Intro to Big Data, Data Science and Artificial Intelligence

Course Curriculum

Chapter 1: Course overview and Introduction to big data

Lecture 1: Course Introduction

Lecture 2: Guest Speakers

Lecture 3: BEFORE YOU START

Lecture 4: Why learn about big data?

Lecture 5: Big data definition and Sources of data

Chapter 2: Big Data in Practice – LOGISTICS & TRANSPORTATION

Lecture 1: Section introduction

Lecture 2: Logistics & Transportation: Social Impact of Artificial Intelligence & IoT

Lecture 3: Logistics & Transportation: Predictive & Prescriptive Maintenance

Lecture 4: Logistics & Transportation: Prepositioning of Goods and Just in Time inventory

Lecture 5: Logistics & Transportation: Route Optimisation

Lecture 6: Logistics & Transportation: Warehouse Optimisation and order picking

Lecture 7: Logistics & Transportation: The Future of the industry

Chapter 3: Big Data in Practice – PREDICTIVE MAINTENANCE IN MANUFACTURING

Lecture 1: Predictive Maintenance in Manufacturing – Case Study SIBUR

Chapter 4: Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT

Lecture 1: Real Estate: Introduction to big data in real estate

Lecture 2: Real Estate: Business Drivers for Using Big Data

Lecture 3: Real Estate & Property Management: Technological Enablers

Lecture 4: Real Estate: Building Asset Management and Building Information Modelling

Lecture 5: Real Estate: Big Data and IoT in Building Maintenance and Management – examples

Lecture 6: Real Estate: Smart Buildings

Lecture 7: Additional Resources to Lecture on Smart Buildings

Lecture 8: Real Estate: Smart Cities (examples – Los Angeles and Hudson Yards in New York)

Lecture 9: Additional resources on Smart Cities

Lecture 10: Real Estate: Smart Technologies Cost and Government Subsidies (example – Norway)

Lecture 11: Real Estate: Data Driven Future

Chapter 5: Big Data in Practice: HEALTHCARE

Lecture 1: Healthcare: Data Challenges in Healthcare Industry

Lecture 2: Healthcare: Transforming Role of AI and Data Measurement Technologies

Lecture 3: Healthcare: Artificial Intelligence in Disease Prevention

Lecture 4: Healthcare: Artificial Intelligence in Anti-Ageing

Lecture 5: Healthcare: AI in Clinical Decision Making and Cancer Treatment

Lecture 6: Healthcare: Clash of AI and Traditional Healthcare Science

Lecture 7: Healthcare: Final Remarks – Value of Artificial Intellegence to Consumers

Lecture 8: BIG DATA IN PRACTICE: SECTION WRAP-UP

Chapter 6: Data Science and Required Skillset

Lecture 1: Data Science Definition and Required Skillset

Lecture 2: Guest Speakers importance of working in teams & understanding business objective

Lecture 3: Data Science Skillset: Section Wrap-Up

Lecture 4: Handouts

Chapter 7: Introduction to Big Data Technologies

Lecture 1: Key Technological Advances and Enablers

Lecture 2: Wide Adoption of Cloud Computing

Lecture 3: Data Management Technological Breakthroughs (e.g. NoSQL, Hadoop)

Lecture 4: Open Source and Open APIs

Lecture 5: Additional Resources and Handouts

Lecture 6: Big Data Technology Architecture (including examples of popular technologies)

Lecture 7: Additional Resources and Handouts

Chapter 8: Introduction to data analysis, Artificial Intelligence and Machine Learning

Lecture 1: Why to be data and tech savvy

Lecture 2: Big Data Analytics and Artificial Intelligence Definitions

Lecture 3: Machine Learning Workflow and Training a Model

Lecture 4: Model Accuracy and Ability to Generalise

Lecture 5: Machine Learning Components: DATA

Lecture 6: Machine Learning Components: FEATURES

Lecture 7: Machine Learning Components: ALGORITHMS

Lecture 8: Additional Resources and Handouts

Chapter 9: Simplified Overview of Machine Learning Algorithms

Lecture 1: Classical Machine Learning: Supervised and Unsupervised Learning

Lecture 2: SUPERVISED LEARNING: Classification

Lecture 3: Classification: Naive Bayes

Lecture 4: Classification: Decision Trees

Lecture 5: Classification: Support Vector Machines (SVM)

Lecture 6: Classification: Logistic Regression

Lecture 7: Classification: K Nearest Neighbour

Lecture 8: Classification: Anomaly Detection

Lecture 9: SUPERVISED LEARNING: Regression

Lecture 10: Classical Machine Learning: Unsupervised Learning

Lecture 11: UNSUPERVISED LEARNING: Clustering

Lecture 12: Clustering: K-Means

Lecture 13: Clustering: Mean-Shift

Lecture 14: Clustering: DBSCAN

Lecture 15: Clustering: Anomaly Detection

Lecture 16: UNSUPERVISED LEARNING: Dimensionality Reduction

Lecture 17: UNSUPERVISED LEARNING: Association Rule

Lecture 18: CLASSICAL MACHINE LEARNING – Section Wrap Up

Lecture 19: REINFORCEMENT LEARNING

Lecture 20: ENSEMBLES

Chapter 10: Introduction to Deep Learning and Neural Networks

Lecture 1: DEEP LEARNING AND NEURAL NETWORKS

Lecture 2: NEURAL NETWORKS: Convolutional Neural Network

Lecture 3: NEURAL NETWORKS: Recurrent Neural Network