Data Science For Product Managers Bootcamp
Data Science For Product Managers Bootcamp, available at $44.99, has an average rating of 3.2, with 66 lectures, 7 quizzes, based on 43 reviews, and has 154 subscribers.
You will learn about Data Analysis to Drive Decision Making Analysis Methods – Descriptive Analysis Predictive Analysis Predictive Analysis Big Data Terminologies Data Science Algorithms and its applications K-Means Clustering Association Rules Regression Analysis K-Nearest Neighbors Decision Trees This course is ideal for individuals who are Individuals seeking a career in Product Management or Professionals aiming to transition into Product Management or Product Managers looking to enhance their skillset or Entrepreneurs wanting a comprehensive understanding of data product development It is particularly useful for Individuals seeking a career in Product Management or Professionals aiming to transition into Product Management or Product Managers looking to enhance their skillset or Entrepreneurs wanting a comprehensive understanding of data product development.
Enroll now: Data Science For Product Managers Bootcamp
Summary
Title: Data Science For Product Managers Bootcamp
Price: $44.99
Average Rating: 3.2
Number of Lectures: 66
Number of Quizzes: 7
Number of Published Lectures: 66
Number of Published Quizzes: 7
Number of Curriculum Items: 73
Number of Published Curriculum Objects: 73
Original Price: $29.99
Quality Status: approved
Status: Live
What You Will Learn
- Data Analysis to Drive Decision Making
- Analysis Methods – Descriptive Analysis
- Predictive Analysis
- Predictive Analysis
- Big Data Terminologies
- Data Science Algorithms and its applications
- K-Means Clustering
- Association Rules
- Regression Analysis
- K-Nearest Neighbors
- Decision Trees
Who Should Attend
- Individuals seeking a career in Product Management
- Professionals aiming to transition into Product Management
- Product Managers looking to enhance their skillset
- Entrepreneurs wanting a comprehensive understanding of data product development
Target Audiences
- Individuals seeking a career in Product Management
- Professionals aiming to transition into Product Management
- Product Managers looking to enhance their skillset
- Entrepreneurs wanting a comprehensive understanding of data product development
DATA SCIENCE FOR PRODUCT MANAGERS
Become Data Driven Product Manager
What will you Learn?
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Demonstrate a fundamental understanding of end to end aspects of Data Science and ability to interlock with Management team and Data Scientist team.
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Types of Data Science Models & Algorithms commonly used in the industry along with is business applications
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Be able to work effectively with data science teams to build great products & services.
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Ability to jumpstart a career as a Data Smart Manager.
Top skills you will learn
Data Analysis to Drive Decision Making, Analysis Methods – Descriptive Analysis, Predictive Analysis, Prescriptive Analysis. Big Data Terminologies, Data Science Algorithms and its applications (ex. Component Analysis, K-Means Clustering, Association Rules, Regression Analysis, K-Nearest Neighbors, Decision Trees etc.). Unsupervised Learning, Data Visualization, Data Story Telling, Data Monetization. Setting up and Managing Data Teams and more.
Ideal For
Business Professionals like Business Analysts, Project Managers, Program Managers.
Technology Professionals like – Q/A, Engineering Leads, Solutions Architect, Software Developers.
Customer Facing Professionals like – Marketing Analysts, Sales, Entrepreneurs, Delivery Managers, Functional Managers.
Anyone who wants to build and end to end understanding and orientation of the world of Data Science and how to drive Data Science projects & Data Driven business decisions.
NO prior knowledge of Data Science, Programming or Statistics required
Course Curriculum
Chapter 1: Setting the context
Lecture 1: Setting the context
Lecture 2: Learning Objectives
Lecture 3: Course Overview
Chapter 2: Chapter 1: Understanding Data Science
Lecture 1: Learning Objective
Lecture 2: 1.1: What is Data Science?
Lecture 3: 1.2: What are “Data Science enabled Products”?
Lecture 4: 1.3: Big Data Landscape
Lecture 5: 1.4: Data Science Basics & Machine Learning
Chapter 3: Chapter 2: Data Science Algorithms and Analysis Methods
Lecture 1: Learning Objective
Lecture 2: 2.1: Analysis Methods
Lecture 3: 2.2: Descriptive Analysis
Lecture 4: 2.3: Predictive Analysis & Prescriptive Analysis
Lecture 5: 2.4: Big Data Terminologies
Lecture 6: 2.5: Data Science Algorithms
Lecture 7: 2.6: Principal Component Analysis
Lecture 8: 2.7: K-Means Clustering
Lecture 9: 2.8: Association Rules
Lecture 10: 2.9: Page Rank Algorithm
Lecture 11: 2.10: Regression Analysis
Lecture 12: 2.11: K-Nearest Neighbors
Lecture 13: 2.12: Decision Trees
Chapter 4: Chapter 3: Building Products With Data Science
Lecture 1: Learning Objective
Lecture 2: 3.1: Data Science Project
Lecture 3: 3.2: Data Format
Lecture 4: 3.3: Variable Types
Lecture 5: 3.4: Variable Selection
Lecture 6: 3.5: Feature Engineering
Lecture 7: 3.6: Algorithm Selection
Lecture 8: 3.7: Parameter Tuning
Lecture 9: 3.8: Evaluating Results
Lecture 10: 3.9: Building Products
Lecture 11: 3.10: Invisible AI As The Best AI
Lecture 12: 3.11: Actionable Insights
Lecture 13: 3.12: Your Users Are Not Data Scientists
Lecture 14: 3.13: Design is AI's Best Friend
Lecture 15: 3.14: Managers Deserve Less
Lecture 16: 3.15: Don't Visualize Data
Lecture 17: 3.16: Be The QA You Want To See
Lecture 18: 3.17: Ask Your Users: Back Testing
Lecture 19: 3.18: Data Science Pitfalls
Chapter 5: Chapter 4: Engaging With Data Science Teams
Lecture 1: Learning Objective
Lecture 2: 4.1: PMs Should Engage With Data Scientists
Lecture 3: 4.2: Product Marketing & Data Science
Lecture 4: 4.3: What Is A Data Smart Product Manager?
Lecture 5: 4.4: Personas In The Data Science Arena
Chapter 6: Chapter 5: Getting Started With Essentials Of Data Science
Lecture 1: Learning Objective
Lecture 2: 5.1: Data Science Essentials
Lecture 3: 5.2: Linear Regression
Lecture 4: 5.3: Scatter Plot
Lecture 5: 5.4: Regression Equation
Lecture 6: 5.5: Regression Result
Lecture 7: 5.6: Regression & Data Science
Lecture 8: 5.7: Cluster Analysis
Chapter 7: Chapter 6: Data Strategy and Visualization
Lecture 1: Learning Objective
Lecture 2: 6.1: What Is Data Strategy?
Lecture 3: 6.2: What Are Some Examples Of Divergent Strategies?
Lecture 4: 6.3: Data Visualization
Lecture 5: 6.4: Time Series Data
Lecture 6: 6.5: Cartographic Data
Lecture 7: 6.6: Financial Chart
Lecture 8: 6.7: Interactive Visualization
Lecture 9: 6.8: Heat Maps
Lecture 10: 6.9: Visualizing Scale
Lecture 11: 6.10: Music
Lecture 12: 6.11: Five ThirtyEight Visualization
Chapter 8: Conclusion
Lecture 1: Summary
Instructors
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Institute of Product Leadership
The Premier B-School for Product Leadership
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 8 votes
- 3 stars: 7 votes
- 4 stars: 10 votes
- 5 stars: 16 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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