“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 Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 1499 ( |
| Duration | 03 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 1,482 students |
| Instructor | Julia Mariasova https://www.linkedin.com/in/juliamariasova |
| Topics Covered |
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| Course Level | N.A |
| Total Student Reviews | 745 |
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 | ||
| BEFORE YOU START | ||
| 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 | ||
| BIG DATA IN PRACTICE: SECTION WRAP-UP | ||
| Healthcare | ||
| 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 | ||
| Handouts | ||
| 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 |
| SUPERVISED LEARNING: Classification | ||
| Classification: Naive Bayes | ||
| Classification: Decision Trees | ||
| Classification: Support Vector Machines (SVM) | ||
| Classification: Logistic Regression | ||
| Classification: K Nearest Neighbour | ||
| Classification: Anomaly Detection | ||
| SUPERVISED LEARNING: Regression | ||
| Classical Machine Learning: Unsupervised Learning | ||
| UNSUPERVISED LEARNING: Clustering | ||
| Clustering: K-Means | ||
| Clustering: Mean-Shift | ||
| Clustering: DBSCAN | ||
| Clustering: Anomaly Detection | ||
| UNSUPERVISED LEARNING: Dimensionality Reduction | ||
| UNSUPERVISED LEARNING: Association Rule | ||
| CLASSICAL MACHINE LEARNING – Section Wrap Up | ||
| REINFORCEMENT LEARNING | ||
| ENSEMBLES | ||
| 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
| Parameters | Intro to Big Data, Data Science and Artificial Intelligence | Big Data for Managers | Artificial Intelligence & Machine Learning for Business |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 3.5 hours | 4 hours | 5.5 hours |
| Rating | 4.7 /5 | 4.3 /5 | 4.5 /5 |
| Student Enrollments | 1,482 | 4,640 | 10,335 |
| Instructors | Julia Mariasova | Ganapathi Devappa | Analytics Vidhya |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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