Foundations of Data Science

Foundations of Data Science

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The Trainer:

Dr Mohammed Atari CEng CSci MIPEM

Dr Mohammed Atari received his BSc in Biomedical Engineering from Jordan University of Science and Technology and then moved to the UK to pursue his postgraduate studies, obtaining a first-class MSc in Advanced Biomedical Engineering from the University of Warwick. He was awarded the University of Warwick Vice Chancellor’s Scholarship to work towards his PhD in Biomedical Systems Modelling, which he earned in 2010. His doctoral thesis looked into mathematically modelling the kinetics and dynamics of the anti-cancer agent topotecan.

Dr Atari then joined Cyprotex (acquired by Evotec in 2016) as a Mathematical Modeller, a contract research organisation specialising in in silico and in vitro ADME-Tox services, where he developed novel mechanistic in silico (in vitro processes and physiologically-based pharmacokinetic) models for drug pharmacokinetics and pharmacodynamics. He was promoted to a Senior Mathematical Modeller (2016) and to a Principal Mathematical Modeller (2021), at the same organisation. He is the 2017 recipient of the Institute of Physics and Engineering in Medicine’s Innovation Early Career Award, recognising an early career member, who has made a significant contribution to advancement of innovation in technology resulting in translation into clinical practice, related to physics and engineering applied to medicine and biology.

In 2021, Dr Atari completed an MSc in Statistics from Lancaster University; his dissertation looked into developing stochastic models to study the impact of restrictions imposed by the United Kingdom’s Government on the transmission of COVID-19. Additionally, these models explored the importance of vaccines in slowing down infections. His research interests lie primarily in the fields of scientific computing, data science, machine learning and artificial intelligence. He is a Chartered Engineer (CEng), Chartered Scientist (CSci) and a Member of the Institute of Physics and Engineering in Medicine (MIPEM).

In October 2023, Dr Atari transitioned to UCB, a multinational biopharmaceutical company, taking on the role of Principal Non-Clinical PK/PD Lead. In this capacity, he continues to contribute his expertise to advance pharmaceutical research and development.

Dr Atari is an experienced trainer in the field of data science, specialising in data mining and machine learning. With over 13 years of industry experience, he has worked with a diverse range of clients in various industries, helping them in developing their data analytics capabilities. He is known for his engaging and interactive training style, using real-world examples to help participants understand complex concepts in machine learning, such as supervised and unsupervised learning, decision trees, and deep learning. Dr Atari is highly skilled in programming languages such as Python, R and Matlab. He is passionate about empowering individuals and organisations to extract valuable insights from their data using the latest techniques and tools in data science and machine learning.


Program Summary:

The use of Data Science in the Banking and Finance industry has become more than essential. Data Science has become a trend in every sector and it has gained its importance due to how it works and how easily it makes the work simpler. With the help of Data Science, banks will be able to focus on their resources efficiently, make smarter decisions, and improve their performances according to their standard targets set.

Data science combines computational and statistical skills to solve data-driven problems. This set of training courses will provide Data Science Teams with the analytical tools they need to design and build advanced technical solutions using modern computational approaches with a focus on rigorous statistical thinking. Knowledge of mathematical methods is expected. Prior experience in high-level programming language (e.g. Python, R and/or Matlab) is desired. The programme combines training in core statistical and machine learning methodology, beginning at an introductory level with a range of modules covering more specialised knowledge in data science, statistical computing and modelling.

This comprehensive training programme is meticulously designed to cater to the needs of professionals operating within diverse roles in the banking and finance sector. This includes seasoned data scientists, financial analysts, risk managers, investment bankers, or individuals working in any other capacity within this dynamic industry. The primary method of training is through seminars; understanding of seminar material is reinforced by computer workshops (hands-on experience) and group tutorials, as well as by self-study.

The 45-hour Foundations of Data Science programme is thoughtfully divided into three comprehensive modules: Statistics for Data Scientists (Module 1), Fundamentals of Statistical Inference (Module 2), and Introduction to Data Science (Module 3). Each module is carefully crafted to provide a detailed understanding of key concepts in the field. Upon successful completion of the programme’s entirety (i.e., the three modules), participants earn accreditation from The CPD Group (, underscoring the programme’s commitment to maintaining high standards in professional development. This accreditation is a testament to the rigorous curriculum and ensures that participants receive recognition for their dedication to advancing their expertise in Data Science within the banking and finance sector.

