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Data Science For Product Managers Bootcamp

Business

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.

Rating3.2
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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?

  • Demonstrate a fundamental understanding of end to end aspects of Data Science and ability to interlock with Management team and Data Scientist team.

  • Types of Data Science Models & Algorithms commonly used in the industry along with is business applications

  • Be able to work effectively with data science teams to build great products & services.

  • 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

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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