Convalidare il feedback del focus group

La settimana scorsa Kristof ha condotto un focus group per Foodwheel, un servizio di consegna pasti. A destra trovi i suoi appunti dell’incontro. Il focus group ha generato molte ipotesi interessanti, ma Kristof non vuole fare proposte alla direzione finché non avrà dei dati quantitativi.

Quale delle seguenti domande sarebbe la migliore da porre a un ampio campione di utenti Foodwheel per convalidare i risultati del focus group?

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Data Science per il Business

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Questo esercizio fa parte del corso

Data Science per il Business

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We'll start the course by defining what data science is. We'll cover the data science workflow, and how data science is applied to real-world business problems. We'll finish the chapter by learning about ways to structure your data team to meet your organization's needs.

Exercise 1: What is Data Science?Exercise 2: Customer Segmentation Workflow Exercise 3: Building a customer service chatbot Exercise 4: Improving OKRs Exercise 5: Applications of Data Science Exercise 6: Assigning data science projects Exercise 7: Investment research Exercise 8: Building a data science team Exercise 9: Interpreting a team sprint Exercise 10: Editing a job posting Exercise 11: Matching skills to jobs Exercise 12: Classifying data tasks

Now that we understand the data science workflow, we'll dive deeper into the first step: data collection. We'll learn about the different data sources your company can draw from, and how to store that data once it's collected.

Exercise 1: Fonti di dati e rischi Exercise 2: Classificare i dati per la sicurezza Exercise 3: Creare eventi di dati web Exercise 4: Proteggere i PII Exercise 5: Dati sollecitati Exercise 6: Identificare lo scopo delle domande Exercise 7: Convalidare il feedback del focus group

Esercizio in corso

Exercise 8: Net Promoter Score Exercise 9: Raccogliere dati aggiuntivi Exercise 10: Ordinare le fonti di dati Exercise 11: Frequenza dell'asma Exercise 12: Archiviazione e recupero dei dati Exercise 13: Piattaforme cloud Exercise 14: Interrogare un database Exercise 15: Quale tipo di database?

In this chapter, we'll discuss ways to explore and visualize data through dashboards. We'll discuss the elements of a dashboard and how to make a directed request for a dashboard. This chapter will also cover making ad hoc data requests and A/B tests, which are a powerful analytics tool that de-risk decision-making.

Exercise 1: Dashboards Exercise 2: Classifying dashboard elements Exercise 3: Improving a dashboard Exercise 4: Choosing the right dashboard Exercise 5: Ad hoc analysis Exercise 6: Filling out an ad hoc request Exercise 7: Classifying requests Exercise 8: A/B Testing Exercise 9: Creating an A/B testing workflow Exercise 10: Sample size Exercise 11: Intermediate results

In this final chapter, we'll discuss the buzziest topic in data science: machine learning! We'll cover supervised and unsupervised machine learning, and clustering. Then, we'll move on to special topics in machine learning, including time series prediction, natural language processing, deep learning, and explainable AI!

Exercise 1: Supervised machine learning Exercise 2: When to use Supervised Learning Exercise 3: Features and labels Exercise 4: Model evaluation Exercise 5: Clustering Exercise 6: Supervised vs. unsupervised Exercise 7: Cluster size selection Exercise 8: Special topics in Machine Learning Exercise 9: Classifying machine learning tasks Exercise 10: Sentiment Analysis Exercise 11: Deep Learning and Explainable AI Exercise 12: Finding the correct solution Exercise 13: Should I use Deep Learning?