Introduction to Data Science

Available Dates

Venue

Dec-30-2024

Paris
Dec-30-2024

Dubai
Jan-06-2025

Dubai
Jan-06-2025

London
Jan-27-2025

Kuala Lumpur
Jan-27-2025

Singapore
Jan-27-2025

Amsterdam
Jan-27-2025

Istanbul
Jan-27-2025

Barcelona
Jan-27-2025

Paris
Apr-07-2025

London
Apr-07-2025

Dubai
Apr-28-2025

Istanbul
Apr-28-2025

Barcelona
Apr-28-2025

Amsterdam
Apr-28-2025

Kuala Lumpur
Apr-28-2025

Paris
Apr-28-2025

Singapore
June-02-2025

Dubai
June-02-2025

London
July-07-2025

Dubai
July-07-2025

London
July-28-2025

Barcelona
July-28-2025

Paris
July-28-2025

Istanbul
July-28-2025

Amsterdam
July-28-2025

Kuala Lumpur
July-28-2025

Singapore
Sep-15-2025

Dubai
Sep-15-2025

London
Oct-27-2025

Barcelona
Oct-27-2025

Paris
Oct-27-2025

Singapore
Oct-27-2025

Istanbul
Oct-27-2025

Kuala Lumpur
Oct-27-2025

Amsterdam
Nov-10-2025

London
Nov-10-2025

Dubai

Course Details

This comprehensive programme provides an in-depth overview of the burgeoning field of Data Science, designed to equip attendees with foundational knowledge of key concepts, tools, and techniques. Through theory and practical examples, participants will delve into data analysis, machine learning, data visualization, and other critical aspects of this essential discipline.

Upon completion of this course, attendees should be able to:

Understand the basics and importance of data science in modern business contexts.
Familiarise with key data science tools and techniques.
Apply data science methods for extracting valuable insights from data.
Comprehend the ethical considerations in data science.

This course is ideal for:

Aspiring data scientists looking to kick-start their journey.
IT professionals aiming to expand their understanding of data science.
Business managers seeking to leverage data-driven insights.
Any professional keen on understanding the value of data in decision-making.

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

Understanding Data Science

Overview of data science: definition, importance, and applications.
Key roles and responsibilities in a data science team.
Introduction to data types and data structures.
Basic principles of data management.

Data Analysis Basics

Introduction to statistics for data science: Descriptive & inferential statistics.
Exploratory data analysis and data cleaning.
Data wrangling and transformation.
Introduction to regression analysis.

Introduction to Machine Learning

Defining machine learning and its importance in data science.
Overview of supervised, unsupervised, and reinforcement learning.
Basic machine learning algorithms: Regression, classification, and clustering.
Training and validation of machine learning models.

Data Visualisation

The importance of data visualisation in data science.
Understanding different data visualisation tools.
Creating impactful visualisations: charts, graphs, heat maps.
Effective storytelling with data.

Ethics and Future Trends in Data Science

Discussion on ethical considerations in data science: privacy, bias, and fairness.
The role of data governance in data science.
Emerging trends in data science: AI, Big Data, IoT.
The future of data science: skills for success.

Introduction to Data Science

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Introduction to Data Science

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