The Arranging and Collecting Data Class 9 Chapter 2 Notes explain the basic concepts of how raw data is collected, organized, and arranged in a systematic way for easy understanding and analysis. These notes help students learn different methods of data collection, classification, and tabulation.
Arranging and Collecting Data Class 9 Notes
What is data collection?
The method of gathering data for calculating and analyzing reliable insights is known as data collection, which is done using standard validated techniques. This data helps for scientific purposes, research, and business to make or predict decisions. For example, suppose you want to compare temperatures in six cities on the same day. The first thing we have to do is the collection of the data. After collecting data, you have to visualize the data, like a bar chart, which helps to compare the temperature in six cities on the same day. So, data collection is very important, and data collection should be collected from genuine sources.
Variables
A variable is an attribute of an object that can change from one case to another. In simple words, a variable value can be a change or a characteristic that can be measured. For example, in the survey on the temperature of many cities worldwide on the same day, the variables are “Temperature” and “City” because both the attributes vary for different cases.
Now variables can be of two types.
- Numerical variable: They represent values that have numbers. For example, age, weight, and height.
- Categorical variable: These variables represent values that have words, for example, name, nationality, sport, etc.
Types of data
Data can be divided into two categories: quantitative and qualitative.
- Quantitative Data: Quantitative data is data that can be measured or counted. It is always expressed in numbers. For example, the number of times a product has been searched on the internet or the number of items sold per month.
- Qualitative data: Qualitative data is data that describes qualities of characteristics. It is usually expressed in words, labels, or categories, not in numbers. For example, a traveler’s review for a hotel or customer service feedback given by a consumer after a telephone conversation.
Sources of data
Data can be collected from different places. These are called data sources, and these data sources are divided into two types.
1. Primary Data Source
In a primary data source, data are collected directly for the specific purpose. Some methods of collecting primary data are
- Physical interviews
- Online surveys
- Feedback forms
2. Secondary Data Sources
These data were collected earlier for other purposes, but not these data are reused. Some methods of collecting secondary data are:
- Social media data tracking
- Web traffic tracking
- Satellite data tracking
What is Big Data?
“Big data” means very large and complex data. When the data volumes exceed the processing capacities of traditional databases, they are called “big data.”
Think of the common social media platforms. Millions of users are using the platforms and creating an enormous amount of content every minute. Processing this vast amount of data requires specialized skills and systems. Such systems capable of extracting statistical insights from a huge amount of data are called Big Data systems.
Now let us understand some of the key characteristics that can define Big Data:
- Volume: This refers to the size of the data. Usually, data sets greater than terabytes and petabytes are called Big Data.
- Variety: Big Data sets are generally collected from a wide range of sources, including transactional databases, sensor data, etc. It could include images, pictures, audio, video, etc.
- Velocity: The rate at which data is generated. Big Data has generally created a rapid speed, resulting in high volumes very soon. For example, social media platforms generate a massive amount of data every minute.
Big Data techniques are widely used in different sectors. Let us see some of them:
- Retail: Big stores like online shops or supermarkets collect customer data; they track their purchases, preferences, and feedback.
- Science: NASA uses big data to store climate data over 32 petabytes. Scientists use this data to study weather patterns and climate change.
- Sports: Formula race cars have a lot of sensors; these sensors collect data like tire pressure and fuel usage.
- Social Media: There are platforms like Facebook, Instagram, and YouTube that collect data in petabytes to analyze the comments, likes, and shares.
- Healthcare: During COVID-19, the government used big data to track infected people across the country. It helps to identify cases, plan treatment, and reduce the spread of the virus.
Questioning your data
Once the data is collected, now we have to understand it. Data is usually stored in:
- Numbers, which are known as numeric data or quantitative data
- Labels or categories, which are known as categorical data.
We ask different questions depending on the data for understanding it. Let’s see some examples.
Question 1: Is this A or B?
In this the questions are based on yes/no or true/false, only two possible answers. This is called binary classification or two-class classification, which has only two possible answers. For example,
- Q: Will a customer buy this product?
- A: Yes/No
- Q: Can India win this cricket match?
- A: Yes/No
If there are more than two answers, then we use multiclass classification.
Question 2: Is this odd?
Sometimes some unusual data is also collected; this data is called an anomaly. This anomaly data should be corrected and should be error-free. For example,
- Q: Your father is getting his blood pressure checked. Is the reading regular?
- Q: You are checking your car tire pressure. Is the reading regular?
Algorithms used for these types of questions are called anomaly detection algorithms.
Question 3: How much or how many?
There are scenarios when we need to predict numerical values based on the data. Some examples are:
- Q: How many goals will your favorite team score in this football match?
- A: 3
- Q: What will be the temperature of your city next Friday?
- A: 32°C
The algorithms that predict these values are called regression algorithms.
Question 4: Can I group the data?
Sometimes data may be separated into distinct groups. This approach is called clustering. For example, consider a class of 60 students. We have recorded their heights and arranged them in a table.
Question 5: What should I do now?
Consider the following questions:
- Q: I am a self-driving car. I am at a traffic signal with a red light. What should I do now?
- A: Brake
- Q: I am a microwave oven. I have already heated the food for the set timing. What should I do now?
- A: Stop.
These are questions that, generally, a machine or robot is programmed to do. Based on trial and error, machines take some actions. These types of learning are called reinforcement learning.
Univariate and multivariate data
Univariate data means data with only one variable; it does not show the relationships between different things. For example,
- Height of students in a class
- Temperature in one city
- Number of books read by each student
Multivariate data means data with two or more variables; it shows how things are connected or related. For example,
- Umbrella sales increase during the rainy season.
- → Variables: Rainfall and Sales
- A student’s marks may depend on study hours, sleep, and attendance.
- → Variables: Study hours, Sleep, Attendance, Marks
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