Evite Machine Learning Data Application

Gain critical data analytics skills to harness machine learning

Instructor-led - Data Analytics Application
What's Included?
  • Jupyter Notebook for analysis. Includes millions of rows of Evite data and provides learner tutorials in running ML algorithms
  • ARC game for learner immersion. Where business problems are delivered and strategic decision-making skills are flexed
Skills you'll learn
  • Data Analytics
  • Machine Learning Algorithms: Linear regression and Random Forest
  • Strategic Decision Making
Time commitments
Experience duration:
4-6 hours
Full details below

Human intelligence meets machine intelligence

Machine Learning (ML) has transformed the art and science of business. Its algorithms provide a powerful analytics capability that organizations across every sector are keen to harness. But be warned. ML is not a magical crystal ball that automatically produces simple, clear-cut answers to questions about the future. To be successful, it requires skilled handlers who understand the right algorithms for the job, while being clear about its limitations. Shortcomings may lie in the underlying data, the particular algorithm used, or how well we can explain the very predictions we want ML to deliver. There is an inherent tension between explainability and predictability; more explainable techniques are usually less accurate. But more accurate techniques are far less transparent, and therein lies the tradeoff and the critical question: how comfortable do you feel about relying on the predictions of ML when the results are difficult to explain?

The Evite Machine Learning Data Analytics Application exposes learners to all these areas of ML, from the technical aspects of data wrangling and running ML algorithms to analyzing the outcomes and bringing the insights back into the business decision-making process.

Available Fall, 2021

Evite Machine Learning

Crack open the black box of machine learning

Learners play the role of a recently hired data scientist at syM-Mtry, a data analytics consulting firm vying for a multi-million-dollar contract with Evite. As the appointed data scientist on this project, they play a critical role in supporting syM-Mtry's efforts to win this business. First, they'll need to demonstrate that they can deliver AI-driven solutions that will grow Evite's business. And if they secure the contract, they'll then need to deliver on their promise to Evite to leverage machine learning at scale to make consequential recommendations and decisions.

This experience has two tightly integrated modalities to support the intended business and technical skills:

The world of syM-Mtry comes to life in an Alternate Reality Courseware (ARC) gaming experienceA set of accompanying Jupyter notebooks, with the ML code and millions of rows of real Evite customer data.

ARC delivers the business problems the students must solve, promoting strategic decision-making skills rooted in a finely tuned analytical mindset. As the ARC narrative progresses, the learner's Jupyter notebook unveils deepening complexity, delivering the case related data, analytical tools (Python and AI/ML algorithms), and varying levels of code to solve the business problem.

Learners will have access to a core set of Evite customer data, including events representative of seasonal events, year-round events, and date-specific events. They will also have data for the hosts, all guests who were invited to the events, and every other event they hosted or attended.

As learners make decisions during the exercise, they will receive personalized feedback tailored to their performance and skill level; students who need help will receive additional support and advanced students will receive more complex challenges to solve. The data application includes interactive and instructional videos by the case author Raghu Iyengar, and other experts in the field.

This data application has three approaches instructors can select from to match the desired coding complexity to the technical capabilities of their learner audience. In all three options, learners will gain access to Jupyter and will be exposed to the analytical tools:

Strategy: for learners who have little to no coding or first-hand data analytics experience, but are keen to gain an understanding of how ML works. This approach is suitable for non-computer science classes, executive & manager audiences.Hybrid: for learners who have moderate coding skills, and may or may not have first-hand data analytics experience. This approach is suitable for learners who are looking to take their existing technical skills to the next level.Complex: for learners who are experienced Python coders, and may or may not have first-hand data analytics experience. This approach is suitable for computer science classes, and adult learners who are in the technical field.

A set of interfaces is provided for instructors to see the real-time progress of their learners and facilitate the final debrief that drives home the lessons of this experience in an engaging and impactful way.

