Data Science and Machine Learning Interview Questions Using R

Data Science and Machine Learning Interview Questions Using R

Vishwanathan Narayanan

• Publisher: BPB Publications
• Year: 2020
• ISBN:
  9789389845846
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Get answers to frequently asked questions on Data Science and Machine Learning using R Key Features

• Understand the capabilities of the R programming language
• Most of the machine learning algorithms and their R implementation covered in depth
• Answers on conceptual data science concepts are also covered
• 
  Description
  This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language.
  The book is divided into various parts, making it easy for you to remember and associate with the questions asked in an interview. It covers multiple possible transformations and data filtering techniques in depth. You will be able to create visualizations like graphs and charts using your data. You will also see some examples of how to build complex charts with this data. This book covers the frequently asked interview questions and shares insights on the kind of answers that will help you get this job.
  By the end of this book, you will not only crack the interview but will also have a solid command of the concepts of Data Science as well as R programming. What will you learn
  
• Get answers to the basics, intermediate and advanced questions on R programming
• Understand the transformation and filtering capabilities of R
• Know how to perform visualization using R
• 
  Who this book is for
  This book is a must for anyone interested in Data Science and Machine Learning. Anyone who wants to clear the interview can use it as a last-minute revision guide. Table of Contents
  1. Data Science basic questions and terms
  2. R programming questions
  3. GGPLOT Questions
  4. Statistics with excel sheet About the Author
  Vishwanathan Narayanan has 18 years of experience in the field of information technology and data analysis. He made many enterprise-level applications with stable output and scalability.Advanced level data analysis for complex problems using both R and Python has been the key area of work for many years. Extreme programmer on Java, Python, R, and many more technologies
  

SECTION 1

Data Science Basic Questions and Terms

Learning objective

In this session, we will learn about data science terminologies and machine learning.

Key points

• Steps involved in data science
• Variables and types
• Machine learning and types
• Algorithms used in Machine learning

Let us begin!

1. Explain the steps involved in data science?
   Ans. Following are the steps involved:
   1) Get Data from various Data sources available
   2) Generate research question from data
   3) Identify variables present in data. Also, identify important variables or variables to be analyzed as such
   4) Generate hypothesis
   5) Analyze data using graph data like a histogram for example
   6) Fit a model from analyzed data
   7) Accept or reject the hypothesis
   8) Research question answer found

   

   Figure 1.1: Steps involved in data science
   Example of above steps:
   1) Get data related to temperature for India reference https://data.gov.in/catalog/annual-and-seasonal-maximum-temperature-india
   A template of data set is as follows:
   “YEAR“, “ANNUAL“, “JAN-FEB“, “MAR-MAY“, “JUN-SEP“, “OCT-DEC“
   “1901“, “28.96“, “23.27“, “31.46“, “31.27“, “27.25“
   “1902“, “29.22“, “25.75“, “31.76“, “31.09“, “26.49“
   “1903“, “28.47“, “24.24“, “30.71“, “30.92“, “26.26“
   “1904“, “28.49“, “23.62“, “30.95“, “30.67“, “26.40“
   “1905“, “28.30“, “22.25“, “30.00“, “31.33“, “26.57“
   “1906“, “28.73“, “23.03“, “31.11“, “30.86“, “27.29“
   “1907“, “28.65“, “24.23“, “29.92“, “30.80“, “27.36“
   “1908“, “28.83“, “24.42“, “31.43“, “30.72“, “26.64“
   “1909“, “28.39“, “23.52“, “31.02“, “30.33“, “26.88“
   “1910“, “28.53“, “24.20“, “31.14“, “30.48“, “26.20“
   “1911“, “28.62“, “23.90“, “30.70“, “31.14“, “26.31“
   “1912“, “28.95“, “24.88“, “31.10“, “31.15“, “26.57“
   “1913“, “28.67“, “24.25“, “30.89“, “30.92“, “26.42“
   “1914“, “28.66“, “24.59“, “30.73“, “30.84“, “26.40“
   “1915“, “28.94“, “23.22“, “31.06“, “31.51“, “27.18“
   “1916“, “28.82“, “24.57“, “31.88“, “30.52“, “26.32“
   “1917“, “28.11“, “24.52“, “30.06“, “30.24“, “25.74“
   “1918“, “28.66“, “23.57“, “30.68“, “31.11“, “26.77“
   2) The research question is the annual temperature in India rising?
   3) Variable of interest from the above data set ANNUAL
   4) Hypothesis: Temperature is rising
   5) Analyze data from the above data set:

