Intro to Big Data, Data Science and Artificial Intelligence

Intro to Big Data, Data Science and Artificial Intelligence, available at $29.99, has an average rating of 4.28, with 80 lectures, 10 quizzes, based on 862 reviews, and has 2777 subscribers.

You will learn about 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 Beginners Introduction to Data Analysis, Artificial Intelligence and Machine Learning Simplified Overview of Machine Learning Algorithms and Neural Networks This course is ideal for individuals who are Non-technical leaders and managers or Anyone who is interested in big data, machine learning and artificial intelligence or Professionals considering career switch or People with technical background who want to gain insights in real life applications of data science skills or Anyone who works with coders, data engineers and data scientists and wants to learn basics about big data technology and tools or People without maths or computer science background, but who want to understand how Machine Learning algorithms work It is particularly useful for Non-technical leaders and managers or Anyone who is interested in big data, machine learning and artificial intelligence or Professionals considering career switch or People with technical background who want to gain insights in real life applications of data science skills or Anyone who works with coders, data engineers and data scientists and wants to learn basics about big data technology and tools or People without maths or computer science background, but who want to understand how Machine Learning algorithms work.

Enroll now: Intro to Big Data, Data Science and Artificial Intelligence

Summary

Title: Intro to Big Data, Data Science and Artificial Intelligence

Price: $29.99

Average Rating: 4.28

Number of Lectures: 80

Number of Quizzes: 10

Number of Published Lectures: 80

Number of Published Quizzes: 10

Number of Curriculum Items: 98

Number of Published Curriculum Objects: 98

Original Price: ￡34.99

Quality Status: approved

Status: Live

What You Will 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

Beginners Introduction to Data Analysis, Artificial Intelligence and Machine Learning

Simplified Overview of Machine Learning Algorithms and Neural Networks

Who Should Attend

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

Target Audiences

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

This course is designed for anyone who is new to big data projects, and would like to get better understanding what machine learning and artificial intelligence mean in practice. It is not a technical course, it does not involve coding, but it will make you feel confident when working in teams with data scientists and programmers. It will bring you up to speed with the data science, ML and AI terminology. 

The course is also designed for people who are generally interested in modern technologies and their applications – we have included case studies covering oil&gas predictive maintenance, use of AI in healthcare, application of sensor and other digital technologies  in buildings and construction, the role of machine learning in transport and logistics and many more.

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, but it is suitable for technical professionals.  I am proud to say that this course was purchased by a large oil&gas company in Asia to educate their field engineers about machine learning as part of their digitalisation strategy.

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!  Please follow me on social media (Shortlisted Productions) – you can find the links on my profile page– just click on my name at the bottom of the page just before the reviews.  And please check out my other courses on Climate Change.

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

Instructors

Julia Mariasova
Management Consultant / Media Producer

Rating Distribution

1 stars: 6 votes

2 stars: 13 votes

3 stars: 137 votes

4 stars: 359 votes

5 stars: 347 votes

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