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WorksheetsData Science
Total questions: 50
Worksheet time: 40mins
Data Science is
The science of creating data.
It is a branch of Social Studies.
Multidisciplinary study of data collections for analysis, prediction, learning and prevention.
It is a specialized field of study under Artificlal Intelligence
Steps in Data Science
Data Modeling ->Data Acquisition -> Clean Data ->Data Analysis ->Deployment and optimization
Data Acquisition -> Clean Data ->Data Analysis -> Data Modeling ->Deployment and optimization
Clean Data ->Data Analysis -> Data Modeling ->Deployment and optimization -> Data Acquisition
Data Modeling ->Data Acquisition -> Clean Data ->Data Analysis ->Deployment and optimization
In Data Acquisition stage which deals with Type and Source of Data, following data sources are correct.
User and Inventory data from transaction databases.
Social Engagement from Social Networks like Facebook Twitter.
Training Data from from tools like CrowdFlower, Mechanical Turk.
Customer Support data from Call Logs, Emails
Data Science is
The science of creating data.
It is a branch of Social Studies.
Multidisciplinary study of data collections for analysis, prediction, learning and prevention.
It is a specialized field of study under Artificlal Intelligence
Steps in Data Science
Data Modeling ->Data Acquisition -> Clean Data ->Data Analysis ->Deployment and optimization
Data Acquisition -> Clean Data ->Data Analysis -> Data Modeling ->Deployment and optimization
Clean Data ->Data Analysis -> Data Modeling ->Deployment and optimization -> Data Acquisition
Data Modeling ->Data Acquisition -> Clean Data ->Data Analysis ->Deployment and optimization
In Data Acquisition stage which deals with Type and Source of Data, following data sources are correct.
User and Inventory data from transaction databases.
Social Engagement from Social Networks like Facebook Twitter.
Training Data from from tools like CrowdFlower, Mechanical Turk.
Customer Support data from Call Logs, Emails
Data integration involves combining data residing in different sources and providing users with a unified view of these data.
True
False
Data are often incomplete, incorrect. Some of possible values in incorrect data can be
Typo : e.g., text data in numeric fields
Out-of-Range Values: e.g., age=1000
Missing Values : some fields may not be collected for some of the examples
Extra spaces in text data.
In Data Analysis stage of Data preparation following are correct.
Univariate Analysis: Analyze/explore variables one by one
Bivariate Analysis: Explore relationship between variables
Statistical Analysis: Deriving inferences from mean,median and mode.
Feature Engineering: Variable transformations and creation of new better variables from raw features.
Predictive data modeling involves the collection of data on consumer behaviour to predict future consumer behaviour and to take action accordingly. Following are the valid examples of the same.
Recommendation systems (netflix, pandora, amazon, etc.)
Payroll data in the organization.
Online user behaviour is used to predict best targeted ads
Customer purchase histories are used to determine how to price,stock, market and display future products.
Machine Learning is the study of algorithms that improve their performance at some task with example data or past experience. It is made up of 3 major parts which are
Model ,Parameters and Learner.
Model, Source and Parameters.
Source, Parameters and Algorithms.
Model , Parameters and Algorithms.
Following are the valid examples of Machine learning application.
Association Analysis
Supervised Learning
Unsupervised Learning
Inverse Analysis
Deployed solutions might include:
A trained data model (model + parameters)
Routines for inputting and prediction
Routines for model improvement
Routines for training
Decision Support System.
Which of the following is performed by Data Scientist ?
Define the question
Create reproducible code
Challenge results
All of the Mentioned
Which of the following is most important language for Data Science ?
Java
Ruby
R
None of the mentioned
Which of the following approach should be used to ask Data Analysis question ?
Find only one solution for particular problem
Find out the question which is to be answered
Find out answer from dataset without asking question
None of the mentioned
Which of the following is one of the key data science skill ?
Statistics
Machine Learning
Data Visualization
All of the Mentioned
Which of the following is key characteristic of hacker ?
