Learn to make the best use of your data by knowing how to gain valuable insights and visually communicating the results.

The most comprehensive and easy-to-understand introduction to Data Science, led by a data sciene expert. Supplemented by quizzes, resources and a course completion certificate.

What will you learn?

  • Demonstrate a fundamental understanding of Data Science and it’s interlock with product management
  • Learn about some common terminology and tools used in data science
  • Be able to work effectively with data science teams to build great products
  • Ability to jumpstart a career as a Data Smart Manager

Pre-requisites

No pre-requisites or prior experience is mandatory, although experience with working in product or data science companies is recommended.

Description

Today, a lot of companies are opening their doors to data science to unlock its power, thereby increasing the value of a data scientist who is skilled to play around with the data at hand. Data science, without doubt adds value to business by the addition of statistics and insights across workflow. Data science can also add value across all industries. This e-learning course by the Institute of Product Leadership is a foundational course that gives you an insight into the basics of data science. Starting from the very basics of “What is data science?”, the course curriculum introduces students to the concepts of data science, building product with data science at its core and the skills required to be a data smart product manager.

Your Instructor

Prof. Mandar Parikh

15+ years of product management leadership, with a proven track record in new product development, strategic planning, go-to-market, and team building with a focus on Enterprise Cloud, Data Science and Mobile.

Immensely passionate about creating lasting products, bringing them to market and driving customer success. Held product leadership roles in Fortune 500 to pre-IPO startups, crafting and evangelizing the product vision, defining the product roadmap and guiding product direction, creating product positioning & messaging platforms and leading product development and adoption.

Specialties: Enterprise Software, Mobile, B2B, Cloud Computing, SaaS, Software Product Management, Design, Strategy, Product Marketing, Lean Startups, Agile, Product planning, Competitive positioning, Packaging & Pricing



Course curriculum

  • 1

    Setting the context

  • 2

    Chapter 1: Understanding Data Science

    • Learning Objective
    • 1.1: What is Data Science?
    • 1.2: What are “Data Science enabled Products”?
    • 1.3: Big Data Landscape
    • 1.4: Data Science Basics & Machine Learning
    • Chapter Quiz
  • 3

    Chapter 2: Data Science Algorithms and Analysis Methods

    • Learning Objective
    • 2.1: Analysis Methods
    • 2.2: Descriptive Analysis
    • 2.3: Predictive Analysis & Prescriptive Analysis
    • 2.4: Big Data Terminologies
    • 2.5: Data Science Algorithms
    • 2.6: Principal Component Analysis
    • 2.7: K-Means Clustering
    • 2.8: Association Rules
    • 2.9: Page Rank Algorithm
    • 2.10: Regression Analysis
    • 2.11: K-Nearest Neighbors
    • 2.12: Decision Trees
    • Unsupervised Learning
  • 4

    Chapter 3: Building Products With Data Science

    • Learning Objective
    • 3.1: Data Science Project
    • 3.2: Data Format
    • 3.3: Variable Types
    • 3.4: Variable Selection
    • 3.5: Feature Engineering
    • 3.6: Algorithm Selection
    • 3.7: Parameter Tuning
    • 3.8: Evaluating Results
    • 3.9: Building Products
    • 3.10: Invisible AI As The Best AI
    • 3.11: Actionable Insights
    • 3.12: Your Users Are Not Data Scientists
    • 3.13: Design is AI's Best Friend
    • 3.14: Managers Deserve Less
    • 3.15: Don't Visualize Data
    • 3.16: Be The QA You Want To See
    • 3.17: Ask Your Users: Back Testing
    • 3.18: Data Science Pitfalls
    • Chapter Quiz
  • 5

    Chapter 4: Engaging With Data Science Teams

    • Learning Objective
    • 4.1: PMs Should Engage With Data Scientists
    • 4.2: Product Marketing & Data Science
    • 4.3: What Is A Data Smart Product Manager?
    • 4.4: Personas In The Data Science Arena
    • Chapter Quiz
  • 6

    Chapter 5: Getting Started With Essentials Of Data Science

    • Learning Objective
    • 5.1: Data Science Essentials
    • 5.2: Linear Regression
    • 5.3: Scatter Plot
    • 5.4: Regression Equation
    • 5.5: Regression Result
    • 5.6: Regression & Data Science
    • 5.7: Cluster Analysis
    • Chapter Quiz
  • 7

    Chapter 6: Data Strategy and Visualization

    • Learning Objective
    • 6.1: What Is Data Strategy?
    • 6.2: What Are Some Examples Of Divergent Strategies?
    • 6.3: Data Visualization
    • 6.4: Time Series Data
    • 6.5: Cartographic Data
    • 6.6: Financial Chart
    • 6.7: Interactive Visualization
    • 6.8: Heat Maps
    • 6.9: Visualizing Scale
    • 6.10: Music
    • 6.11: Five ThirtyEight Visualization
    • Chapter Quiz
  • 8

    Conclusion

    • Summary
    • Final Exam

FAQ

  • When does the course start and finish?

    The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.

  • How long do I have access to the course?

    How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.

  • What if I am unhappy with the course?

    We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 7 days and we will give you a full refund.

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