   

   Figure 1.2: Graph showing year vs. temperature
   6) Fit the model
   7) Hypothesis accepted or rejected
2. Define a variable?
   Ans. Anything which keeps on changing is called variable.
3. Explain different types of variables?
   Ans. Variables are of the following type:
   • Dependant/Outcome: A variable being affected for example annual temperature in the above example.
   • Independent/Predictor: A variable affecting the outcome e.g. deforestation, pollution, and so on in the above example.
4. Define Categorical measurement?
   Ans. Categorical measurement contains categories i.e. distinct entities. Example of categories of life on earth is plants, animals, and so on.
5. Define Binary variables?
   Ans. Binary variables are those in which only two classes exist like live or dead male or female on or off.
6. Define Nominal measurement?
   Ans. Nominal measurements are those of more than two classes. Such categories can be numbers too.
7. Explain the Ordinal variable?
   Ans. These are nominal variables that have a logical order. Examples include team ranks in cricket or football, merit list of students appearing for grade students.
8. Define Continuous variables?
   Ans. These are variables that can take can any value on the measurement scale example includes pitch of voice which can take any possible value within the range.
9. Define Discrete variables?
   Ans. These are variables that can take fixed values in the range. Example number of customers in a bank.
10. Is it possible to convert continuous values to discrete and vice versa?
    Ans. Yes based upon the motive of study it is possible to convert discrete values to continuous and vice versa. Example Level of water in the tank can take any value in the range and as such a continuous variable.
    But we can approximate the same to three different levels like empty, full, or half empty and this now becomes discrete.
11. Explain the interval variables?
    Ans. These are variables that are grouped on the interval. E.g. is age can be divided into the range like 1-10, 10-20, 20-30, and so on and the person with a particular age would be placed in one of the above groups. When intervals are equal they represent the difference in the equal property being measured.
12. Explain the ratio variables?
    Ans. This is a subtype of interval variables where the ratio of scales is used for measurement.
    E.g. Water representation in chemistry is H2O which represent two molecules of hydrogen and one molecule of oxygen. Thus the ratio of elements is 2: 1.
13. Define Univariate and Bivariate variables?
    Ans. Following are the definitions:
    • Univariate variable: When the variable under consideration is only one then it is called a univariate variable study.
    • Bivariate variable: Involves the study of the relationship between two variables.
14. Explain the measurement error?
    Ans. The discrepancy between the measured value and actual value in terms of number is called measurement error.
    E.g. While buying fruits from a vendor in kilograms, if we wanted 1 kilogram of fruits and the vendor’s weighing machine showed 1 kilogram when we brought the same. After checking the same in another machine if the measured value show 0.1 kilograms less than expected then this difference is what we call as measurement error.
15. Define Validity?
    Ans. Validity implies whether an instrument measures what it is supposed to measure.
16. Explain Reliability?
    Ans. Reliability implies whether the instrument gives consistent results across different conditions.
    E.g. if we test the same value twice on the same entity then the results from the instrument should remain the same if it has to be reliable. Such test is known as test-retest.
17. Explain different ways to test hypotheses?
    Ans. There are two ways in which hypotheses can be tested
    1) Correlational research:
    • This is also known as cross-sectional research
    • This involves observing the natural pattern or occurrence to test
    • Original occurrences are not manipulated
    2) Experimental research:
    • We select the variabl...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication
• About the Author
• Foreword
• About the Reviewer
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Data Science Basic Questions and Terms
• 2. Programming Questions
• 3. GGPLOT Questions
• 4. Statistics with Excel Sheet