1. Data basics
Hi, welcome to the course.
My name is Maarten, and I will be your instructor throughout this wonderful data journey.
2. Data is everywhere
You encounter data daily without realizing it. In fact, you are data yourself
3. Data is everywhere
- your name, age, hobbies, and even
4. Data is everywhere
the number of shirts you own or coffee cups you drink. Social media comments, videos, and photos are data too.
5. Data is everywhere
Data is everywhere - from the number of trees in a city
6. Data is everywhere
to the time between
7. Data is everywhere
Earth and the moon.
8. What is data?
"Data" comes from the Latin "datum,"
9. What is data?
meaning "fact." In today's world, facts are incredibly valuable - but that's only if we know their meaning.
10. Data context
Data gains value with context. For example, this graph shows goals scored per season by Messi, a footballer, and Ovechkin, a hockey player.
11. Data context
In some seasons, Messi scored more goals.
12. Data context
In others, Ovechkin scored more. But does that tell us who is better? Not really.
13. Data context
Without knowing the difference between football and ice hockey, the graph alone isn't very helpful.
This missing knowledge is "data context." Context includes details, such as when or where data was collected, often called "metadata.", and it's extremely important when working with data.
14. Types of data
Something else we should first consider when starting to work with data is what data types we have: structured vs. unstructured, quantitative vs. qualitative.
Unstructured data, like a football match video, appears without labels or order. Structured data, like a table listing goals, times, and players, is organized and easier to analyze. Neither is better - it depends on our needs.
15. Structured data
Structured data, common in spreadsheets, is easy to filter and analyze. Examples include sales records, employee attendance, or weather data.
16. Unstructured data
Unstructured data, like highlight videos, interviews, or product pictures, is harder to analyze directly but can offer rich insights when processed.
17. Quantitative vs. qualitative data
While structured and unstructured data differ by how the information is presented, quantitative and qualitative data differ by the type of information they provide.
Quantitative data, or "numerical data", involves numbers, like points scored, height, or temperature, which is ideal for calculations and visualizations.
Qualitative data, or "categorical data", describes things grouped into categories, like favorite sports or customer feedback, useful for spotting patterns.
18. Let's recap
To recap:
Structured data is organized and easy to analyze.
Unstructured data is complex but insightful.
Quantitative data is numerical and ideal for calculations.
Qualitative data describes categories and reveals trends.
19. Let's practice!
Enough theory for now. Let's do some interactive exercises.