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Validation: Cross-verify cleaned data against original sources for accuracy.
Quality Assurance: Ensure data consistency and document cleaning steps for transparency.
Export: Save cleaned dataset in a suitable format (e.g., CSV) for analysis.
Final Check: Validate data integrity post-export to ensure reliability for analysis.
2. Assertion and Reasoning Questions:
Direction: Question 2, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions by selecting
the appropriate option given below:
(a) Both A and R are true and R is the correct explanation of A.
(b) Both A and R are true but R is not the correct explanation of A.
(c) A is true but R is false.
(d) A is false but R is true.
Assertion (A): Cleaning data is a crucial step in the data analysis process.
Reasoning (R): Data cleaning ensures that the dataset is free from errors, duplicates, and missing values, which
enhances the reliability and accuracy of subsequent analysis.
Ans. a
Unsolved Questions
SECTION A (Objective Type Questions)
uiz
A. Tick ( ) the correct option.
1. Data literacy is able to cultivate ………………………. skills to understand and explore data's implications by questioning
assumptions.
a. critical thinking b. programming
c. awareness d. probability
2. Data literacy fuels ………………………. by providing tools and techniques to explore data from different perspectives.
a. errors b. comprehension
c. innovation d. repetition
3. ………………………. enables users to tackle complex problems and derive meaningful relevance.
a. Mathematics b. Trends
c. Project cycle d. Data literacy
4. Data literacy has an impact on which of the following?
a. Public policy b. Cooking
c. Driving d. Jogging
5. By implementing a ………………………. learning approach, organisations can provide a set of diverse resources that align
with individual learning styles.
a. modern b. prescriptive
c. planned d. latest
6. The process of collecting data from websites using software is called ………………………. .
a. Data analysis b. Data reference
c. Data literacy d. Data scraping
7. ………………………. is the process of increasing the amount and diversity of data.
a. Data augmentation b. Data filtering
c. Data processing d. Data modelling
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