“Intro to Big Data, Data Science and Artificial Intelligence” course is for students who have trouble reading lengthy manuals with formulae but are nevertheless highly interested in contemporary technology and its uses. Students will gain knowledge of big data, the Internet of Things (IoT), data science, big data technologies, artificial intelligence (AI), machine learning (ML) algorithms, and neural networks. They will also discover why these topics may be relevant to them even if they lack technical or data science background.

The course contains interviews with business leaders who discuss big data trends in the real estate, logistics, and healthcare sectors. Students will discover what technology is utilized in controlling smart buildings and smart cities, like Hudson Yards in New York, and how machine learning is used to detect engine breakdowns, as well as how artificial intelligence is employed in anti-aging, cancer therapy, and clinical diagnosis. Currently, udemy is offering the Intro to Big Data, Data Science, and Artificial Intelligence course for up to 0 % off i.e. INR 1499 (INR 1,499).  (18.3 USD)

Who Can Opt for this Course?

  • Managers and leaders who are not technical
  • Anyone with an interest in artificial intelligence, machine learning, or big data
  • Professionals thinking about changing careers
  • People with technical backgrounds who want to learn more about how data science abilities are used in everyday life
  • Anyone who wants to master the fundamentals of big data technology and tools and works with coders, data engineers, and data scientists People interested in learning how machine learning algorithms operate but lacking a background in mathematics or computer science

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 1499 (INR 1,4990 % off
Duration03 Hours
Student Enrollment1,482 students
InstructorJulia Mariasova https://www.linkedin.com/in/juliamariasova
Topics Covered
  • Why learn about big data?
  • Big data definition and Sources of data
  • Logistics & Transportation: The Future of the industry
Course LevelN.A
Total Student Reviews745

Learning Outcomes

  • Practice Examples of Big Data and Data Science (Healthcare, Logistics & Transportation, Manufacturing, and Real Estate & Property Management industries)
  • Definition and sources of big data
  • Why it’s important for us to be tech and data smart
  • A description of data science and the knowledge and abilities needed for working with big data
  • Technological Advances that Support Big Data Solutions (Connectivity, Cloud, Open Source, Hadoop, and NoSQL)
  • Big Data Technology Architecture and the most widely utilized technological instruments for each layer of the architecture An introduction for beginners to machine learning, artificial intelligence, and data analysis
  • Overview of Neural Networks and Machine Learning Methods in Simplified Form

Course Content

S.No.Module (Duration)Topics
1.Course overview and Introduction to big data (13 minutes)Course Introduction
Guest Speakers
Why learn about big data?
Big data definition and Sources of data
Big Data Definition
New Sources of Data
2.Big Data in Practice – LOGISTICS & TRANSPORTATION (13 minutes)Section Introduction
Logistics & Transportation: Social Impact of Artificial Intelligence & IoT
Logistics & Transportation: Predictive & Prescriptive Maintenance
Logistics & Transportation: Prepositioning of Goods and Just in Time inventory
Logistics & Transportation: Route Optimisation
Logistics & Transportation: Warehouse Optimisation and order picking
Logistics & Transportation: The Future of the industry
Logistics and Transportation Quiz
Google Maps News
3.Big Data in Practice – PREDICTIVE MAINTENANCE IN MANUFACTURING (03 minutes)Predictive Maintenance in Manufacturing – Case Study SIBUR
Predictive maintenance
4.Big Data in Practice: REAL ESTATE & PROPERTY MANAGEMENT (21 minutes)Real Estate: Introduction to big data in real estate
Real Estate: Business Drivers for Using Big Data
Real Estate & Property Management: Technological Enablers
Real Estate: Building Asset Management and Building Information Modelling
Real Estate: Big Data and IoT in Building Maintenance and Management – examples
Real Estate: Smart Buildings
Additional Resources to Lecture on Smart Buildings
Real Estate: Smart Cities (examples – Los Angeles and Hudson Yards in New York)
Additional resources on Smart Cities
Real Estate: Smart Technologies Cost and Government Subsidies (example – Norway)
Real Estate: Data-Driven Future
Real Estate and Property Management
Operational Efficiencies and Sustainability
5.Big Data in Practice: HEALTHCARE (38 minutes)Healthcare: Data Challenges in Healthcare Industry
Healthcare: Transforming Role of AI and Data Measurement Technologies
Healthcare: Artificial Intelligence in Disease Prevention
Healthcare: Artificial Intelligence in Anti-Ageing
Healthcare: AI in Clinical Decision-Making and Cancer Treatment
Healthcare: Clash of AI and Traditional Healthcare Science
Healthcare: Final Remarks – Value of Artificial Intelligence to Consumers
AI in Medical Research
6.Data Science and Required Skillset (08 minutes)Data Science Definition and Required Skillset
Guest Speaker’s importance of working in teams & understanding business objective
Data Science Skillset: Section Wrap-Up
Data Science Skills
Data Science and Business Skills
7.Introduction to Big Data Technologies (23 minutes)Key Technological Advances and Enablers
Wide Adoption of Cloud Computing
Data Management Technological Breakthroughs (e.g. NoSQL, Hadoop)
Open Source and Open APIs
Big Data Enablers
Additional Resources and Handouts
Big Data Technology Architecture (including examples of popular technologies)
Big data technology architecture
Additional Resources and Handouts
Technology Architecture
8.Introduction to data analysis, Artificial Intelligence and Machine Learning (22 minutes)Why be data and tech-savvy
Big Data Analytics and Artificial Intelligence Definitions
Machine Learning Workflow and Training a Model
Model Accuracy and Ability to Generalise
Machine Learning Components: DATA
Machine Learning Components: FEATURES
Machine Learning Components: ALGORITHMS
Additional Resources and Handouts
Introduction to AI quiz
9.Simplified Overview of Machine Learning Algorithms (35 minutes)Classical Machine Learning: Supervised and Unsupervised Learning
Classification: Naive Bayes
Classification: Decision Trees
Classification: Support Vector Machines (SVM)
Classification: Logistic Regression
Classification: K Nearest Neighbour
Classification: Anomaly Detection
Classical Machine Learning: Unsupervised Learning
Clustering: K-Means
Clustering: Mean-Shift
Clustering: DBSCAN
Clustering: Anomaly Detection
UNSUPERVISED LEARNING: Dimensionality Reduction
Machine Learning Quiz
10.Introduction to Deep Learning and Neural Networks (14 minutes)DEEP LEARNING AND NEURAL NETWORKS
NEURAL NETWORKS: Convolutional Neural Network
NEURAL NETWORKS: Recurrent Neural Network
NEURAL NETWORKS: Generative Adversarial Network (GAN)
Additional Resources
Neural Networks Quiz
11.Machine Learning Sections Wrap-up (12 minutes)Machine Learning Algorithms Use Cases
Choosing AI algorithms
Additional Resources and Handouts
Course Wrap up
Your feedback and more resources

