Types of data in Ds

 📊 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 📊

In the world of data science, understanding the various types of data is crucial for making informed decisions and driving valuable insights. Here’s a quick overview of the key data types you’ll encounter:

𝟭.𝗡𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮: 🔢 Data made up of numbers. This includes:

・Continuous Data: Data that spans a range, such as temperature or height.

・Discrete Data: Data with specific values, like the number of customers or items.

𝟮.𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮: 🏷️ Characteristics grouped into categories:

・Nominal Data: No specific order (e.g., colors or gender).

・Ordinal Data: Has a ranking or order (e.g., ratings or educational levels).

𝟯.𝗧𝗲𝘅𝘁 𝗗𝗮𝘁𝗮: ✍️ Words, sentences, or paragraphs that are often processed in Natural Language Processing (NLP) tasks to derive meaning from language.

𝟰.𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗗𝗮𝘁𝗮: ⏳ Observations collected at specific intervals over time, each with a timestamp (e.g., stock prices or weather data).

𝟱.𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗗𝗮𝘁𝗮: 🌍 Geographic or location-based data, including coordinates, shapes, and other spatial information.

𝟲.𝗕𝗶𝗻𝗮𝗿𝘆 𝗗𝗮𝘁𝗮: ⚫⚪ Data with only two possible values, typically 0 and 1. This is common in classification tasks.

𝟳.𝗜𝗺𝗮𝗴𝗲 𝗗𝗮𝘁𝗮: 🖼️ Visual data represented as pixels, widely used in computer vision tasks like object detection and recognition.

𝟴.𝗔𝘂𝗱𝗶𝗼 𝗗𝗮𝘁𝗮: 🎙️ Sound waves used in speech recognition, music analysis, and other audio classification tasks.

Understanding these different data types is essential for applying the right techniques and tools in data science projects. 🔍

What type of data are you working with the most? 

Source

https://data-flair.training/data-science-courses

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