Wharton Data Analytics Applications

Developed in partnership with Analytics at Wharton, Wharton Data Analytics Applications (WDAAs) are a new breed of interactive learning experiences that combine business analytics and technical case studies to teach data science and analytics in classrooms around the world. In the case of this data application, millions of rows of real-world data from Evite is provided to every learner in their own, self contained environment, as well as the code to teach business concepts through the lens of business analytics.

WDAAs consist of two tightly integrated modalities to support the intended business and technical skills:

Alternate Reality Courseware (ARC) immerses learners in a simulated environment that delivers the business problem to promote strategic decision-making skills, introduce non-player characters to advance and amplify lessons of the overall experienceJupyter Notebooks deliver students the case related data, analytics tools (R, Python, AI/ML capabilities), and partial or full code to solve the set of business problems

WDAAs teach in three ways:

Learning objectives - skills you will learn and use in the futurePractice objectives - specific experiences you will encounter, including working with real code, data, and algorithms, so that when you see them in the real world, you will know what to doThinking objectives - mental techniques you will learn that are applicable outside of the context of the simulation

Our Partners

Analytics at Wharton
eVite

How our experiences work

Access teaching materials, support and notifications every step of the way

Setup

Configure for your learning objectives, set up classes in the experience

Players Prepare

Invite learners to enroll and set up their groups

Play

Run the experience and access support and notifications as you go

Debrief

Summarize the experience for your learners and the outcomes

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Lessons of Evite Machine Learning Data Application include:

  • Learn how to convert data into insights through understanding the questions to ask, the limits of the data, and the stories the data tells
  • Explore the impact of heterogeneity in data gathering and analysis
  • Overcome the black box of machine learning by understanding what different quantitative approaches actually do
  • Understand how to deploy your quantitative solution to make real-time decisions that drive measurable improvement for an aspect of user experience or lifecycle
  • Learn the role of judgment and feature engineering, and how individual decisions affect the insights gained from machine learning
  • Experiment with the basics of regression and random forest analysis and employ these methods for managerial decisions
  • Manage the trade-offs involved in using simple or complex models, and between accountability and predictability
  • Experience making decisions based on quantitative analysis
  • Engage with critical stakeholders in high-stakes settings: managers & employees.
  • Persuade stakeholders of your analysis and strategy: pitching your model to appeal to a variety of audiences
  • Analytical thinking: the ability to analyze and frame problems
  • Creative thinking: recognizing and pursuing creative approaches towards machine learning by considering multiple techniques
  • Perspective-taking: paying attention to multiple stakeholders with different levels of expertise, abilities, and points of view

Learning objectives

  • Learn how to convert data into insights through understanding the questions to ask, the limits of the data, and the stories the data tells
  • Explore the impact of heterogeneity in data gathering and analysis
  • Overcome the black box of machine learning by understanding what different quantitative approaches actually do
  • Understand how to deploy your quantitative solution to make real-time decisions that drive measurable improvement for an aspect of user experience or lifecycle
  • Learn the role of judgment and feature engineering, and how individual decisions affect the insights gained from machine learning

Practice objectives

  • Experiment with the basics of regression and random forest analysis and employ these methods for managerial decisions
  • Manage the trade-offs involved in using simple or complex models, and between accountability and predictability
  • Experience making decisions based on quantitative analysis
  • Engage with critical stakeholders in high-stakes settings: managers & employees.
  • Persuade stakeholders of your analysis and strategy: pitching your model to appeal to a variety of audiences

Thinking objectives

  • Analytical thinking: the ability to analyze and frame problems
  • Creative thinking: recognizing and pursuing creative approaches towards machine learning by considering multiple techniques
  • Perspective-taking: paying attention to multiple stakeholders with different levels of expertise, abilities, and points of view

Authors

iyengar_head_shot-129x139.jpg

Raghuram Iyengar

Professor of Marketing

Read Raghuram's Bio