Trees are well-known as a non-linear data structure. They don’t store data in a linear way. **They organize data hierarchically.**

A tree is a collection of entities called nodes. Nodes are connected by edges. Each node contains a value or data, and it may or may not have a child node .

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- Root is the topmost node of the tree
- Edge is the link between two nodes
- Child is a node that has a parent node
- Parent is a node that has an edge to a child node
- Leaf is a node that does not have a child node in the tree
- Height is the length of the longest path to a leaf
- Depth is the length of the path to its root

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**Statistics is using math to do technical analysis of data**. Instead of guesstimating, data helps us get concrete and factual information.

The most widely used statistical concept in data science is called *Statistical Features.* It includes important measurements like bias, variance, mean, median and percentiles. It’s all code-friendly too.

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**Fashion and trend forecasting is a process to predict the behaviour, buying habits and inclination of the consumer during a particular time of the year.**

Apart from the age, geography, or income levels of the consumer, one has to look at the occasion, mood, beliefs, and fashion cycles and predict upcoming trends.

The calculation is done as follows: *Season>Target Market>Consumer>Colors>Fabrics>Silhouette>Texture>Usage*

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*"There is no inherent value in any piece of data because all information is meaningless in itself. Why? Because information doesn’t tell you what to do."*

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