Certified Data Science Practitioner (CDSP)

Start Date End Date Venue Fees (US $)
19 Jul 2026 Dubai, UAE $ 3,900 Register
06 Dec 2026 Al-Khobar, KSA $ 4,500 Register

Certified Data Science Practitioner (CDSP)

Introduction

Data science in its core uses mathematics and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data, while Data management is defined as the practice of organizing and maintaining data processes to meet ongoing information lifecycle needs. These insights are used for science-based decision-making and strategic planning. Through this training course, the delegates will be learning and applying the adequate methods and tools for data analysis and giving the data its true value. This highly participative training course will address data management principlescyber security risks and mitigation measures, business process mining, as well as the application of data science within enterprises. This training course on Data Science, covers a discussion of critical areas of Data ManagementProcess Mining, and Data Analytics in the modern world, with the issues that have arisen in terms of data insights, correlation, and forecasting.

Participants attending will develop the following competencies:

  • Understand the elements of the Data Life Cycle
  • Get acquainted with data and mathematical concepts of data science
  • Identify the optimal ways for process mining within their organization
  • Use data science methodologies and toolkits, including data storytelling
  • Get to apply statistics to analyze and understand data sets
  • Apply analytics methods to industry and business scenarios in different industries

Objectives

    The training course on Data Science aims to help participants to develop the following critical objectives:

    • Understand the meaning and impact of strategic thinking in Data Science,
    • Know how to determine adequate methods for data cleaning,
    • Develop skills in identifying bias in the data,
    • Use methods to perform process mining within the organization,
    • Understand the theory of graphs and the importance of data visualization,
    • Build good data analysis models,
    • Get acquainted with different data analysis software.

Training Methodology

This training course will combine presentations with instructor-guided interactive discussions between participants relating to their individual interests. Practical exercises, video material and case studies aiming at stimulating these discussions and providing maximum benefit to the participants will support the formal presentation sessions. Above all, the course leader will make extensive use of case examples and case studies of issues in which he has been personally involved.

Who Should Attend?

This Certificate in Data Science training course is suitable for a wide range of professionals but will be particularly beneficial to:

  • Technology Engineers, Chief Technology Officer (CTO) and Chief Information Officer (CIO)
  • CEOs
  • CTOs, CIOs and Engineers
  • Data Scientists, Data Analysts
  • Statisticians and technology personnel
  • Marketing and research specialists
  • Project Managers, Project Engineers
  • Supply Chain and Logistics personnel
  • Anyone who is using Data Analysis in their day-to-day work

Course Outline

Day 1 - Data Management and Data Science

  • Data Life Cycle
  • Data visualisation
  • Data quality
  • Use of software for data analysis
  • Available AI software platforms

Day 2 - Methodologies within Data Science

  • Statistical analysis of data
  • Graph theory
  • Matrices
  • Linear programming
  • Multi-criteria decision making
  • Univariate, Bivariate and Multivariate Statistics

Day 3 - Process mining

  • Data mining,
  • Process analytics,
  • Discover, validate and improve workflows,
  • AI in process mining
  • Increase operational process efficiency

Day 4 - Machine learning

  • Machine learning, deep learning, and neural networks
  • Machine learning process
    • Decision process
    • Error Function
    • Model Optimization Process
  • Supervised machine learning,
  • Un Supervised machine learning,
  • Semi-supervised learning

Day 5 - Real-world Data Science project

  • Data gathering and data quality,
  • Process mining,
  • Automation,
  • Optimization,
  • Predictive and prescriptive analytics.

Accreditation

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