Data is everywhere — from the products you buy to the videos you watch. Data Science is how we turn that data into useful insights, predictions and decisions.
Data Science is not just coding. It combines data, technology, statistics and problem-solving to answer useful questions.
In this course, you'll first understand what, why and how Data Science works — then peek under the hood at its basic tools, data concepts and algorithms.
Let's understand the idea before worrying about the technology.
A Data Scientist looks at data, searches for patterns and uses evidence to answer questions.
Because modern organizations create far more data than humans can manually examine.
👆 Tap an icon to see the role.
Businesses use data to understand customers, forecast demand, improve operations and make better decisions.
Data Science is usually a process, not one magical step.
Tap a card to reveal the example.
Your shopping behaviour can help a store suggest products you may find useful.
Historical and current data can help estimate demand in different areas.
Listening behaviour can be used to suggest songs that may match your interests.
Historical observations and other measurements help models estimate future conditions.
Three questions. Choose the best answer. You'll get an explanation after each choice.
Meet the tools, data concepts, statistics and algorithms.
There isn't one magic tool. Different tools solve different parts of the problem.
👆 Tap a tool to see its job.
Python is a programming language widely used to manipulate data, automate tasks and build models.
A Data Scientist needs to understand what kind of information they're working with.
An algorithm is simply a step-by-step method for solving a problem or finding a useful pattern in data.
👆 Different questions need different types of algorithms.
The algorithm uses past data to estimate a numerical value.
Real-life example: A store uses previous sales to estimate how much it might sell next month.
Statistics help us summarize data and understand what it is telling us.
Imagine these are the daily orders received by a small store.
Tap the cards to reveal the example.
Past sales can be analyzed to estimate future demand.
Transactions may be classified as normal or potentially suspicious for further review.
Viewing behaviour can help suggest content that may interest you.
Customers with similar behaviours can be grouped to reveal useful segments.
Choose the best answer. Read the feedback — then retry if you want to improve your score.