Module 1 - Basics of Big Data

• What is Big Data
• Where does Big Data come from
• What makes Big Data valuable
• Data Science vs Big Data vs Data Analytics
• What is Structured and Unstructured data?
• What’s unique about Big Data
• Leveraging Big Data
• Why is Big Data the next big thing
• What are the opportunities for Big Data
• Risks of Big Data

Module 2 - Big Data and IoT

• Understanding the relationship between big data and IoT

Module 3 - Concepts of Big Data

• Big Data systems
• Volume
• Velocity
• Variety
• Veracity
• Variability
• Value
• Data Structures
• Data Serialization
• Batch Processing
• Cluster Computing
• Data Mining
• ETL
• Data Warehousing

Module 4 - Big Data Frameworks

• Designing Algorithms
• HADOOP
• Parquet
• Sqoop
• HDFS and YARN
• Impala
• Apache Spark
• PIG
• Hive
• NoSQL

Module 5 - Big Data Platforms

• Cloudera
• Amazon Web Services
• Hortonworks
• Microsoft HDInsight
• IBM
• Teradata Enterprise Access for Hadoop
• Intel Distribution for Apache Hadoop
• Google
• Oracle
• CISCO

Module 6 - Infrastructure of Big Data

• Big Data life cycle
• Ingesting Data
• Persisting the data in storage
• Analyzing and Computing the data
• Interpreting the results
• Government approval and policy
• Mauritius Resolution on Big Data

Module 7 - Deep Learning

• What is deep learning?
• Supervised Learning
• Unsupervised Learning
• How to train and optimize basic neural networks, convolutional neural networks and long and short term memory networks
• Learning systems in TensorFlow
• Caffe
• Theano
• Torch

Module 8 - Machine Learning

• What is Machine Learning?
• Machine Learning system design
• Supervised Learning
• Unsupervised Learning
• Data sources for Supervised and Unsupervised Learning
• Anomaly Detection
• Large and Small Scale Machine Learning
• Linear Regression with One Variable
• Linear Regression with Multiple Variables

Module 9 - Neural Networks

• What are Neural Networks?
• The relationship between Neural Networks and Machine Learning
• Elements in a Neural Network
• Concepts of Deep Neural Networks
• Multiple Linear Regression and Updaters
• Adding custom layers, activation functions and loss functions to the network
• Neural Networks and Artificial Intelligence

Module 10 - Artificial Intelligence

• Introduction to Artificial Intelligence
• The importance of data for AI
• Machine Learning Algorithms
• Training an Algorithm
• Integrating data with AI
• Applications of AI

Module 11 - AI and Robotics

• Introduction to Robotics
• Building AI for Robotics
• Why we need AI in Robotics?
• Integrating AI in Robotics
• Ethics for AI powered Robots

Module 12 - Big Data Business models

• Making money with big data
• Information based differentiation
• Information based brokering
• Information based delivery networks
• Types of big data business models
• Data users- Use data to make better decisions
• Data suppliers- Supply data
• Delivery networks- Use data to provide ad delivery platforms
• Data facilitators- Sell tools to analyze big data

Module 13 - Entrepreneurship Opportunities in IoT

• Knowing your Big data business model
• Hiring and setting up the right team.
• Funding
• Infrastructure
• Webhosting
• Development and design
• Marketing

Module 14 - Big Data case studies

• Big data and the public sector
• Big data in retail
• Big data in learning
• Big data in transportation
• Big data in healthcare
• Big data in finance
• Big data in advertising
• Big data in entertainment and media
• Big data in manufacturing
• Big data in cybersecurity
• Big data in computer forensics
• Big data in government surveillance
• Big data in incident response
• Big data in scientific analysis
• Big data in astronomy
• Big data in quantum physics
• Big data in smart cities
• Big data in Industry 4.0
• Big data in autonomous vehicles
• Big data algorithms in Internet search engines
• Big data in the film industry

Module 15 - Ethics of Big data

• Privacy concerns of Big data
• How much data is too much data
• Using data to manipulate markets