Scelta del numero di cluster

Crystal gestisce le operazioni per un servizio di consegna pasti a Houston, Texas. Ha a disposizione latitudine e longitudine delle richieste di pasti dell’ultimo mese. Vorrebbe creare alcuni nuovi "hub di consegna" nelle aree dove la domanda è stata più alta. Per farlo, ha chiesto a una Machine Learning Scientist di usare un algoritmo di clustering per dividere gli ordini in gruppi in base alla loro posizione.

La Machine Learning Scientist non è sicura di quanti cluster creare. A destra, esegui l’algoritmo con numeri diversi di cluster per vedere i risultati.

Qual è il numero di cluster più adatto?

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Data Science per il Business

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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: Data sources and risks Exercise 2: Classifying data for security Exercise 3: Creating web data events Exercise 4: Protecting PII Exercise 5: Solicited data Exercise 6: Identifying question purpose Exercise 7: Validating focus group feedback Exercise 8: Net Promoter Score Exercise 9: Collecting additional data Exercise 10: Sorting data sources Exercise 11: Asthma frequency Exercise 12: Data storage and retrieval Exercise 13: Cloud platforms Exercise 14: Querying a database Exercise 15: Which type of 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: Machine Learning supervisionato Exercise 2: Quando usare il Supervised Learning Exercise 3: Feature e label Exercise 4: Valutazione del modello Exercise 5: Clustering Exercise 6: Supervised vs. Unsupervised Exercise 7: Scelta del numero di cluster

Esercizio in corso

Exercise 8: Argomenti speciali nel Machine Learning Exercise 9: Classificare i compiti di machine learning Exercise 10: Sentiment Analysis Exercise 11: Deep Learning ed Explainable AI Exercise 12: Trovare la soluzione corretta Exercise 13: Dovrei usare il Deep Learning?