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Step 7 Click on Home Sort & Filter Filter to remove the filter from the dataset. Your dataset is now clean and
ready for further processing.

21 st
VIDEO SESSION Century #Experiential Learning
Skills
Scan the QR code or visit the following link to watch the video:
Cleaning Data in Excel

https://www.youtube.com/watch?v=_jmiEGZ6PIY
After watching the video, answer the following question:
In your own Excel tasks, which data-cleaning step do you think you’ll use most often? Why?

Dimensionality of Data

In the digital world, we deal with vast amounts of data every day. Data can be as simple as a list of numbers or as
complex as images, audio, and videos. To understand and work with data effectively, it is important to know about
dimensionality — the number of features or attributes that describe each piece of data. Dimensionality helps in
organising, analysing, and visualising data for decision-making.
In simple terms, dimensionality of data refers to the number of variables or features used to represent an object or
observation. For example, if we store the height of a group of students, the dataset has one dimension. If we store
height and weight, the dataset has two dimensions. The more features we add, the higher the dimensionality becomes.
Understanding the dimensionality of data is a key step in data analysis, data mining, and machine learning. It helps
data scientists explore patterns, detect trends, and make predictions. However, working with very high-dimensional
data can be complex, as visualization and computation become challenging.
Dimensionality also plays an important role in how data is represented and interpreted. Each dimension adds more
information but also increases complexity. Managing dimensionality effectively allows us to extract meaningful insights
from large datasets while keeping computations efficient and visualization understandable.

Data Visualization 177