Data Science MCQ Quiz Questions & Answer pdf

Hello aspirants,

1. Data Science Overview:

Data science is an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract insights and knowledge from structured and unstructured data.
2. Data Collection and Cleaning:

Gathering relevant data from various sources, such as databases, APIs, and websites.
Cleaning and preprocessing the data to remove errors, inconsistencies, and missing values.
3. Exploratory Data Analysis (EDA):

Understanding the data’s distribution, patterns, and relationships using statistical and visualization techniques.
4. Data Visualization:

Creating meaningful visual representations of data to facilitate understanding and decision-making.
Tools like Matplotlib, Seaborn, and Plotly are commonly used.
5. Machine Learning:

Using algorithms to enable computers to learn from data and make predictions or decisions without explicit programming.
Types of machine learning: supervised, unsupervised, and reinforcement learning.
6. Feature Engineering:

Selecting, transforming, and creating relevant features (variables) from raw data to improve the performance of machine learning models.
7. Model Building and Evaluation:

Developing and training machine learning models using algorithms like regression, decision trees, neural networks, etc.
Evaluating model performance using metrics like accuracy, precision, recall, and F1-score.
8. Big Data and Distributed Computing:

Dealing with large datasets that can’t be processed on a single machine, often using technologies like Hadoop and Spark.
9. Natural Language Processing (NLP):

Applying machine learning to analyze and understand human language, including tasks like sentiment analysis, text generation, and language translation.
10. Data Ethics and Privacy:
– Ensuring that data collection, analysis, and storage adhere to ethical guidelines and protect users’ privacy.

11. Data Science Tools:
– Programming languages like Python and R are commonly used in data science.
– Libraries like pandas, NumPy, scikit-learn, and TensorFlow facilitate data manipulation, analysis, and machine learning.

12. Data Pipelines and Automation:
– Creating workflows to automate the process of data collection, preprocessing, modeling, and deployment.

13. Data Visualization:
– Presenting data insights using various charts, graphs, and dashboards to effectively communicate findings.

14. Business Applications:
– Data science is widely applied across industries for tasks like customer segmentation, fraud detection, recommendation systems, and more.

15. Continuous Learning:
– Data science is a rapidly evolving field, and staying updated with new algorithms, tools, and techniques is crucial.

Download GK Notes 

Most Important Data Science MCQ Quiz Questions & Answer

1.  Which one is NOT from Phase 1 of Data Science Life Cycle

1. Learning the target domain
2. Developing initial hypothesis
3. Visualize initial hypothesis
4. Identifying key variables

Answer: Visualize initial hypothesis

2. Which of the following is the most important language for Data Science?

1. Ruby
2. R
3. Java
4. None

Answer: R

3. A collection of information about a related topic is referred to as a__________

1. Visualisation
2. Analysis
3. Conclusion
4. Data

Answer: Visualisation

4. To find the _________ you add up all the numbers and then divide by how many numbers you have.

1. Median
2. Mean
3. Mode
4. Range

Answer: Mean

5. Which of the following is performed by Data Scientist ?

1. Create reproducible code
2. Challenge results
3. Define the question
4. All of the above

Answer: Challenge results

6. Which is not a tool for Statistical Data Analysis?

1. Logistic Regression
2. Linear & Non-linear Regression
3. Histogram
4. ANOVA

Answer: Histogram

7. What is the mean of test scores?{70, 70, 80, 85, 85, 90, 95, 95, 100, 100}

1. 85, 95, and 100
2. 30
3. 87
4. None

Answer: 87

8. Choose the correct keyword for this definition: A graphical representation of a data set

1. Data Set
2. Investigative Cycle
3. Visualisation
4. None

Answer: Visualisation

9. To find the ________ you put all numbers in order from least to greatest and find the number that is in the middle.

1. Median
2. Mode
3. Mean
4. Range

Answer: Median

10. R is an interpreted language so it can access through _____________?

1. Command line interpreter
2. Disk operating system
3. Operating system
4. User interface operating system

Answer: Command line interpreter

11. Data has been collected on visitors’ viewing habits at a bank’s website. Which technique is used to identify pages commonly viewed during the same visit to the website?

1. Clustering
2. Classification
3. Association Rules
4. Regression

Answer: Association Rules

12.  A relationship between two or more variables is referred to as a ________

1. Trend
2. Spike
3. All of above
4. None of above

Answer: Trend

13. A graphical representation of a data set is referred to as a ______

1. Visualization
2. Data Set
3. Investigative Cycle
4. None

Answer: Visualization

14. Which of the following step is performed by data scientist AFTER acquiring the data?

1. Data Integration
2. Data Replication
3. Data Cleansing
4. All of the above

Answer: Data Cleansing

15. Data that sits outside the trend is referred to as a ______

1. Outlier
2. Trend
3. Spike
4. Both 1 & 2

Answer: Both 1 & 2

16. Which of the following approach should be used to ask Data Analysis question?

1. Find out the question which is to be answered
2. Find only one solution for particular problem
3. Find out answer from dataset without asking question
4. None

Answer: Find out the question which is to be answered

17. Which of the following is NOT a machine learning algorithm?

1. SVG
2. Random Forest
3. SVM
4. None

Answer: SVG

18. What is Big Data?

1. Data with the word ‘big’ in it
2. Data about people who are big
3. Data with a large size
4. Data made with a big purpose

Answer: Data with a large size

19. What is R an implementation of?

1. Logical Scoping
2. S Programming Language
3. Lexical Scoping
4. Q Programming Language

Answer:  S Programming Language

20. The 5 steps required to identify a problem and come up with a solution are referred to as the ________ Cycle

1. Visualization
2. Investigative
3. Conclusion
4. None

Answer: Investigative

21. Which of the following is characteristic of Processed Data?

1. Hard to use for data analysis
2. Data is not ready for analysis
3. All steps should be noted
4. None of the above

Answer: None of the above

22. Which was not mentioned as a latest trend tool________

1. Excel
2. Pentaho
3. SPSS
4. Notepad

Answer: Notepad

23. Which of the following is one of the key data science skill ?

1. Machine Learning
2. Statistics
3. Data Visualization
4. All of the above

Answer: All of the above

24. Which of the following is not a stage in the Investigative Cycle?

1. Investigate
2. Analysis
3. Conclusion
4. None

Answer: Investigate

25. Vectors come in two parts_____ and _____

1. Atomic vectors and list
2. Atomic vectors and array
3. Atomic vectors and matrix
4. None

Answer: Atomic vectors and list

26.Choose the correct keyword for this definition: A collection of information about a related topic

1. Trend
2. Spike
3. Data Set
4. None

Answer: Data Set

27. The process of evaluating data through analytical and statistical tools.

1. Data Mining
2. Data Exploration
3. Data Analysis
4. Data Visualization

Answer: Data Analysis

28. Which of the following is key characteristic of hacker ?

1. Willing to find answers on their own
2. Afraid to say they don’t know the answer
3. Not Willing to find answers on their own
4. All of the mentioned

Answer: Willing to find answers on their own

29. Which of the following characteristic of big data is relatively more concerned to data science ?

1. Variety
2. Volume
3. Velocity
4. None

Answer: Variety

30. R is an__________ programming language?

1. GPL
2. Open source
3. Closed source
4. Definite source

Answer: Open source

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