Afraid to say they don’t know the answer
Willing to find answers on their own
Not Willing to find answers on their own
All of the mentioned
Which of the following is the top most important thing in data science ?
answer
question
data
none of the Mentioned
Which of the following term is appropriate to the above figure ?
Large Data
Big Data
Dark Data
None of the mentioned
Which of the following characteristic of big data is relatively more concerned to data science ?
Velocity
Variety
Volume
None of the Mentioned
Which of the following step is performed by data scientist after acquiring the data ?
Data Integration
Data Replication
Data Cleansing
All of the Mentioned
What is data?
Unprocessed infomation
Set of values
Ideas or objects
All of the choices
What is Big Data?
Data with a large size
Data with the word 'big' in it
Data about people who are big
Data made with a big purpose
What are the 3 main concepts of Data Science?
Data, Science, and Knowledge
Mathematics, Computer Science, and Domain Expertise
Machine Learning, Data Processing, and Statistical Research
What is Data Science comprised of?
Predictive
Machine Learning
Business Intelligence
Personal Opinion
R Programming is compromised of two features. What are they?
Statistical
Programming
Letter Generation
Testing
Can be described as huge amount of data
Volume
Variety
Value
Velocity
Veracity
Can be described as different formats of data from various sources
Volume
Value
Veracity
Variety
Velocity
Can be described with extracting useful data
Volume
Value
Veracity
Variety
Velocity
Can be described as the high speed of accumulation of data
Volume
Variety
Value
Velocity
Veracity
Can be described as inconsistencies and uncertainty in data
Volume
Value
Veracity
Variety
Velocity
The process of evaluating data through analytical and statistical tools
Data Mining
Data Analysis
Data Exploration
Data Visualization
Which was not mentioned as a latest trend tool
SPSS
Excel
Pentaho
Notepad
So why use R programming?
Open Source
I like the letter R
Because I was told to
R extensions and R algorigthms
Which companies were shown using R?
Which of the following approach should be used to ask Data Analysis question ?
Find only one solution for particular problem
Find out the question which is to be answered
Find out answer from dataset without asking question
None of the mentioned
Which of the following is the top most important thing in data science ?
answer
statistical question
data
none of the Mentioned
Q1. Which of the following is the most accurate definition of data science?
a) Data science is extracting meaning from large data sets in order to provide insights to support decision-making
b) Data science is using computers to analyse data and to perform calculations on the data to produce information
c) Data science is performing experiments and recording the data produced by those experiments
d) Data science is writing code to make sure that any inaccuracies in data sets are spotted and removed (cleaned)
Data science is the process of learning about the world using data and computation.
Exploring data sets
Using statistics
Creating data visualizations
Identifying patterns in data
Making predictions
True
False
____can be answered simply by looking up a single value in a dataset. These do not qualify as statistical questions.
Relate Question .
Compute questions:
Lookup questions
What are observations that are measured or counted called?
qualitative data
quantitative data
What are observations made using our five senses called?
qualitative data
quantitative data
The flower is 8 cm tall.
qualitative data
quantitative data
The center of the flower is yellow and fuzzy.
qualitative data
quantitative data
The data science life cycle is a sequence of steps for processing and using data.
True
False
Justin is formulating some statistical questions that can be answered with data. Which stage of data cycle is he going through?
Ask Question
Consider Data
Analyze Data
Interpret Data
Adil is collecting data records, or finding an existing dataset. Which stage of data cycle is he going through?
Ask Question
Consider Data
Analyze Data
Interpret Data
Ahmad is performing statistical analysis, running calculations and/or create data displays to identify patterns and relationships? What is this stage of data cycle?
Ask Question
Consider Data
Analyze Data
Interpret Data
A teacher, after looking at test results, decided to reteach the topic and allow the students retake the test so majority would pass. What stage of data cycle made her make this decision
Interpret Data
Ask Questions
Consider Data
Analyze Data
