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.

What is data science?

Data science everywhere!

Importance of data science

Applications of data science

Daily dose of data science

The science of smart assistants

Data science roles and tools

Selecting the right candidate

A data scientist role

Introduction to Data Science

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.

Sumber data

Permintaan

Data Perusahaan

Data Terbuka

Mengelompokkan sumber data

Frekuensi asma

Tipe data

Opsi

Data Kuantitatif

Data Kualitatif

Mengklasifikasikan tipe data

Net promoter score

Pelacak aktivitas

Penyimpanan dan pengambilan data

Platform cloud

Mengkueri basis data

Kumpulan data

Basis Data Relasional

Basis Data Dokumen

Tipe basis data yang mana?

Saluran Data

Karakteristik saluran data

Tugas

Extract

Transform

Load

Extract Transform Load

Data Collection and Storage

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.

Data preparation

The truth is out there

Are you prepared?

Exploratory Data Analysis

Numerical EDA

Visual EDA

Visualization

Interactive dashboards

Improving a dashboard

Preparation, Exploration, and Visualization

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.

A/B Testing

Creating an A/B testing workflow

Statistical significance

Intermediate results

Time series forecasting

Classifying time series data

Interpret a time series plot

Supervised machine learning

When to use supervised learning

Features and labels

Model Evaluation

Clustering

Supervised vs. unsupervised

Cluster size selection

Congratulations!

Experimentation and Prediction

Course Glossary

Apa itu ilmu data, mengapa begitu populer, dan mengapa Harvard Business Review menyebutnya sebagai “pekerjaan terseksi abad ke-21”? Dalam kursus non-teknis ini, Anda akan diperkenalkan pada semua hal yang mungkin selama ini enggan Anda tanyakan tentang bidang yang berkembang pesat dan menarik ini, tanpa perlu menulis satu baris kode pun. Melalui latihan interaktif, Anda akan mempelajari berbagai peran ilmuwan data, topik dasar seperti pengujian A/B, analisis deret waktu, dan Pembelajaran Mesin, serta bagaimana ilmuwan data mengekstraksi pengetahuan dan wawasan dari data dunia nyata. Jadi, jangan khawatir dengan berbagai istilah populer. Mulailah belajar, kembangkan keterampilan di bidang yang sangat diminati ini, dan temukan mengapa ilmu data cocok untuk semua orang!

Video dilengkapi transkrip langsung yang dapat diakses dengan mengeklik "Show transcript" di kiri bawah video.

Glosarium kursus dapat ditemukan di sebelah kanan pada bagian sumber daya.

Untuk memperoleh kredit CPE, Anda perlu menyelesaikan kursus dan mencapai skor 70% pada asesmen yang memenuhi syarat. Anda dapat menuju asesmen dengan mengeklik callout kredit CPE di sebelah kanan.

Pelajari data science, alasan kepopulerannya, dan cara melakukannya dengan latihan praktis tanpa kode.

Memahami Ilmu Data

Pengantar data science tanpa keterlibatan coding.

Memahami Topik Data

Frekuensi asma

Memahami Ilmu Data

Latihan interaktif langsung