The Product Management for AI & Data Science Course is designed for aspiring Product Managers who want to learn AI and Data Science. The course is ideal for beginners as it begins with the fundamentals of Product Management for AI and data.
The instructor of the course, Danielle Thé, is a Senior Product Manager and has years of experience working in the digital sector as a Product Manager for companies like Google and Deloitte Digital. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get The Product Management for AI & Data Science Course for INR 449.
Who all can opt for this course?
- If you want to learn more about the topic of AI and Data Science, or if you want to become a Product Manager, you should enroll in this course.
- If you desire a successful job, you should take this course.
- The training is excellent for novices as well because it starts with the basics and gradually develops your skills.
|Registration Link||Apply Now!|
|Price||INR 449 (|
|Student Enrollment||13,408 students|
|Instructor||365 Careers https://www.linkedin.com/in/365careers|
|Topics Covered||Product Management for AI and Data, Business Strategy for AI and Data, User Experience, Data Management|
|Total Student Reviews||2,477|
- This course offers comprehensive insight for a product manager interested in the fields of Data Science and AI.
- Learn how to be the bridge between business needs and technically oriented Data Science and AI personnel.
- Find out what a Product Manager does and what makes a Project Manager different from a Product Manager.
- Understand the difference between Data Analysis and Data Science
- Know the difference between an AI and an algorithm
- Differentiate the various kinds of Machine Learning
- Plan and execute a business strategy for AI and Data
- Conduct a SWOT analysis
- Study the creation and testing of hypotheses
- Acquire user experience for AI and data science skills
- Source data for your projects and be aware of the management requirements
- Investigate an AI or data science project’s whole lifespan within a corporation
- Learn how to manage teams in Data Science and AI
- Address bias, privacy, and ethics
|1.||Intro to Product Management for AI & Data (22 minutes)||Introduction|
|Growing Importance of an AI & Data PM|
|The Role of a Product Manager|
|Differentiation of a PM in AI & Data|
|Product Management vs. Project Management|
|2.||Key Technological Concepts for AI & Data (34 minutes)||A Product Manager as an Analytics Translator|
|Data Analysis vs. Data Science|
|A Traditional Algorithm vs. AI|
|AI vs Traditional Algorithm|
|Explaining Machine Learning|
|Explaining Deep Learning|
|When to use Machine Learning vs. Deep Learning|
|Machine Learning or Deep Learning|
|Supervised, Unsupervised, & Reinforcement Learning|
|Supervised Learning, Unsupervised Learning or Reinforcement Learning|
|3.||Business Strategy for AI & Data (24 minutes)||AI Business Model Innovations|
|When to Use AI|
|Building a Hypothesis|
|Testing a Hypothesis|
|AI Business Canvas|
|Dr.DermaApp Case Study|
|4.||User Experience for AI & Data (21 minutes)||User Experience for Data & AI|
|Getting to the Core Problem|
|User Research Methods|
|Developing User Personas|
|Prototyping with AI|
|5.||Data Management for AI & Data (30 minutes)||Data Growth Strategy|
|Crowdsourcing Labeled Data|
|New Feature Data|
|Acquisition/Purchase Data Collection|
|Data Collection Needs Matching|
|Databases, Data Warehouses, & Data Lakes|
|6.||Product Development for AI & Data (27 minutes)||AI Flywheel Effect|
|Top & Bottom Problem Solving|
|Product Ideation Techniques|
|Complexity vs. Benefit Prioritization|
|MVPs & MVDs (Minimum Viable Data)|
|Agile & Data Kanban|
|7.||Building The Model (26 minutes)||Who Should Buid Your Model|
|Machine Learning as a Service (MLaaS)|
|In-House AI & The Machine Learning Lifecycle|
|Timelines & Diminishing Returns|
|Setting a Model Performance Metric|
|8.||Evaluating Performance (21 minutes)||Dividing Test Data|
|The Confusion Matrix|
|Precision, Recall & F1 Score|
|Optimizing for Experience|
|AutoBikerz Case Study|
|9.||Deployment & Continuous Improvement (20 minutes)||Model Deployment Methods|
|Selecting a Feedback Metric|
|User Feedback Loops|
|10.||Managing Data Science & AI Teams (21 minutes)||AI Hierarchy of Needs|
|AI Within an Organization|
|Roles in AI & Data Teams|
|Managing Team Workflow|
|Dual & Triple-Track Agile|
|11.||Communication (23 minutes)||Internal Stakeholder Management|
|Setting Data Expectations|
|Active Listening & Communication|
|Compelling Presentations with Storytelling|
|Running Effective Meetings|
|12.||Ethics, Privacy, & Bias (18 minutes)||AI User Concerns|
|Bad Actors & Security|
|AI Amplifying Human Bias|
|Data Laws & Regulations|
No prior experience or knowledge is required. The instructor begins with the basics of Product Management.
Neethi Ganesh Palanisamy (5/5): The presenter is able to connect with the listener with broad industry knowledge. Jack of All Trades, Master of None is a very good analogy use to describe a Product Manager!!!
- Madhusudhan Pamula (5/5): This was the best course I have taken for Product Management.
- Dinesh Vuppalapu (5/5): Best course for someone who would like to build their career in the field of AI/ML Product Manager
- Mohammed A Raheem (5/5): This best course foundation of data science and communication among team members
- Harry Shea (5/5): The explanation of AI concepts in this course are the best that I’ve come across online.
- Chandan Kumar G. (2.5/5): A more pictorial representation would be more efficient.
- Scott C. (2.5/5): There are a lot of typos, and not that much material that go beyond just watching the instructor talk. As a data scientist, there are also a lot of statements that I disagree with and I worry could be confusing product managers in their work. That said, I do appreciate the distinctions that were made between typical product management and PM for data science.
- Mal P. (1.5/5): on mobile every time I go from one lesson to the next (or sometimes in the middle of a lesson) it will say that there was an error. I have to back out of it, then click back into it. I’m at lesson 38 now. I’ve probably done this 20 times.
- Bharghvi P. (1/5): It is very monotonous and unclear. Not interesting at all.
About the Author
The instructor of this course is 365 Careers who are creating opportunities for Data Science and Finance students. With a 4.6 instructor rating and 6,18,572 reviews on Udemy, they offer 91 courses and have taught 2,143,657 students so far.
- On Udemy, 365 Careers is the top-selling provider of courses in Business, Finance, and Data Science.
- In 210 different countries, more than 2,000,000 students have taken the company’s courses.
- People who have finished 365 Careers training now work at renowned companies like Apple, PayPal, and Citibank.
- On Udemy right now, 365 focuses on the following subjects: Finance, Data Science, Office Productivity, Business, and Blockchain.
- The courses offered by 365 Careers are the ideal place to start whether you want to work as a Financial Analyst, Data Scientist, Business Analyst, Data Analyst, Business Intelligence Analyst, Business Executive, Finance Manager, FP&A Analyst, Investment Banker, or Entrepreneur.
|Parameters||The Product Management for AI & Data Science Course 2023||Advanced Product Management: Vision, Strategy & Metrics||Advanced Product Management: Leadership & Communication|
|Offers||INR 455 (||INR 455 (||INR 455 (|
|Duration||5 hours||5.5 hours||4.5 hours|
|Rating||4.6 /5||4.5 /5||4.5 /5|
|Instructors||365 Careers||Cole Mercer||Cole Mercer|
|Register Here||Apply Now!||Apply Now!||Apply Now!|