# Data Acquisition AI Class 9 Comprehensive Notes

#### Bytutorialaicsip

Nov 13, 2020

If you are looking for the comprehensive notes for Data Acquisition AI Class 9, then you reached on the perfect article for the same.

In this article, we will discuss sub unit Data Acquisition AI Class 9 that is part of Unit 2 AI project cycle of Class 9 CBSE curriculum.

Before starting this article, I would like to recommend you to go through the previous article by following the link:

Problem Scoping

Topics Covered

## Introduction to Data Acquisition AI Class 9

Data Acquisition consists of two words:

1. Data : Data refers to the raw facts , figures, or piece of facts, or statistics collected for reference or analysis.
2. Acquisition: Acquisition refers to acquiring data for the project.

The stage of acquiring data from the relevant sources is known as data acquisition.

Now you need to understand the classification of data for Data Acquisition AI Class 9.

## Classification of Data

Now Observe the following diagram to for the data classification, we will discuss each of them in detail:

### Basic Data

Basically, data is classified into two categories:

1. Numeric Data: Mainly used for computation. Numeric data can be classified into the following:
• Discrete Data: Discrete data only contains integer numeric data. It doesn’t have any decimal or fractional value. The countable data can be considered as discrete data. For example 132 customers, 126 Students etc.
• Continuous Data: It represents data with any range. The uncountable data can be represented in this category. For example 10.5 KGS, 100.50 Kms etc.
2. Text Data: mainly used to represent names, collection of words together, phrases, textual information etc.

### Structural Classification

The data which is going to be feed in the system to train the model or already fed in the system can have a specific set of constraints or rules or unique pattern can be considered as structural data.

The structure classification is divided into 3 categories:

1. Structured Data: As we discussed the structured data can have a specific pattern or set of rules. These data have a simple structure and stores the data in specific forms such as tabular form. Example, The cricket scoreboard, Your school time table, Exam datasheet etc.
2. Unstructured Data: The data structure which doesn’t have any specific pattern or constraints as well as can be stored in any form is known as unstructured data. Mostly the data that exists in the world is unstructured data. Example, Youtube Videos, Facebook Photos, Dashboard data of any reporting tool etc.
3. Semi-Structured Data: It is the combination of both structured and unstructured data. Some data can have a structure like a database whereas some data can have markers and tags to identify the structure of data.

### Other Classification

This classification is sub divided into the following branches:

1. Time-Stamped Data: This structure helps the system to predict the next best action. It is following a specific time-order to define the sequence. This time can be the time of data captured or processed or collected.
2. Machine Data: The result or output of a specific program, system or technology considered as machine data. It consists of data related to a user’s interaction with the system like the user’s logged-in session data, specific search records, user engagement such as comments, likes and shares etc.
3. Spatiotemporal Data: The data which contains information related to geographical location and time is considered as spatiotemporal data. It records the location through GPS and time-stamped data where the event is captured or data is collected.
4. Open Data: It is freely available data for everyone. Anyone can reuse this kind of data.
5. Real-time Data: The data which is available with the event is considered as real-time data.
6. Big Data: You may hear this word most often. The data which cannot be stored by any system or traditional data collection software like DBMS or RDBMS software can be considered as Big data. Big data itself a very deep topic.

## Data Features

Data features refer to the type of data you want to collects. Here two terms are associated with this:

1. Training Data: The collected data through the system is known as training data. In other words the input given by the user in the system can be considered as training data.
2. Testing Data: The result data set or processed data is known as testing data. In other words, the output of the data is known as testing data.

The big data and some of the characteristics of big data is explained in this article.

Feel free to share your suggestions/feedback/views in the comment section.