Certified Data Scientist™ (CPDS™) spans the full data‑science workflow—from Python‑based data management and classical statistics to modern deep‑learning methods—so candidates can design, build, and deploy production‑ready analytics solutions.
The exam covers six parts—Data Scientist Foundation, Time‑Series Analysis, Data‑Reduction Methods, Advanced Analytics & Machine Learning, Natural Language Processing, and Neural Networks & Deep Learning—ensuring breadth across cleaning, forecasting, dimensionality reduction, ensemble modelling, text mining, and neural‑network fine‑tuning.
Successful CPDS™ holders prove they can combine rigorous statistics with diverse machine‑learning techniques and operationalise models for tasks such as demand forecasting, customer personalisation, anomaly detection, and text insight extraction.
Part 1 (10%)
Data Scientist Foundation
Python environment setup
Data structures
Data import/export
Data cleaning
Exploratory statistics
Hypothesis testing
Introduction to logistic regression
Basic data visualization
Part 2 (15%)
Time Series Analysis
Fundamental time series definitions
Time series decomposition
Stationarity concepts
ARIMA modeling
Seasonal ARIMA
Residual diagnostics
Forecasting methods
Model performance
Part 3 (10%)
Data Reduction Methods
Rationale for dimensionality reduction
PCA fundamentals
PCR workflow
Advantages/limitations of PCA and PCR
KMeans algorithm steps
Elbow Method
Importance of scaling
Implementation with libraries
Part 4 (25%)
Advanced Analytics and ML
Naive Bayes classification
K-Nearest Neighbors
Support Vector Machines
Decision trees
Random forest ensembles
Weight of Evidence & Information Value
Market Basket Analysis
Model evaluation metrics
Part 5 (15%)
Natural Language Processing
Foundational Skills in Python
Data Management Techniques
Statistical Foundations
Inferential Analyses
Basic Regression Concepts
ETL Awareness
Tools & Libraries
Robust Data Handling
Part 6 (25%)
Neural Networks and Deep Learning
Foundational Skills in Python
Data Management Techniques
Statistical Foundations
Inferential Analyses
Basic Regression Concepts
ETL Awareness
Tools & Libraries
Robust Data Handling
CPDS™ Certification Exam Structure
To earn the Certified Professional in Data Analytics (CPDATM) designation, candidates must successfully complete two online proctored examinations: a knowledge-based exam and a practical application exam.
Exam A: Test of Knowledge
This online proctored exam assesses a candidate’s theoretical understanding and applied knowledge across the data analytics domain. It includes multiple-choice, multi-select, and true/false questions. The exam duration is 120 minutes, and a minimum score of 70% is required to pass.
Exam B: Practical Application
Also delivered as an online proctored assessment, this exam presents candidates with realistic business scenarios and datasets. Candidates must demonstrate their ability to apply the data analytics lifecycle, analyze complex data, and communicate findings clearly. The exam duration is 180 minutes, and a minimum score of 85 out of 150 marks is required to pass.