Meningkatkan OKR

OKR adalah singkatan dari Objective/Key Result. Objective adalah sasaran, dan Key Result adalah ukuran numerik dari sasaran tersebut.

Perusahaan Justina membuat OKR setiap kuartal untuk menetapkan sasaran dan memantau kemajuan. Timnya menyiapkan slide OKR Data Science di sebelah kanan. Ia telah menyelesaikan dua OKR pertama, tetapi masih mengerjakan OKR 3. Ia ingin memilih Key Result untuk OKR 3 dari daftar di bawah.

Manakah dari Key Result berikut yang merupakan contoh "Memprediksi dan Bereksperimen dengan Data", langkah ketiga dalam alur kerja data science?

Latihan ini adalah bagian dari kursus

Data Science untuk Bisnis

Latihan interaktif praktis

Ubah teori menjadi tindakan dengan salah satu latihan interaktif kami.

Mulai berolahraga

Latihan ini adalah bagian dari kursus

Data Science untuk Bisnis

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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: Apa itu Data Science?Exercise 2: Alur Kerja Segmentasi Pelanggan Exercise 3: Membangun chatbot layanan pelanggan Exercise 4: Meningkatkan OKR

Latihan Saat Ini

Exercise 5: Penerapan Data Science Exercise 6: Menetapkan proyek data science Exercise 7: Riset investasi Exercise 8: Membangun tim data science Exercise 9: Menafsirkan sebuah sprint tim Exercise 10: Menyunting lowongan pekerjaan Exercise 11: Mencocokkan keterampilan dengan pekerjaan Exercise 12: Klasifikasi tugas data

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?