaInformation Governance Advisor at Banca d'Italia

What the CDS™ letters signal.

Valid for life · no renewal fees

Earn the badge
Awarded by the ADaSci, not a course vendor.
Add CDS™ after your name
On CVs, profiles and email signatures.
Employers verify online
In seconds at adasci.org/verify-certificate

More reasons to earn the CDS™ certification · 3

The CDS designation is recognised globally as a “gold-standard” professional charter in data science, signalling that the holder possesses advanced skills, practical experience (minimum two years full-time in data science or a related role), and a commitment to ethical practice. The credential is developed and maintained by leading practitioners and academics under ADaSci’s oversight.

Global recognition and distinction as a certified data science professional.
Demonstration of comprehensive, cross-domain data science knowledge and experience.
Signalling adherence to professional ethics and standards as defined by ADaSci.

Verified

Your Name, CDS™

Lead Data Scientist

The Chartered Data Scientist (CDS™) Designation

ADaSci · Issued November 2026 · No expiry

Credential ID ADS-CDS™-••••

Show credential

More about the CDS™

Everything else, one tab away.

Who the CDS™ is for

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Roles and prerequisites for The Chartered Data Scientist (CDS™) Designation.

Roles The Chartered Data Scientist (CDS™) Designation suits

Data scientists, analysts, or AI professionals seeking to validate their expertise at a global level.
Professionals from adjacent fields (e.g., engineering, operations, business analytics) looking to transition into data science roles.
Students or recent graduates interested in making a strong entry into the data science profession by showcasing a globally recognised credential.

Requirements

Eligibility: 13 and over, with a parent's consent under 18.
Relevant work experience: At least 2 years of full-time experience in data science or a closely related field is required for the awarding of the charter. The candidate may appear for the exam before accumulating this experience, but the charter will be on hold until the experience requirement is met.
Exam structure: A computer-based multiple-choice exam featuring 150 questions, to be completed in 3 hours. There is no negative marking. The exam is offered in English and can be taken remotely.
It is a self-study certification. ADaSci does not require candidates to complete any formal training to sit the exam. It provides a detailed curriculum and study resources instead.
The awarded charter has lifetime validity, provided that all conditions (including experience and ethical adherence) are met.

Ask the team How exams work

04 — REQUIREMENTS

Requirements

•Eligibility: 13 and over, with a parent's consent under 18.
•Relevant work experience: At least 2 years of full-time experience in data science or a closely related field is required for the awarding of the charter. The candidate may appear for the exam before accumulating this experience, but the charter will be on hold until the experience requirement is met.
•Exam structure: A computer-based multiple-choice exam featuring 150 questions, to be completed in 3 hours. There is no negative marking. The exam is offered in English and can be taken remotely.
•It is a self-study certification. ADaSci does not require candidates to complete any formal training to sit the exam. It provides a detailed curriculum and study resources instead.
•The awarded charter has lifetime validity, provided that all conditions (including experience and ethical adherence) are met.

Continue the path.

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News

Latest news and updates

12 Nov 2026
November 2026 — Market Intelligence: EU AI Act High-Risk Enforcement Elevates Chartered Data Scientist (CDS™) Distinction for Senior AI Leadership
Enterprise AI adoption in late 2026 has officially entered an era of strict statutory governance. Following the full enforcement of EU AI Act high-risk compliance obligations alongside NIST AI RMF and stringent enterprise CISO security gates, corporate boards and hiring committees can no longer rely on unverified certificates or short-term bootcamp credentials when appointing senior AI leaders:
• The Executive Credibility & Governance Requirement: Enterprise hiring committees across Wall Street financial institutions, Fortune 500 tech firms, global consultancies, and healthcare networks now prioritize leaders who combine mathematical rigor, deep learning mastery, and model interpretability (SHAP, LIME) with binding professional ethics.
• The Post-Nominal Distinction: Like the CFA in finance, Charterholders use the globally accredited post-nominal CDS™ on LinkedIn, CVs, and executive signatures—differentiating themselves for Principal Data Scientist, VP of Analytics, and Chief AI Officer (CAIO) pathways.
• High-Impact Compensation Benchmarks: Senior and Principal Data Scientists holding accredited professional charters command total compensation packages ranging from $210,000 to $390,000+ in the US/EU, and ₹30 Lakh to ₹75+ Lakh LPA in India.
The Gold Standard Solution: The Chartered Data Scientist (CDS™) Designation
Conferred by the Association of Data Scientists (ADaSci), the Chartered Data Scientist (CDS™) designation is the premier professional charter for senior data science leaders. It validates mastery across 10 core domains—including statistical theory, open lakehouse architecture, deep learning, transformers, MLOps, and ethical AI stewardship—through a rigorous 150-question, 3-hour proctored charter examination and a 2-year verified work experience requirement:
1. Advanced Mathematical & Machine Learning Rigor: Demonstrate proven mastery across probability theory, linear algebra, supervised and unsupervised algorithms, transformers, and deep learning architectures.
2. Governance, Explainability & Executive Leadership: Master EU AI Act compliance, model interpretability (SHAP, LIME), production MLOps deployment pipelines, and strategic enterprise decision-making.
👉 Explore the CDS™ Charter & Lock In Pre-Revision Member Pricing →

Certification questions, answered.

7questions answered

info@adasci.org

Age: 13 and over, with a parent's consent under 18. Experience: The candidate needs to have two years of relevant experience as a data scientist. Educational qualification: As data science is an open field, there is no educational criteria or background required to become a Chartered Data Scientist.
A candidate can take the Chartered Data Scientist Exam even if the candidate does not carry the two years of the required experience. In this condition, the candidate will be given only the exam results and their charter remains on hold. Once they complete the two years of experience, they are awarded this charter. The candidate can inform the ADaSci through email at info@adasci.org once they complete the two years of experience. Although, the exam result also shows the knowledge of the candidate in the field of data science, and the same is acknowledged by the recruiters.
There are no education criteria to appear for the Chartered Data Scientist exam. You can appear at any point of time provided the age requirements.
You do not require to showcase any training to appear for the Chartered Data Scientist exam. It only requires two years of relevant work experience as a criteria.
You can register for CDS exam at any time and schedule it at your convenience. The exam needs to be taken within one year of the date of the registration for the same.
No refund is provided after registering for the exam.
The exam once scheduled cannot be rescheduled. However, showing a valid reason such as a personal emergency, it can be rescheduled for one time only.

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Keep reading

The Chartered Data Scientist Designation (CDS™)

What this credential certifies.

At a glance

The Chartered Data Scientist (CDS™) Designation

2026 Market Intelligence: The Value of Charter Distinction

The 10 Core Domains of the CDS™ Body of Knowledge

Prerequisites & Charter Requirements

Who Should Pursue the CDS™ Charter?

Enrollment & Member Pricing

What you'll be able to do.

The body of knowledge.

Earned, never granted.

Enroll

Learn at your pace

Sit the proctored exam

Claim your credential

Get job-ready, then get hired

Practise

Build

Show it

Get hired

One price. Everything included.

In their words.

What the CDS™ letters signal.

Everything else, one tab away.

Who the CDS™ is for

Roles The Chartered Data Scientist (CDS™) Designation suits

Requirements

Requirements

Continue the path.

Latest news and updates

November 2026 — Market Intelligence: EU AI Act High-Risk Enforcement Elevates Chartered Data Scientist (CDS™) Distinction for Senior AI Leadership

Certification questions, answered.

Earn the CDS™.

Read next

Interview with Sai Srikanth Gorthy, Chartered Data Scientist – A Data Science Visionary

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