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A CertNexus Certification Training Course

Certified Data Science Practitioner (CDSP)

Apply data science methods to solve business problems and deliver measurable value

The Certified Data Science Practitioner (CDSP) training course develops structured capability in applying data science techniques to analyse data, build predictive models, and translate insights into practical business solutions that support informed decision-making and performance improvement.

Core Competencies:

Data Science Foundations
Machine Learning Models
Business Analytics
Insight Delivery

Course Schedule

18 - 22 May 2026

London

| $5950

17 - 21 Aug 2026

Amsterdam

| $5950

23 - 27 Nov 2026

Dubai

| $5950

17 - 21 Aug 2026

Online

| $3950

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Introduction Key Learning Outcomes Training Methodology Who Should Attend? Outline

Introduction

In a data-driven economy, organisations rely on data not only to understand current performance but also to anticipate future trends and guide strategic direction. Data science plays a central role in transforming raw data into actionable insights that inform decisions, optimise operations, and create competitive advantage. Without a structured and repeatable data science approach, organisations risk underutilising data or drawing unreliable conclusions.

The Certified Data Science Practitioner (CDSP) training course equips professionals with the practical knowledge required to apply data science principles across the full analytical lifecycle. Participants learn how to prepare data, explore and analyse datasets, design machine learning approaches, and evaluate model performance in a business context. The course emphasises applying data science techniques to real-world business challenges, ensuring insights are relevant, interpretable, and aligned with organisational objectives.

Key focus areas include:

Applying data science to address business questions
Preparing and analysing datasets using structured techniques
Designing and evaluating machine learning models
Communicating insights and deploying data-driven solutions

Key Learning Outcomes

At the end of this Certified Data Science Practitioner (CDSP) training course, participants will be able to:

Analyse business problems using structured data science methodologies
Apply data preparation techniques to extract, transform, and organise datasets
Evaluate data using statistical and exploratory analysis techniques
Design and assess machine learning models for classification, regression, and clustering
Interpret model outputs to support business decision-making
Integrate data science outputs into operational and strategic processes

Training Methodology

This Certified Data Science Practitioner (CDSP) training course adopts a practical, application-focused learning approach centred on real business data scenarios. Participants work through structured exercises, analytical workflows, and applied examples to build confidence in using data science techniques. Emphasis is placed on practical problem-solving, analytical reasoning, and translating technical outputs into business-relevant insights.

Who Should Attend?

This Certified Data Science Practitioner (CDSP) training course is ideal for:

Data analysts seeking to advance into data science roles
Professionals applying data to solve business problems
Technology and digital transformation specialists
Business analysts working with advanced analytics
Risk, compliance, and assurance professionals using data-driven insights
Professionals involved in analytics, modelling, and data-driven decision-making

Course Outline

Day 1

Addressing Business Issues with Data Science

Initiate a data science project
Formulate a data science problem

Extracting, Transforming, and Loading Data

Extract data
Transform data
Load data

Day 2

Analyzing Data

Examine data
Explore the underlying distribution of data
Use visualizations to analyze data
Preprocess data

Day 3

Designing a Machine Learning Approach

Identify machine learning concepts
Test a hypothesis

Developing Classification Models

Train and tune classification models
Evaluate classification models

Day 4

Developing Regression Models

Train and tune regression models
Evaluate regression models

Developing Clustering Models

Train and tune clustering models
Evaluate clustering models

Day 5

Finalizing a Data Science Project

Communicate results to stakeholders
Demonstrate models in a web app
Implement and test production pipelines

International Standards & Professional Alignment

Our training courses are aligned with internationally recognised professional standards and frameworks across leadership, strategy, finance, governance, risk, compliance, and audit. By integrating globally trusted models, we ensure learners develop practical, relevant, and industry-recognised capabilities.

Our trainings draw on leading international standards and professional frameworks, including ISO, ISACA, COSO, OECD, IIA, FATF, Basel, IFRS/ISSB, GRI, NIST, CPD, ILM and the OECD AI Principles. This alignment ensures consistency with global best practices across financial management, risk oversight, digital governance, sustainability, and strategic decision-making..

Designed in alignment with globally recognised professional bodies, our courses support continuous professional development, strengthen organisational capability, and provide clear pathways toward professional certifications valued worldwide.