Net Promoter Score

Net Promoter Score (veya NPS), bir ürünün ya da web sitesinin başarısını izlemek için kullanılan yaygın bir metriktir. Basit bir soruyla ölçülür:

Bu ürünü veya web sitesini bir arkadaşına ya da meslektaşına önerme olasılığın nedir?

Kullanıcılar 0 - 10 arasında yanıt verir; 0 hiç önermeyecek, 10 ise son derece önerecek anlamına gelir.

NPS

Aşağıdakilerden hangisi NPS'i en iyi şekilde açıklar?

Bu egzersiz

İş Dünyası için Data Science

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

İş Dünyası için Data Science

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Veri kaynakları ve riskler Exercise 2: Güvenlik için verileri sınıflandırma Exercise 3: Web veri olayları oluşturma Exercise 4: KKB'yi koruma Exercise 5: Talep edilen veriler Exercise 6: Soru amacını belirleme Exercise 7: Odak grubu geri bildirimini doğrulama Exercise 8: Net Promoter Score

Geçerli Egzersiz

Exercise 9: Ek veri toplama Exercise 10: Veri kaynaklarını ayırma Exercise 11: Astım sıklığı Exercise 12: Veri depolama ve alma Exercise 13: Bulut platformları Exercise 14: Bir veritabanında sorgu çalıştırma Exercise 15: Hangi tür veritabanı?

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?