Intro to Big Data, Data Science and Artificial Intelligence
4.3 (248 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
742 students enrolled

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.
4.3 (248 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
742 students enrolled
Created by Julia Mariasova
Last updated 7/2020
English
English [Auto]
Price: $39.99
30-Day Money-Back Guarantee
This course includes
  • 3.5 hours on-demand video
  • 9 articles
  • 10 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
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
Expand all 79 lectures 03:27:05
+ Course overview and Introduction to big data
5 lectures 13:22
BEFORE YOU START
01:19
Big data definition and Sources of data
02:21

Test what you have just learnt!

Big Data Definition
12 questions
+ Big Data in Practice - LOGISTICS & TRANSPORTATION
7 lectures 13:02
Section introduction
00:15
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

Check what you have learnt!

Logistics and Transportation Quiz
4 questions
Read the article 'How Google Maps uses machine learning to predict bus traffic delays in real time'
Google Maps News
1 question
+ Big Data in Practice - PREDICTIVE MAINTENANCE IN MANUFACTURING
1 lecture 03:16
Predictive Maintenance in Manufacturing - Case Study SIBUR
03:16
Let's brainstorm about what data you might use when building a model for predictive maintenance.
Predictive maintenance
1 question
+ Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT
11 lectures 21:48
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

Let's check what you have learnt.

Real Estate and Property Management
3 questions
Let's brainstorm on the subject of how AI and smart technologies can be both efficient and sustainable
Operational Efficiencies and Sustainability
1 question
+ Big Data in Practice: HEALTHCARE
8 lectures 38:23
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

Let's check what you've learnt!

Healthcare
8 questions
Let's think how AI is changing our traditional understanding of things
AI in Medical Research
1 question
+ Data Science and Required Skillset
4 lectures 08:35
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

Let's check what you've learnt in this section.

Data Science Skills
3 questions
On importance of the business objectives and the right skillset for delivery of data science projects.
Data Science and Business Skills
2 questions
+ Introduction to Big Data Technologies
7 lectures 23:43
Wide Adoption of Cloud Computing
02:47
Data Management Technological Breakthroughs (e.g. NoSQL, Hadoop)
06:42
Open Source and Open APIs
02:23

Let's check how attentive you were!

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
What technology is used in your organisation to collect, store, process and analyse large amounts of data?
Technology Architecture
2 questions
+ Introduction to data analysis, Artificial Intelligence and Machine Learning
8 lectures 22:49
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

Let's check what you know about AI and ML.

Introduction to AI quiz
16 questions
+ Simplified Overview of Machine Learning Algorithms
20 lectures 35:53
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

Let's recap some of the things which you have learnt by doing another quiz.

Machine Learning Quiz
9 questions
+ Introduction to Deep Learning and Neural Networks
5 lectures 14:41
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
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