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.

Data sources

Sorting data sources

Asthma frequencies

Data types

Classifying data types

Net promoter score

Activity tracker

Data storage and retrieval

Cloud platforms

Querying a database

Which type of database?

Data Pipelines

Data pipeline characteristics

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.

Preparación de datos

La verdad está ahí fuera

Tareas

Falso

Verdadero

¿Te has preparado?

Análisis exploratorio de datos

EDA numérico

Visual EDA

Visualización

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

¿Qué es la ciencia de datos, por qué es tan popular, y por qué la Harvard Business Review la ha aclamado como el "trabajo más sexy del siglo XXI"? En este curso no técnico conocerás todo lo que siempre has temido preguntar sobre este campo apasionante y de rápido crecimiento, sin necesidad de escribir una sola línea de código. Con ejercicios prácticos, aprenderás los diferentes roles de los científicos de datos; temas fundamentales como las pruebas A/B, el análisis de series temporales y el machine learning; y cómo los científicos de datos extraen conocimientos e información de los datos del mundo real. Así que no te dejes desanimar por las palabras de moda. Empieza a aprender, adquiere destrezas en este campo tan demandado, ¡y descubre por qué la ciencia de datos es para todos!

Descubre qué es la ciencia de datos, por qué es popular y aprende con ejercicios prácticos. Sin código.

Comprender la ciencia de datos

Introducción a la ciencia de datos, ¡y no hay que programar!

Comprender los temas de datos

Improving a dashboard

Comprender la ciencia de datos

Ejercicio interactivo práctico