Net Promoter Score

Net Promoter Score (of NPS) is een veelgebruikte maat om het succes van een product of website te volgen. Je meet het door één simpele vraag te stellen:

Hoe waarschijnlijk is het dat je dit product of deze website aanbeveelt aan een vriend of collega?

Gebruikers antwoorden op een schaal van 0 - 10, waarbij 0 staat voor helemaal niet waarschijnlijk en 10 voor zeer waarschijnlijk.

NPS

Welke van de volgende beschrijvingen past het best bij NPS?

Deze oefening maakt deel uit van de cursus

Data Science voor het bedrijfsleven

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Deze oefening maakt deel uit van de cursus

Data Science voor het bedrijfsleven

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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: Databronnen en risico's Exercise 2: Gegevens classificeren voor veiligheid Exercise 3: Webdata-events aanmaken Exercise 4: PII beschermen Exercise 5: Gevraagde data Exercise 6: Het doel van vragen bepalen Exercise 7: Feedback van de focusgroep valideren Exercise 8: Net Promoter Score

Huidige oefening

Exercise 9: Aanvullende gegevens verzamelen Exercise 10: Gegevensbronnen ordenen Exercise 11: Frequentie van astma Exercise 12: Gegevens opslaan en ophalen Exercise 13: Cloudplatformen Exercise 14: Een database bevragen Exercise 15: Welk type 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?