Resources Required

  • Interest in commerce and technology
  • There aren’t any unique specifications or prerequisites
  • The course is open to everyone

Featured Review

Anirudh Saraswat (4/5) : A good insight into the introduction of topics covered around data analytics and AI.

Pros of course

  • Nienke Stuut (4/5) : Great content for beginners like myself and lots of examples of how certain things work in practice, provided by excellent guest speakers.
  • Supun Malintha Sandanayaka (5/5) : Really really good course for beginners! Covers so much information in an attractive way and which is presented in a way that even non-statistic / non-AI people can understand.
  • Elisabeth Fruehwirth (5/5) : Really really good! Covers so much information which is presented in a way that even non-statistic / non-AI people can understand.
  • Olga Isakova (5/5) : Gives you a very precise idea how businesses are using the data.

Cons of course

  • Arif A.  (3/5) : Provide good basic to intermediate information. I wish for more graphical / montage to go along with oral presentations to really illustrate applications.
  • Fadzrul Izwan Muhd A.(3/5) : Course is informative, maybe a bit too informative for non- Data Scientists. Will benefit from using a lot more visual aid to complement the chatty delivery.
  • Hasnisham Mat H. (3/5) : Improve knowledge about data science, internet of things, artificial intelligence and machine data. Deep learning is very good to know all the knowledge. Recommend to share the presentation pack for future note and references.
  • Adam B A R. (3/5) : Shed some light on what the subject is related to in properties, oil & gas, healthcare. To explore ideas to expand the opportunity related to operations stability and efficiency.

About the Author

The instructor of this course is Julia Mariasova who is a Management Consultant/Media Producer. With a 4.7 Instructor Rating and 809 Reviews on Udemy, he/she offers 8 Courses and has taught 2,171 Students so far.

  • The subjects of climate change, decarbonization, energy transition, digital technologies, data science, and machine learning are particularly interesting to me
  • In his opinion, even if you are not involved in the technology or climate change industries, you still need to be educated on these vital subjects so that you can adapt to changing circumstances and make a positive impact on society and the environment
  • As a result, Julia Mariasova creates his own training programs, and programs for business clients, and provide production services to other lecturers, instructors, or organizations
  • Professionally, Julia Mariasova is a management consultant with 20 years of experience in the financial services industry (operations and consulting) and 10 years in the media/video production industry
  • Julia Mariasova is also a project, program, and change manager (educational content and corporate communications)
  • Change, strategic development, operational transformation, and learning new things are all things that Julia Mariasova is enthusiastic about
  • Julia Mariasova has worked at both huge corporations and start-ups, and he also had my own firm

Comparison Table

ParametersIntro to Big Data, Data Science and Artificial IntelligenceBig Data for ManagersArtificial Intelligence & Machine Learning for Business
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration3.5 hours4 hours5.5 hours
Rating4.7 /54.3 /54.5 /5
Student Enrollments1,4824,64010,335
InstructorsJulia MariasovaGanapathi DevappaAnalytics Vidhya
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