Dasbor interaktif

Dasbor itu bagus, dan dasbor interaktif lebih baik lagi. Berikut adalah sebuah dasbor untuk melacak aktivitas sebuah situs web. Dasbor ini terdiri dari (A) rata-rata lama kunjungan halaman yang dipilah berdasarkan usia, (B) jumlah kunjungan hari ini, (C) pengguna aktif bulanan dari waktu ke waktu, dan (D) sumber trafik yang dipilah per bulan.

Jika Anda mengarahkan kursor atau mengetuk elemen tertentu pada grafik batang dan garis, Anda akan melihat interaktivitas yang menampilkan detail lebih lanjut tentang data yang dipilih. Anda selalu dapat mengatur ulang plot ke keadaan awalnya dengan mengeklik ganda plot tersebut.

Dengan memanfaatkan interaktivitas ini, berapa banyak trafik situs web pada bulan Maret yang berasal dari mesin pencari?

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Latihan interaktif langsung

Ubah teori menjadi aksi dengan salah satu latihan interaktif kami

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Latihan ini merupakan bagian dari kursus

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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: Persiapan data Exercise 2: Kebenaran itu ada di luar sana Exercise 3: Apakah Anda sudah siap?Exercise 4: Analisis Data Eksploratif Exercise 5: EDA numerik Exercise 6: EDA Visual Exercise 7: Visualisasi Exercise 8: Dasbor interaktif

Latihan Saat Ini

Exercise 9: Meningkatkan sebuah dasbor

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: A/B Testing Exercise 2: Creating an A/B testing workflow Exercise 3: Statistical significance Exercise 4: Intermediate results Exercise 5: Time series forecasting Exercise 6: Classifying time series data Exercise 7: Interpret a time series plot Exercise 8: Supervised machine learning Exercise 9: When to use supervised learning Exercise 10: Features and labels Exercise 11: Model Evaluation Exercise 12: Clustering Exercise 13: Supervised vs. unsupervised Exercise 14: Cluster size selection Exercise 15: Congratulations!