Note: Participants are required to bring laptops, preferably their own.


The objective of the training program:

The primary objective of the Foundations of Data Science training programme is to equip professionals in the Banking and Finance industry with a comprehensive and advanced understanding of Data Science principles. Participants will embark on a transformative learning journey that not only acknowledges the indispensable nature of Data Science in today’s dynamic landscape but also empowers them to harness its potential effectively.


Key Goals:


  • Holistic skill development: the programme aims to cultivate a diverse skill set encompassing computational, statistical, and analytical competencies. Trainees will acquire a profound understanding of how to leverage these skills synergistically for data-driven problem-solving.


  • Rigorous statistical thinking: through a thorough exploration of statistics and statistical inference (Modules 1 and 2), participants will develop a solid foundation in statistical methodologies. The emphasis on Bayesian, frequentist, and Fisherian inference ensures a comprehensive grasp of inferential methods.


  • Practical application: trainees will gain hands-on experience in designing and constructing advanced technical solutions using modern computational approaches. The focus on real-world applications, statistical computing, and modelling ensures practical relevance.


  • Ethical data science practices: the Introduction to Data Science module (Module 3) underscores the importance of ethical considerations in data science projects. Participants will engage in critical discussions on privacy, fairness, and ethical implications, fostering a responsible approach to large-scale data analytics.


  • Professional accreditation: completion of the entire 45-hour programme, consisting of three meticulously crafted modules, leads to accreditation from The CPD Group ( This accreditation serves as a testament to the participants’ commitment to maintaining high standards in professional development and acknowledges their expertise in Data Science within the banking and finance sector.


The content of the training program:

Module 1: Statistics for Data Scientists

This course aims at providing trainees with strong foundation in statistics which is crucial for any data scientist. Trainees will review and complete exercises on basic probability theory, statistical estimation and hypothesis testing. In addition, this module will cover practical statistics and associated computing.

Module 2: Fundamental of Statistical Inference

The aim of this module is to explores the primary approaches to statistical inference, covering point estimation, hypothesis testing, and the construction of confidence sets. The emphasis is on explaining the fundamental principles of Bayesian, frequentist, and Fisherian inference. The module provides a formal treatment of statistical inference through a decision-theoretic framework. It delves into key aspects of Bayesian and frequentist theory, concentrating on inferential methods related to significant parametric problems and the application of data reduction principles. The module also extensively covers general-purpose inference methods based on the maximum likelihood principle. Throughout the course, special attention is devoted to evaluating and comparing the properties of different inference methods.

Module 3: Introduction to Data Science

This module concentrates on the fundamental techniques and concepts necessary for the execution and upkeep of professional data science projects. It encompasses topics such as suitable code and project structures, webscraping, managing large datasets, creating reproducible workflows, scalability, and exploratory data analysis. Additionally, the module delves into a critical examination of both appropriate and inappropriate applications of data science, addressing privacy, fairness, and ethical considerations in the context of large-scale data analytics.



Target groups:

The target audience for the Foundations of Data Science training programme in the banking and finance sectors includes:

  • Data Scientists
  • Financial Analysts
  • Risk Managers
  • Investment Analysts
  • Investment Bankers
  • Actuaries
  • Quantitative Analysts
  • Statisticians
  • Financial Modellers
  • Quantitative Researchers
  • Business Intelligence Analysts
  • Data Analysts
  • Data Engineers
  • IT Professionals in Banking and Finance

This training programme is tailored to accommodate professionals with diverse roles and responsibilities within the banking and finance sectors, providing valuable insights and skills applicable to a wide range of positions.


Number of training hours:

Statistics for Data Scientists (Module 1): 15 hours

Fundamentals of Statistical Inference (Module 2): 15 hours

Introduction to Data Science (Module 3): 15 hours


The time of the training program:

Statistics for Data Scientists (Module 1): 12-5 pm

Fundamentals of Statistical Inference (Module 2): exact times TBC

Introduction to Data Science (Module 3): exact times TBC


The Period:

Statistics for Data Scientists (Module 1): 25-27 February 2024

Fundamentals of Statistical Inference (Module 2): April 2024 (exact dates TBC)

Introduction to Data Science (Module 3): May 2024 (exact dates TBC)


Participation fees for each program: 440JD



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Date And Time

2024-02-25 to 2024-02-27

Date And Time

2024-02-25 to



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