Course introduction

Welcome to Big Data, AI & Machine Learning! ​

In this course, we are going to explore the rapidly evolving Big Data, AI & Machine Learning landscape and enable you to start identifying opportunities to drive economic value from these technologies within the Financial Services industry. ​​ 

The course has been designed as a comprehensive guide that will enable you to identify opportunities to implement Big Data, AI and Machine Learning initiatives. There are many challenges associated with getting started in AI – including a shortage of data scientists. And there is also a dearth of executives and non-technical workers able to identify opportunities to implement AI. But spotting these opportunities doesn’t necessarily require an advanced computer science degree, as we will show you.

Given the high data volume, large amounts of historical data and highly quantitative nature of the Financial Services, Big Data, AI & Machine Learning technologies lend themselves particularly well to this industry. Leading banks and Financial Services companies are deploying AI technologies to optimize processes, identify fraud, improve customer service, underwrite loans and automate trading decisions. We are going to explore many of these use cases in depth and equip you with a toolkit to identify your own.

​The course is aimed at executives and non-technical staff. Although it’s not a technical course, we will be covering a lot of detail about these technologies that will empower you to work with technologists and data teams in your organization. ​

This eLearning course has been developed by Alqami Courses. We deliver cutting edge data and technology education to executives in many leading organizations around the world, driven by our expertise as a data intermediary.

Contributors to the course include Harpreet Geekee, David Riley, and Jon Peters

Harpreet is a proven technical leader having held multiple CTO roles with Fortune 500 companies. Harpreet was previously the CTO for Juniper’s Global Financial Services Vertical responsible for helping clients in the Financial Services Industry with strategy, and solutions to achieve Digital Transformation. In his spare time, Harpreet is a guest lecturer at Syracuse University, teaching a course on Blockchain and AI architecture, is an avid yogi and self-proclaimed ‘Geek’.

Jon has significant experience across finance, telecoms and advanced technologies and has been advisor to a portfolio of early-stage tech companies in Big Data, AI/ML, IoT, AR/VR, Blockchain, Digital Marketing, and AdTech. Before Alqami, he was Managing Director of Theomobex, a technology company in the AI/M2M data analytics marketing space.

David has spent the last 20 years in strategy, leadership, business, and advisory roles in financial services and consulting firms. He has extensive experience in product and proposition development, as well as M&A and post-merger integrations, for both early phase and global organisations. He has held senior positions with Barclays, Deutsche Bank and the London Clearing House. 

The course is going to provide you with: 

  • A strong conceptual understanding of Big Data, AI and Machine Learning ​technologies
  • The ability to identify and assess opportunities for AI in your organization
  • A data strategy and governance framework that will help you to implement AI solutions ​
  • An informed view of Artificial Intelligence and its social & ethical impact ​
  • And an appreciation of future AI trajectory to help you make predictions

We have broken the course down into 6 sections:

Section 1 - Introduction to Machine Learning and Big Data​​

The first section will introduce you to the meaning of “Big Data”. We will explore various forms of real-world data, and the amount of data generated from different sources. We will also look at how to apply various AI techniques such as Machine Learning, or ML, to derive insights and make decisions. ​​​​

Section 2 - Data Strategy & Governance​​

In the Data Strategy & Governance section we will explore the competitive advantage of data – and what it means to become a data driven business. We’ll then introduce a data management framework, comprising good practice in 9 key areas of data management. We’ll then take a look at machine learning pipelines and management – from baseline, to proof of concept and into production. We’ll wrap up the section by taking a look at considerations for building and managing an analytics team.

Section 3 - State of the Art & Developments​​

In the State of the Art & Developments section we will cover alternative data, the benefits of using it and the potential sources and types available. We will then look at the current tooling and frameworks used in ML & Big Data, including new Auto ML platforms, and discuss their advantages and disadvantages. 

Section 4 - How to identify where to apply ML & Big Data​​

In How to identify where to apply ML & Big Data we explain the concepts around identifying potential applications of ML / Big Data within organizations and how to perform an assessment of the potential project impact, deliverables and challenges. ​​​

Section 5 - Trust and Ethics​​

In the trust and ethics section we start by discussing the regulatory perspective of ML and Big Data, explaining the current legal, regulation and ethical issues around it. We then explain how this impacts the way we work with ML and big data, and how to ensure that you ‘Trust’ your models. We end this section with the concepts of ownership and consent of data. ​

Section 6 - Futurology

In this section we discuss what the future has in store for us and how to be at the leading edge of the technological wave. We look at some of the most cutting-edge potential technologies and what opportunities these could present to financial services and society at large. ​

Along with the video lectures, the course is presented with a number of additional resources. For each section, a course workbook is available to download if you’d find that useful to make notes in. There are thought exercises that you can use within your organization, spreadsheet template downloads for the practical sections. And each section has an accompanying quiz which you can use to revisit the key points we’ve covered - however the quizzes are not mandatory in order to proceed with the course. 

There’s also a glossary and list of references and further reading available to download in this section.

To get help with the course, or with any technical issues or billing queries, please email us at [email protected].

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