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• Learn Data Manipulation: Understanding how to manipulate data to meet the requirements is also one of the key
factors. Methods like filtering, sorting, grouping, and omitting are essential for extracting insights from large data set.
• Practise Cleaning: Learning to remove data redundancy and data inaccuracy is essential to be data literate.
3. What are the steps involved in Data Acquisition?
Ans. The three key steps involved in Data Acquisition are as follows:
i. Data discovery: It is about hunting for valuable information in different places, checking if it's good quality, and
making sense of what we find.
ii. Data augmentation: It is the process of increasing the amount and diversity of data. We do not collect new data,
rather we transform the already present data. Data augmentation means increasing the amount of data by adding
copies of existing data with small changes.
iii. Data generation: It refers to generating or recording data using sensors. Recording temperature readings of a
building is an example of data generation. Recorded data is stored in a computer in a suitable form.
4. Explain the importance of having a clear structure in data and provide examples of good and poor data structure.
Ans. Clear structure in data ensures it is organised logically, facilitating efficient analysis and interpretation. For example,
marks of students arranged in a spreadsheet is a good structure, whereas a poor structure example if the student
records were stored in a disorganised manner, with inconsistent naming conventions or missing attributes, it would
impede data analysis and decision-making processes.
5. Why is data privacy important?
Ans. It is important because:
• A data breach at a government agency can put top secret information in the hands of an enemy state.
• A breach at a hospital can put personal health information in the hands of those who might misuse it.
• A breach at a corporation can put proprietary data in the hands of a competitor.
• A breach at a school can cause inconvenience to the parents, such as constant calling from tuition and coaching
centers that leads to disturbance.
6. Explain the term computer vision and the type of data used in this ?
Ans. Computer vision is like giving eyes to computers. It helps them look at pictures and videos from the real world and
understand what they're seeing. With Computer Vision, computers can figure out what's in a picture or video, just like
we do with our eyes. They can recognise objects, people, and even actions happening in videos.
Types of data used in computer vision include:
• Image Data: Digital images captured by cameras or satellite imagery, and medical scans.
• Video Data: Video data captured using camera, and surveillance footage.
C. Competency-based/Application-based questions: #Problem Solving & Logical Reasoning
1. Your teacher has asked students to give the choice of at least 3 co-curricular activities from the given list:
a. Painting e. Dance - Indian
b. Music - Western f. Best out of waste
c. Music - Indian g. English Theatre
d. Dance - Western h. Hindi Theatre
You're provided with a dataset containing errors, duplicates, and missing values. How would you approach organising
and cleaning this data to ensure its reliability and usefulness for analysis?
Outline the steps you would take to organise and clean the dataset, ensuring that it is free from errors, duplicates, and
missing values. Additionally, describe any methods or techniques you would use to address these issues and ensure the
dataset's reliability and usefulness for analysis.
Ans. Organising and cleaning the dataset containing co-curricular activities involves several steps to ensure it is free from
errors, duplicates, and missing values, thus making it reliable and useful for analysis.
Initial Assessment: Review dataset structure and identify errors, duplicates, and missing values.
Standardisation: Normalise activity names and correct spelling inconsistencies.
Duplicate Removal: Identify and remove exact duplicates of activities.
Missing Values: Impute missing data using appropriate methods like mode for categorical values.
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