Data Intelligence & Reporting Professional (CDIP)

Course Category : Data Management

A professional technical programme focused on transforming raw data into actionable intelligence and producing reliable analytical reports that support evidence-based decision-making using recognised best practices. 5 Days – Intermediate Level

Introduction

In today’s data-driven economy, data analysis is no longer a purely technical skill but a strategic capability that directly influences organisational decision quality and sustainability. This programme is designed to equip participants with a comprehensive understanding of the full data lifecycle—from data acquisition and cleansing to analysis, visualisation, and actionable insight generation within business contexts.
The course integrates analytical thinking, statistical foundations, data mining techniques, and modern visualisation tools, while addressing governance, compliance, and professional exam readiness..

Targeted Audience

Data Analysts and Business Intelligence Analysts
Data and Information Administrators
Data and Analytics Managers
Entry-level and intermediate Data Scientists
Non-technical professionals seeking to understand the business value of data (Marketing, HR, Finance, Market Research)

Targeted Skills

Data Analysis and Decision-Making
Data Cleaning and Preparation
Business Intelligence and Statistics
Insight Generation
Professional Data Visualisation

Expected Outcomes

Apply the full data analytics lifecycle from source to final reporting.
Identify and manage different data types and sources effectively.
Use statistical and analytical methods to support business decisions.
Develop foundational analytical and predictive models.
Produce professional data visualisations to communicate business cases.

Training Topics Index

Data intelligence concepts and strategic value
The data analytics lifecycle
Business intelligence and decision support
Risks and challenges in data projects

Data sources and collection channels
Public and organisational data
Contemporary data laws and compliance
Data types and storage models

Data quality challenges
Data cleansing and preprocessing techniques
Variable selection and encoding
Preparing data for statistical analysis

Descriptive and inferential statistics
Data mining techniques
Predictive modelling and classification
Introduction to machine learning and NLP

Analytical visual design principles
Exploratory and explanatory graphics
Visualisation tools
Preparation for the EXIN Data Analytics Foundations exam

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code