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.

Data preparation

The truth is out there

Tasks

False

True

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

What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!

Discover what data science is, why it's so popular, and learn how to do it with hand-on exercises. There is no code required in this introductory course. 

Understanding Data Science

An introduction to data science with no coding involved.

Understanding Data Topics

Data often comes in a ready to analyze state.

Tidy data means having the features as rows and the observations as columns.

Country names should always be abbreviated.

Non-prepared data can lead to errors, biased algorithms and incorrect results.

Removing duplicates, verifying data types, handling missing values, ensuring values use the same measurement system are part of data preparation.

Missing values are tricky, but there are several ways of dealing with them.

Are you prepared?

Understanding Data Science

Hands-on interactive exercise