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30-Day Money-Back Guarantee

This course includes:

  • 3.5 hours on-demand video
  • 10 articles
  • 10 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
Development Data Science Big Data

Intro to Big Data, Data Science and Artificial Intelligence

Big Data Technology & Tools for Non-Technical Leaders. Industry expert insights on IoT, AI and Machine Learning for all.
Rating: 4.4 out of 54.4 (657 ratings)
1,167 students
Created by Julia Mariasova
Last updated 7/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Examples of Big Data and Data Science in Practice (Healthcare, Logistics & Transportation, Manufacturing, and Real Estate & Property Management industries)
  • Big Data Definition and Data Sources. Why we need to be data and technology savvy.
  • Introduction to Data Science and Skillset required for working with Big Data
  • Technological Breakthroughs which Enable Big Data Solutions (Connectivity, Cloud, Open Source, Hadoop and NoSQL)
  • Big Data Technology Architecture and most popular technology tools used for each Architecture Layer
  • Beginner's Introduction to Data Analysis, Artificial Intelligence and Machine Learning
  • Simplified Overview of Machine Learning Algorithms and Neural Networks

Course content

11 sections • 80 lectures • 3h 27m total length

  • Preview01:58
  • Preview04:21
  • BEFORE YOU START
    01:19
  • Preview03:23
  • Big data definition and Sources of data
    02:21
  • Big Data Definition
    12 questions
  • New Sources of Data
    1 question

  • Section introduction
    00:15
  • Preview02:10
  • Logistics & Transportation: Predictive & Prescriptive Maintenance
    03:02
  • Logistics & Transportation: Prepositioning of Goods and Just in Time inventory
    01:53
  • Logistics & Transportation: Route Optimisation
    02:15
  • Logistics & Transportation: Warehouse Optimisation and order picking
    01:49
  • Logistics & Transportation: The Future of the industry
    01:38
  • Logistics and Transportation Quiz
    4 questions
  • Google Maps News
    1 question

  • Predictive Maintenance in Manufacturing - Case Study SIBUR
    03:16
  • Predictive maintenance
    1 question

  • Real Estate: Introduction to big data in real estate
    01:36
  • Real Estate: Business Drivers for Using Big Data
    02:20
  • Real Estate & Property Management: Technological Enablers
    00:39
  • Real Estate: Building Asset Management and Building Information Modelling
    02:18
  • Real Estate: Big Data and IoT in Building Maintenance and Management - examples
    02:59
  • Real Estate: Smart Buildings
    01:42
  • Additional Resources to Lecture on Smart Buildings
    00:13
  • Real Estate: Smart Cities (examples - Los Angeles and Hudson Yards in New York)
    06:54
  • Additional resources on Smart Cities
    00:11
  • Real Estate: Smart Technologies Cost and Government Subsidies (example - Norway)
    02:21
  • Real Estate: Data Driven Future
    00:35
  • Real Estate and Property Management
    3 questions
  • Operational Efficiencies and Sustainability
    1 question

  • Preview04:57
  • Healthcare: Transforming Role of AI and Data Measurement Technologies
    08:07
  • Healthcare: Artificial Intelligence in Disease Prevention
    07:15
  • Healthcare: Artificial Intelligence in Anti-Ageing
    04:46
  • Healthcare: AI in Clinical Decision Making and Cancer Treatment
    02:35
  • Healthcare: Clash of AI and Traditional Healthcare Science
    03:19
  • Healthcare: Final Remarks - Value of Artificial Intellegence to Consumers
    06:18
  • BIG DATA IN PRACTICE: SECTION WRAP-UP
    01:06
  • Healthcare
    8 questions
  • AI in Medical Research
    1 question

  • Data Science Definition and Required Skillset
    03:34
  • Guest Speakers importance of working in teams & understanding business objective
    04:28
  • Data Science Skillset: Section Wrap-Up
    00:29
  • Handouts
    00:04
  • Data Science Skills
    3 questions
  • Data Science and Business Skills
    2 questions

  • Preview05:25
  • Wide Adoption of Cloud Computing
    02:47
  • Data Management Technological Breakthroughs (e.g. NoSQL, Hadoop)
    06:42
  • Open Source and Open APIs
    02:23
  • Big Data Enablers
    12 questions
  • Additional Resources and Handouts
    00:09
  • Big Data Technology Architecture (including examples of popular technologies)
    06:15
  • Big data technology architecture
    7 questions
  • Additional Resources and Handouts
    00:01
  • Technology Architecture
    2 questions

  • Preview02:25
  • Big Data Analytics and Artificial Intelligence Definitions
    04:45
  • Machine Learning Workflow and Training a Model
    04:58
  • Model Accuracy and Ability to Generalise
    01:57
  • Machine Learning Components: DATA
    06:26
  • Machine Learning Components: FEATURES
    00:45
  • Machine Learning Components: ALGORITHMS
    01:22
  • Additional Resources and Handouts
    00:11
  • Introduction to AI quiz
    16 questions

