Pemilihan jumlah klaster

Crystal mengelola operasional layanan pengantaran makanan di Houston, Texas. Ia memiliki data lintang dan bujur untuk permintaan makanan pada bulan lalu. Ia ingin membuat beberapa "pusat pengantaran" baru di lokasi dengan permintaan tertinggi. Untuk melakukan ini, ia meminta seorang ilmuwan machine learning menggunakan algoritma pengelompokan untuk membagi pesanan ke dalam kelompok berdasarkan lokasinya.

Ilmuwan machine learning tersebut belum yakin berapa banyak klaster yang harus dibuat. Tinjau hasil algoritma pengelompokan dengan jumlah klaster yang berbeda-beda.

Berapa jumlah klaster yang paling sesuai?

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Latihan ini merupakan bagian dari kursus

Memahami Ilmu Data

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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 problems. We'll finish the chapter by learning about different roles within the data science field.

Exercise 1: What is data science?Exercise 2: Data science everywhere!Exercise 3: Importance of data science Exercise 4: Applications of data science Exercise 5: Daily dose of data science Exercise 6: The science of smart assistants Exercise 7: Data science roles and tools Exercise 8: Selecting the right candidate Exercise 9: A data scientist role

Now that we understand the data science workflow, we'll dive deeper into the first step: data collection and storage. We'll learn about the different data sources you can draw from, what that data looks like, how to store the data once it's collected, and how a data pipeline can automate the process.

Exercise 1: Data sources Exercise 2: Sorting data sources Exercise 3: Asthma frequencies Exercise 4: Data types Exercise 5: Classifying data types Exercise 6: Net promoter score Exercise 7: Activity tracker Exercise 8: Data storage and retrieval Exercise 9: Cloud platforms Exercise 10: Querying a database Exercise 11: Which type of database?Exercise 12: Data Pipelines Exercise 13: Data pipeline characteristics Exercise 14: Extract Transform Load

Data preparation is fundamental: data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This chapter will show you how to diagnose problems in your data, deal with missing values and outliers. You will then learn about visualization, another essential tool to both explore your data and convey your findings.

Exercise 1: Data preparation Exercise 2: The truth is out there Exercise 3: Are you prepared?Exercise 4: Exploratory Data Analysis Exercise 5: Numerical EDA Exercise 6: Visual EDA Exercise 7: Visualization Exercise 8: Interactive dashboards Exercise 9: Improving a dashboard

In this final chapter, we'll discuss experimentation and prediction! Beginning with experiments, we'll cover A/B testing, and move on to time series forecasting where we'll learn about predicting future events. Finally, we'll end with machine learning, looking at supervised learning, and clustering.

Exercise 1: Pengujian A/B Testing Exercise 2: Membuat alur kerja pengujian A/B Exercise 3: Signifikansi statistik Exercise 4: Hasil sementara Exercise 5: Peramalan deret waktu Exercise 6: Mengklasifikasikan data deret waktu Exercise 7: Menafsirkan plot deret waktu Exercise 8: Pembelajaran mesin yang diawasi Exercise 9: Kapan menggunakan pembelajaran dengan pengawasan Exercise 10: Fitur dan label Exercise 11: Evaluasi Model Exercise 12: Pengelompokan data yang tidak berlabel Exercise 13: Dengan Pengawasan vs. Tanpa Pengawasan Exercise 14: Pemilihan jumlah klaster

Latihan Saat Ini

Exercise 15: Selamat!