Data Science and Machine Learning Interview Questions Using Python

A Complete Question Bank to Crack Your Interview

Vishwanathan Narayanan

• Publisher: BPB Publications
• Year: 2020
• eBook ISBN: 9789389845785
• Topic: Computer Science
• Subtopic: Programming in Python
• Index: Computer Science

Know Data science with numpy, pandas, scipy, sklearn Key Features

• Questions related to core/basic Python, Excel, basic and advanced statistics are included
• Book will prove to be a companion whenever you want to go for an interview
• Simple to use words have been used in the answers for the questions to help ease of remembering
• 
  Description
  "Data science and Machine learning interview questions using Python, " a book which is a true companion of people aspiring for data science and machine learning, and it provides answers to most asked questions in an easy to remember and presentable form.Book mainly intended to be used as last-minute revision, before the interview, as all the important concepts and various terminologies have been given in a very simple and understandable format. Many examples have been provided so that the same can be used while giving answers in an interview.The book is divided into six chapters, which starts with the Data Science Basic Questions and Terms then covers the questions related to Python Programming, Numpy, Pandas, Scipy, and its Applications, then at the last covers Matplotlib and Statistics with Excel Sheet. What will you learn
  
• You can learn the basic concept and terms related to Data Science, python programming
• You will get to learn how to program in python, basics of Numpy
• You will get familiarity with the questions asked in an interview related to Pandas and learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet
• 
  Who this book is for
  The book is mainly intended to help people represent their answer in a sensible way to the interviewer. The answers have been carefully rendered in a way to make things quite simple and yet represent the seriousness and complexity of the matter. Since data science is incomplete without mathematics, we have also included a part of the book dedicated to statistics. Table of Contents
  1. Data Science Basic Questions and Terms
  2. Python Programming Questions
  3. Numpy Interview Questions
  4. Pandas Interview Questions
  5. Scipy and its Applications
  6. Matplotlib Samples to Remember
  7. Statistics with Excel Sheet About the Author
  Vishwanathan has twenty years of hard code experience in the software industry spanning across many multinational companies and domains. Playing with data to derive meaningful insights has been his domain, and that is what took him towards data science and machine learning.
  

CHAPTER 1

Data Science Basic Questions and Terms

Note: [Q: Question Number and Ans: Answer]

Q1: 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 histogram for example.
6. Fit a model from analyzed data.
7. Accept or reject the hypothesis.
8. Research question answer found.

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:
   “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. 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.

   

6. Fit the model.
7. Hypothesis accepted or rejected.

Q2: Explain variable and different types of variables?

Ans: Anything which keeps on changing is called variable. Variables are of different type and below are the following:

Dependant/Outcome: A variable being affected, for example annual temperature in above example.

Independent/Predictor: A variable affecting the outcome for e.g. deforestation, pollution, and so on in above example.

Q3: Explain Categorical measurement?

Ans: Categorical measurement contains categories i.e. distinct entities. Example of categories of life on earth is plants, animals, and so on.

Q4: Explain Binary variables?

Ans: Binary variables are those in which only two classes exist, like live or dead male or female on or off.

Q5: Explain Nominal measurement?

Ans: Nominal measurements are there more than two classes. Such categories can be numbers too.

Q6: Explain Ordinal variable?

Ans: These are nominal variables which have logical order. Examples include team ranks in cricket or football, merit list of students appearing for grade students.

Q7: Explain Continuous variables?

Ans: These are variables which can take can any value on the measurement scale example includes pitch of voice which can take any possible value within the range.

Q8: Explain Discrete variables?

Ans: These are variables which can take fixed values in range. For example, number of customers in a bank.

Q9: 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 for example, Level of water in 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 in nature.

Q10: What are interval variables?

Ans: These are variables which are grouped on interval. Example is age can be divided in range like 10-20, 20-30 and so on and, person with particular age would be placed in one of the above groups. When intervals are equal, they represent difference in equal property being measured.

Q11: What are ratio variables?

Ans: This is sub type of interval variables where ratio of scales is used for measurement.

For Example 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.

Q12: What are Univariate and Bivariate variables?

Ans: Univariate variable: When the variable under consideration is only one then it is called univariate variable study.

Bivariate variable: Involves study of relationship between two variables.

Q13: What is measurement error?

Ans: The discrepancy between the measured value and actual value in terms of number is called measurement error.

For Example 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 shows 0.1 kilogram less than expected then this difference is what we call as measurement error.

Q14: Explain Validity?

Ans: Validity implies whether an instrument measures what it is supposed to measure.

Q15: Explain Reliability?

Ans: Reliability implies whether the instrument gives consistent result across different conditions.

For example, if we test the same value twice on the same entity then the results from the instrument should remain same if it has to be reliable. Such tests are known as test-retest.

Q16: What are the 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 variables of interest
   • Then we manipulate some aspect of the environment
   • Observe the effect on selected variable

Q17: Explain the different types of variation?

Ans: There are two types in variation explained as follows:

Systematic variation:

• Introduced by experimenter
• The participants are tested under different conditions and the difference in condition is i...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication
• About the Author
• Preface
• Foreword
• Table of Contents
• 1. Data Science Basic Questions and Terms
• 2. Python Programming Questions
• 3. Numpy Interview Questions
• 4. Pandas Interview Questions
• 5. Scipy and its Applications
• 6. Matplotlib Samples to Remember
• 7. Statistics with Excel Sheet