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Fundamentals of Data Science

Total questions: 25

Worksheet time: 12mins

Name
Class
Date
1.

Identify the key data science skills among the following:

a)

machine learning

b)

statistics

c)

machine learning

d)

all the above

2.

Which of the following is a common data visualization tool?

a)

Tableau

b)

Excel

c)

Python

d)

All of the above

3.

What is the primary purpose of data cleaning in data science?

a)

To enhance data quality

b)

To increase data volume

c)

To visualize data

d)

To store data

4.

What is the significance of exploratory data analysis (EDA) in data science?

a)

To summarize the main characteristics of data

b)

To clean the data

c)

To build predictive models

d)

To store data efficiently

5.

Which programming language is widely used for data analysis?

a)

Java

b)

Python

c)

C++

d)

Ruby

6.

What is the role of a data scientist?

a)

To collect data

b)

To analyze and interpret complex data

c)

To manage databases

d)

All of the above

7.

Which library is commonly used for machine learning in Python?

a)

NumPy

b)

Pandas

c)

Scikit-learn

d)

Matplotlib

8.

What is the purpose of feature engineering in data science?

a)

To select the best model

b)

To create new features from existing data

c)

To visualize data

d)

To clean the data

9.

During a class project, Dia is trying to choose a programming language for statistical analysis. Which of the following languages should she consider?

a)

Java

b)

R

c)

Swift

d)

Go

10.

What is the main goal of data visualization in data science?

a)

To present data in a graphical format

b)

To store data efficiently

c)

To clean the data

d)

To increase data complexity

11.

Which of the following techniques is commonly used for data preprocessing?

a)

Normalization

b)

Data mining

c)

Data warehousing

d)

Data encryption

12.

What is the purpose of using a confusion matrix in machine learning?

a)

To evaluate the performance of a classification model

b)

To visualize data distributions

c)

To clean the dataset

d)

To select features for the model

13.

What is the significance of model evaluation in data science?

a)

To assess the accuracy of a model

b)

To increase data size

c)

To visualize data

d)

To clean the data

14.

What is the main function of a data pipeline in data science?

a)

To automate data collection and processing

b)

To visualize data

c)

To store data securely

d)

To clean the data

15.

Which of the following is a widely used library for data manipulation in Python?

a)

NumPy

b)

Pandas

c)

Scikit-learn

d)

TensorFlow

16.

What is the main objective of supervised learning in machine learning?

a)

To find hidden patterns in data

b)

To predict outcomes based on labeled data

c)

To cluster similar data points

d)

To reduce dimensionality

17.

During a data science project, Myra is tasked with preparing the dataset for analysis. What is the role of data normalization in data preprocessing?

a)

To scale data to a standard range

b)

To increase data redundancy

c)

To visualize data trends

d)

To store data in a database

18.

What is the main benefit of using a decision tree in machine learning?

a)

To provide a clear visualization of decision-making

b)

To increase data complexity

c)

To clean the dataset

d)

To store data efficiently

19.

In a recent project, Neha noticed that some of the data collected was incomplete. Which of the following is a common method for handling missing data?

a)

Imputation

b)

Data encryption

c)

Data mining

d)

Data warehousing

20.

What is the purpose of feature selection in data science?

a)

To reduce the number of input variables

b)

To increase model complexity

c)

To visualize data

d)

To clean the data

21.

Aashi is working on a project that involves analyzing data for her research. Which of the following is a common technique for data transformation?

a)

Log transformation

b)

Data encryption

c)

Data warehousing

d)

Data mining

22.

Which of the following is a popular library for data visualization in Python?

a)

Seaborn

b)

Pandas

c)

NumPy

d)

Scikit-learn

23.

What is the main purpose of data wrangling in data science?

a)

To clean and transform raw data into a usable format

b)

To visualize data trends

c)

To store data in a database

d)

To analyze data patterns

24.

Which of the following is a common method for data sampling?

a)

Random sampling

b)

Data encryption

c)

Data mining

d)

Data warehousing

25.

What is the primary function of a data warehouse?

a)

To store large volumes of data

b)

To clean and preprocess data

c)

To visualize data

d)

To analyze real-time data