What is data science?

1. What is data science?

Hi, I’m Maarten! Did you know that every time you shop online, post something on social media, or use your smartphone's GPS, you're adding to a vast pool of data? In this course, we’re going to uncover how that data is collected, analyzed, and transformed into powerful insights—and how you can tap into this process yourself. So, what exactly is data science? It might sound intimidating, but it’s just a way to make sense of the huge amounts of information we generate every day. Think about it: every tap on your phone, every personalized recommendation on your streaming service, every fraud alert on your credit card—all of that is data science working behind the scenes. Take online shopping, for example. Retailers analyze your purchase history and browsing habits to recommend that perfect gift or decide the best moment to send you a sale notification. It’s all about using data to understand what you might need, sometimes even before you realize it yourself. At its core, data science is about taking all that raw data, cleaning it up, and uncovering the patterns hidden inside. These patterns help us make better predictions and improve outcomes, whether it’s identifying a disease, forecasting local weather, or curating your personalized playlist on your favorite music app. So why is data science so central today? It’s because we’re generating more data than ever before. Every online purchase, every ride with a rideshare app, every interaction with a smart home device contributes to a digital tapestry that businesses, governments, and tech innovators use to understand our world better. So, are you ready to learn how to harness this power for yourself? Let’s dive in some exercises and explore how data science can help you make smarter decisions, solve real-world challenges, and even glimpse into the future!

2. Let's practice!

This exercise is part of the course

Understanding Data Science

BeginnerSkill Level

4.8+

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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?

Current Exercise

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: 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!