  • Preview02:44
  • SUPERVISED LEARNING: Classification
    01:49
  • Classification: Naive Bayes
    01:11
  • Classification: Decision Trees
    01:30
  • Classification: Support Vector Machines (SVM)
    01:10
  • Classification: Logistic Regression
    00:55
  • Classification: K Nearest Neighbour
    02:35
  • Classification: Anomaly Detection
    00:48
  • SUPERVISED LEARNING: Regression
    02:44
  • Classical Machine Learning: Unsupervised Learning
    00:49
  • UNSUPERVISED LEARNING: Clustering
    00:55
  • Clustering: K-Means
    01:27
  • Clustering: Mean-Shift
    01:32
  • Clustering: DBSCAN
    01:06
  • Clustering: Anomaly Detection
    00:49
  • UNSUPERVISED LEARNING: Dimensionality Reduction
    02:26
  • UNSUPERVISED LEARNING: Association Rule
    02:20
  • CLASSICAL MACHINE LEARNING - Section Wrap Up
    02:52
  • REINFORCEMENT LEARNING
    03:19
  • ENSEMBLES
    02:52
  • Machine Learning Quiz
    9 questions

  • DEEP LEARNING AND NEURAL NETWORKS
    04:40
  • NEURAL NETWORKS: Convolutional Neural Network
    03:35
  • NEURAL NETWORKS: Recurrent Neural Network
    02:46
  • NEURAL NETWORKS: Generative Adversarial Network (GAN)
    03:35
  • Additional Resources
    00:05
  • Neural Networks Quiz
    10 questions

Requirements

  • Curiosity about business and technology
  • There are no special requirements or prerequisites. Anyone can learn from this course.

Description

If you are like me - finding it difficult to read thick manuals with formulae, but still very much interested in modern technologies and their applications, then this course is for you.

You will learn about big data, Internet of Things (IoT), data science, big data technologies, artificial intelligence (AI), machine learning (ML) algorithms, neural networks, and why this could be relevant to you even if you don't have technology or data science background. Please note that this is NOT TECHNICAL TRAINING and it does NOT teach Coding/Development or Statistics.

The course includes the interviews with industry experts that cover  big data developments in Real Estate, Logistics & Transportation and Healthcare industries.  You will learn how machine learning is used to predict engine failures, how artificial intelligence is used in anti-ageing, cancer treatment and clinical diagnosis, you will find out what technology is used in managing smart buildings and smart cities including Hudson Yards in New York.  We have got fantastic guest speakers who are the experts in their areas:

- WAEL ELRIFAI - Global VP of Solution Engineering - Big Data, IoT & AI at Hitachi Vantara with over 15 years of experience in the field of machine learning and IoT. Wael is also a Co-Authour of the book "The Future of IoT".

- ED GODBER - Healthcare Strategist with over 20 years of experience in Healthcare, Pharmaceuticals and start-ups specialising in Artificial Intelligence.

- YULIA PAK - Real Estate and Portfolio Strategy Consultant with over 12 years of experience in Commercial Real Estate advisory, currently working with clients who deploy IoT technologies to improve management of their real estate portfolio.

Hope you will enjoy the course and let me know  in the comments of each section how I can improve the course!

Who this course is for:

  • Non-technical leaders and managers
  • Anyone who is interested in big data, machine learning and artificial intelligence
  • Professionals considering career switch
  • People with technical background who want to gain insights in real life applications of data science skills
  • Anyone who works with coders, data engineers and data scientists and wants to learn basics about big data technology and tools
  • People without maths or computer science background, but who want to understand how Machine Learning algorithms work

Featured review

Awaluddin Berwanto
Awaluddin Berwanto
8 courses
8 reviews
Rating: 5.0 out of 57 months ago
I have learnt about Big Data and its 3-4 Vs variable, how to manage the big data as well as the application in various services and industries, such as logistic, property and health care. There is also sufficient information on Data Science, required skill sets for implement the data science and also brief of machine learning algorithms. At the end, to me it is very good course.

Instructor

Julia Mariasova
Management Consultant / Media Producer
Julia Mariasova
  • 4.4 Instructor Rating
  • 657 Reviews
  • 1,167 Students
  • 1 Course

An enthusiastic management consultant, project, programme and change manager, media producer/director with 20 years of experience in financial services industry (operations and consulting) and 5 years of experience in media/video production industry  (educational content and corporate communications).

Passionate about change, strategic development, operational transformation and learning new things.  I have experience of working in both large corporate and start-up environments.

Masters in Banking and Finance. Qualified Management Accountant (CIMA). Certified in Filmmaking (London Film Academy).

I design my own training courses, and offer production services to other instructors/lecturers or organisations.

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