WorksheetsBig Data and Business Analytics Module 1 & 2
Total questions: 40
Worksheet time: 23mins
Business Analytics is
The use of data, information technology, statistical analysis, quantitative methods and mathematical or computer based models
Help managers gain improved insight about their business operation
Make better, fact based decision
All of the above
In short, Business Analytics is
Big Data
Use of tools and techniques to turn data into meaningful business insights
Analysis of business
None of the above
Why organisation need Analytics?
Removing inefficiencies – Ability to take right decisions or better decisions
Change in the business environment
Create competitive advantage
All of the above
Components of Business Analytics are
Business Context
Technology
Data Science
All of the above
Data mining is
Mining tool
Data screening
Better understanding characteristics and patterns among variables in large databases using variety of statistical and analytical tools
None of the above
Challenges of Business Analytics (Click all the right options)
Data often need lot of cleaning
Data is much more unstructured
Data volumes are growing fast
Good analytics does not solve bad business process
Option 1 and 2 Only
Characteristics of Business Analytics (Click all the right options)
Web based front end
Data filters/ Drill-down
Security
option 1 and 3 Only
Types of Business Analytics (Click all the right options)
Descriptive Analytics
Web Analytics
Predictive Analytics
Prescriptive Analytics
Descriptive Analytics is
Helps in predicting future
Finding optimum solution
Used for understanding the trends in past data which can be useful for generating insights
All of the above
Predictive Analytics is
Used for understanding the trends in past data which can be useful for generating insights
It predict by examining historical data, detecting patterns or relationship in these data and then extrapolating these relationships forward in time
Finding optimum solution
None of the above
Prescriptive Analytics is
It aims to predict the probability of occurrence of a future event
innovative ways of data summarization.
Used for understanding the trends in past data which can be useful for generating insights
It assist in finding the optimal solution to a problem or in making the right decision among several alternatives
The Question "What happened in past" is answered by
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
All of the above
The Question " What will happen in Future" is answered by
Descriptive Analytics
Prescriptive Analytics
Predictive Analytics
All of the above
The Question "What is the best action" is answered by
Predictive Analytics
Prescriptive Analytics
Descriptive Analytics
All of the above
Technologies involved in Analytics (Click all the right options)
NoSQL databases
Stream analytics
In-memory data fabric
Distributed file stores
Data virtualization
Current Trends in Business Analytics
Data-driven Culture
Augmented Analytics
Mobile BI
All of the above
Types of Data (Click all the right options)
Structured data
Semi-Structured data
Unstructured data
Big Data
Metadata is
It is data about data.
Big data
Kilo Byte Data
All of the above
Scale of Measurement are
Categorical (nominal) data
Ordinal data
Interval data
Ratio data
All of the above
Big Data means
It refer to massive amounts of business data from wide variety of sources, much of which is available in real time and much of which is unpredictable or uncertain.
Meta Data
Kilo Bytes of data
None of the above
Characteristics of Big Data (Click all the right option)
Volume, Velocity
Variety, Veracity
Validity, Volatility
Variability
The most frequently used predictive analytics techniques are
Regression
Logistics regression
Classification trees
All of the above
The frequently used tools in prescriptive analytics are
Linear programming
Integer programming
Meta heuristics
All of the above
Excel is
Software tool for entering, calculating, manipulating and analyzing set of numbers
Is a Hardware
Data type
None of the above
What is "R programming"
Software
Hardware
language for data analysis and statistics.
All of the above
Data Preprocessing is
Machine Learning process
Data mining technique that involves transforming raw data into an understandable format.
Real-world data
All of the above
Data preprocessing stages are:
Data cleaning, Data integration, and Data transformation
Data collection, Data integration, Data reduction, and Data transformation
Data cleaning, Data mining, Data reduction, and Data transformation
Data cleaning, Data integration, Data reduction, and Data transformation
Data cleaning refers to techniques to ‘clean’ data by
Removing outliers
Replacing missing values
Smoothing noisy data
Correcting inconsistent data
All of the above
In order to deal with missing data, which of the below is "INCORRECT" approach?
Filling in missing value manually
Using a standard value to replace the missing value
Using central tendency (mean, median, mode) for attribute to replace the missing value
Binning and Outlier analysis
Using the most probable value to fill in the missing value
Select all the most common approaches to integrate data
Data consolidation
Data propagation
Outlier analysis
Data virtualization
Select all the methods to reduce the volume of data
Missing values ratio
Data virtualization
Low variance filter
High correlation filter
Principal component analysis
Valid measures of central tendency are
Mean, Median, Range and Mode
Mean, Median, IQR and Mode
Mean, Median, Standard Deviation and Mode
None of the above
To calculate the most frequently occurring value we use
Mean
Mode
Median
Range
Select all the valid measures of Dispersion or Variation
Range
Variance
Standard deviation
Mean deviation and interquartile range
None of the above
A distribution of data item values may be
Symmetrical or asymmetrical.
Structured or unstructured
Nominal or Ordinal
All of the above
Key features of the normal distribution are (Click all the right options)
Symmetrical shape
Mode, median and mean are the same
There can only be one mode
Most of the data are clustered around the centre
None of the above
A distribution is said to be positively skewed when
Most of the data are clustered around the centre
Most of the values tend to cluster toward the right side of the x-axis
The tail on the left side of the histogram is longer than the right side
The tail on the right side of the histogram is longer than the left side
A distribution is said to be negatively skewed when
The tail on the right side of the histogram is longer than the left side
Most of the values tend to cluster toward the left side of the x-axis
Most of the values tend to cluster toward the right side of the x-axis
Most of the data are clustered around the centre
Bivariate data means
This type of data consists of only one variable.
This type of data involves two different variables.
This type of data involves three or more variables
None of the above
Which of the below data visualization tool is used to understand relationship between two variables
Pie Chart
Bar Chart
Coxcomb Chart
Scatter Plot
