Migliorare gli OKR

OKR sta per Objective/Key Result (Obiettivo/Risultato chiave). Un Objective è un traguardo, e un Key Result è una misura numerica di quel traguardo.

L'azienda di Justina crea OKR ogni trimestre per definire obiettivi e monitorare i progressi. Il suo team ha preparato la diapositiva sugli OKR di Data Science sulla destra. Ha completato i primi due OKR, ma sta ancora lavorando all'OKR 3. Vuole scegliere un Risultato chiave per l'OKR 3 dall'elenco qui sotto.

Quale dei seguenti Risultati chiave sarebbe un esempio di "Prevedere e Sperimentare con i Dati", il terzo passaggio del workflow di data science?

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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: Che cos'è la Data Science?Exercise 2: Workflow per la segmentazione dei clienti Exercise 3: Creare un chatbot per l'assistenza clienti Exercise 4: Migliorare gli OKR

Esercizio in corso

Exercise 5: Applicazioni della Data Science Exercise 6: Assegnare i progetti di data science Exercise 7: Ricerca sugli investimenti Exercise 8: Creare un team di data science Exercise 9: Interpretare uno sprint del team Exercise 10: Modificare un annuncio di lavoro Exercise 11: Abbinare competenze e ruoli Exercise 12: Classificare le attività sui dati

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: 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?