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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.
Instructor:
Julia Mariasova
1,268 students enrolled
English [Auto]
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

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!

Course overview and Introduction to big data

1
Course Introduction
2
Guest Speakers
3
BEFORE YOU START
4
Why learn about big data?
5
Big data definition and Sources of data
6
Big Data Definition

Test what you have just learnt!

7
New Sources of Data

Big Data in Practice - LOGISTICS & TRANSPORTATION

1
Section introduction
2
Logistics & Transportation: Social Impact of Artificial Intelligence & IoT
3
Logistics & Transportation: Predictive & Prescriptive Maintenance
4
Logistics & Transportation: Prepositioning of Goods and Just in Time inventory
5
Logistics & Transportation: Route Optimisation
6
Logistics & Transportation: Warehouse Optimisation and order picking
7
Logistics & Transportation: The Future of the industry
8
Logistics and Transportation Quiz

Check what you have learnt!

9
Google Maps News

Big Data in Practice - PREDICTIVE MAINTENANCE IN MANUFACTURING

1
Predictive Maintenance in Manufacturing - Case Study SIBUR
2
Predictive maintenance

Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT

1
Real Estate: Introduction to big data in real estate
2
Real Estate: Business Drivers for Using Big Data
3
Real Estate & Property Management: Technological Enablers
4
Real Estate: Building Asset Management and Building Information Modelling
5
Real Estate: Big Data and IoT in Building Maintenance and Management - examples
6
Real Estate: Smart Buildings
7
Additional Resources to Lecture on Smart Buildings
8
Real Estate: Smart Cities (examples - Los Angeles and Hudson Yards in New York)
9
Additional resources on Smart Cities
10
Real Estate: Smart Technologies Cost and Government Subsidies (example - Norway)
11
Real Estate: Data Driven Future
12
Real Estate and Property Management

Let's check what you have learnt.

13
Operational Efficiencies and Sustainability

Big Data in Practice: HEALTHCARE

1
Healthcare: Data Challenges in Healthcare Industry
2
Healthcare: Transforming Role of AI and Data Measurement Technologies
3
Healthcare: Artificial Intelligence in Disease Prevention
4
Healthcare: Artificial Intelligence in Anti-Ageing
5
Healthcare: AI in Clinical Decision Making and Cancer Treatment
6
Healthcare: Clash of AI and Traditional Healthcare Science
7
Healthcare: Final Remarks - Value of Artificial Intellegence to Consumers
8
BIG DATA IN PRACTICE: SECTION WRAP-UP
9
Healthcare

Let's check what you've learnt!

10
AI in Medical Research

Data Science and Required Skillset

1
Data Science Definition and Required Skillset
2
Guest Speakers importance of working in teams & understanding business objective
3
Data Science Skillset: Section Wrap-Up
4
Handouts
5
Data Science Skills

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

6
Data Science and Business Skills

Introduction to Big Data Technologies

1
Key Technological Advances and Enablers
2
Wide Adoption of Cloud Computing
3
Data Management Technological Breakthroughs (e.g. NoSQL, Hadoop)
4
Open Source and Open APIs
5
Big Data Enablers

Let's check how attentive you were!

6
Additional Resources and Handouts
7
Big Data Technology Architecture (including examples of popular technologies)
8
Big data technology architecture
9
Additional Resources and Handouts
10
Technology Architecture

Introduction to data analysis, Artificial Intelligence and Machine Learning

1
Why to be data and tech savvy
2
Big Data Analytics and Artificial Intelligence Definitions
3
Machine Learning Workflow and Training a Model
4
Model Accuracy and Ability to Generalise
5
Machine Learning Components: DATA
6
Machine Learning Components: FEATURES
7
Machine Learning Components: ALGORITHMS
8
Additional Resources and Handouts
9
Introduction to AI quiz

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

Simplified Overview of Machine Learning Algorithms

1
Classical Machine Learning: Supervised and Unsupervised Learning
2
SUPERVISED LEARNING: Classification
3
Classification: Naive Bayes
4
Classification: Decision Trees
5
Classification: Support Vector Machines (SVM)
6
Classification: Logistic Regression
7
Classification: K Nearest Neighbour
8
Classification: Anomaly Detection
9
SUPERVISED LEARNING: Regression
10
Classical Machine Learning: Unsupervised Learning
11
UNSUPERVISED LEARNING: Clustering
12
Clustering: K-Means
13
Clustering: Mean-Shift
14
Clustering: DBSCAN
15
Clustering: Anomaly Detection
16
UNSUPERVISED LEARNING: Dimensionality Reduction
17
UNSUPERVISED LEARNING: Association Rule
18
CLASSICAL MACHINE LEARNING - Section Wrap Up
19
REINFORCEMENT LEARNING
20
ENSEMBLES
21
Machine Learning Quiz

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

Introduction to Deep Learning and Neural Networks

1
DEEP LEARNING AND NEURAL NETWORKS
2
NEURAL NETWORKS: Convolutional Neural Network
3
NEURAL NETWORKS: Recurrent Neural Network
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Certificate of Completion
Intro to Big Data, Data Science and Artificial Intelligence
Category:
Data Science, Development
Teacher
Daniel W
Price:
$55.98 $45
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