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MCQs | Data Analytics Overview
Data Analytics Overview MCQs: This section contains the Multiple-Choice Questions & Answers on Data Analytics Overview with explanations.
Submitted by IncludeHelp, on October 18, 2021
Data Analytics Overview MCQs
1. Data analytics refers to a,
- Analyzing Data
- Scientific approach to get insights from the data or data sets
- Find the hidden patterns from data
- All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Data analytics refers to a scientific approach to get insights from the data or data sets. Data analytics not only analyzes the data but also explores it to find the hidden patterns from data. Data Analytics examines data sets in order to identify trends and develop conclusions about the information contained within them.
2. Data analytics applies on,
- Raw Data or Data Set
- Algorithm
- Scientific method
- None of the mentioned above
Answer: A) Raw Data or Data Set
Explanation:
Data analytics applies to raw data to convert it into useful information. The science of studying raw data in order to draw conclusions about it is known as data analytics. Data analytics techniques and processes have been turned into mechanical processes and algorithms that operate on raw data for human consumption.
3. A good data analytics explores the data from different angles to get the information,
- True
- False
Answer: A) True
Explanation:
A good data analytics explores the data from different angles to get the information which can be used in decision making.
4. Amongst which of the following is / are the significance of data analytics,
- To collect the data, store it and put analysis
- Analyze the data to get the fruitful insights and hidden information
- Organizations can utilize data analytics to gain control over their data
- All of the mentioned above
Answer: D) All of the mentioned above
Explanation:
Companies are collecting the data, store it and put analysis on it to get fruitful insights and hidden information from the data. It can be identified and recognized that what data is saying and what will be the future prediction so that organizations existence can be established.
5. Data analytics works by analyzing large data sets with a variety of tools and methods,
- True
- False
Answer: A) True
Explanation:
Data analytics works by analyzing large data sets with a variety of tools and methods in order to uncover unique patterns, hidden correlations and relevant trends, and other insights that can be used to make data-driven decisions in the pursuit of improved results.
6. Data collection refers to,
- Store the data
- Collect the data
- Process the data
- None of the mentioned above
Answer: B) Collect the data
Explanation:
Data collection refers to collect the data from the source which is needed to analyze. This can be accomplished through a variety of means, including computers, web sources, cameras, environmental sources, human beings, etc.
7. Data Cleaning refers to the act of preparing data for analysis through the removal or modification of data,
- True
- False
Answer: A) True
Explanation:
Data cleaning is the act of preparing data for analysis through the removal or modification of data that is erroneous, incomplete, irrelevant or duplicated, or that has been incorrectly formatted.
8. Data analysis is a process to draw insights through numerous data sets.
- True
- False
Answer: A) True
Explanation:
Data analysis is a process to draw insights through numerous data sets. Data analytics techniques and processes have been turned into mechanical processes and algorithms that operate on raw data for human consumption. A company's performance can be improved by using data analytics.
9. Amongst which of the following is / are the tools used in Data Analytics,
- SAS
- R
- Python
- All of the mentioned above
Answer: D) All of the above mentioned
Explanation:
SAS, R and Python are some set of popular tools which are used in the data analytics.
10. Data analytics plays a vital role in Decision Making.
- True
- False
Answer: A) True
Explanation:
Data analysis plays a vital role in decision-making. The goal is to make these corporate activities more time-efficient by streamlining them. Operational costs, product development, and labor planning are just a few examples.