Data Science with Machine Learning

Python Interview Questions

Vishwanathan Narayanan

• Publisher: BPB Publications
• Year: 2019
• ISBN:
  9789388176637
• Topic: Computer Science
• Subtopic: Programming in Python
• Index: Computer Science

For beginners to level up Core Programming Skills Key Features

• Easy to learn, step by step explanation of examples.
• Questions related to core/basic Python, Excel, basic and advanced statistics are included.
• Covers numpy, scipy, sklearn and pandas to a greater detail with good number of examples

Description
The book "Data science with Machine learning- Python interview questions" is a true companion of people aspiring for data science and machine learning and provides answers to mostly asked questions in a easy to remember and presentable form.
Data science is one of the hottest topics mainly because of the application areas it is involved and things which were once upon of time, impossible with earlier software has been made easy. This book is mainly intended to be used as last-minute revision, before interview, as all the important concepts have been given in simple and understand format. Many examples have been provided so that same can be used while giving answers in interview.
This book tries to include various terminologies and logic used both as a part of Data Science and Machine learning for last minute revision. As such you can say that this book acts as a companion whenever you want to go for interview.
Simple to use words have been used in the answers for the questions to help ease of remembering and representation of same. Examples where ever deemed necessary have been provided so that same can be used while giving answers in interview. Author tried to consolidate whatever he came across, on multiple interviews that he attended and put the same in words so that it becomes easy for the reader of the book to give direction on how the interview would be.
With the number of data science jobs increasing, Author is sure that everyone who wants to pursue this field would like to keep this book as a constant companion. What Will You Learn

• You can learn the basic concept and terms related to Data Science
• You will get to learn how to program in python
• You can learn the basic questions of python programming
• By reading this book you can get to know the basics of Numpy
• You will get familiarity with the questions asked in interview related to Pandas.
• You will learn the concepts of Scipy, Matplotib, and Statistics with Excel Sheet

Who This Book Is For
The book is intended for anyone wish to learn Python Data Science, Numpy, Pandas, Scipy, Matplotib and Statistics with Excel Sheet. This book content also covers the basic questions which are asked during an interview. This 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 matter. Since data science is incomplete without mathematics we have also included a part of the book dedicated to statistics. Table of Contents

• Data Science Basic Questions and Terms
• Python Programming Questions
• Numpy Interview Questions
• Pandas Interview Questions
• Scipy and its Applications
• Matplotlib Samples to Remember
• Statistics with Excel Sheet
• About the Author
  Mr Vishwanathan has twenty years of hard code experience in 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.
  

Data Science basic questions
and terms

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

Q 1:

Explain the steps involved in data science?

A:

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.

Q 2:	Explain variable and different types of variables?
A:	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.
Q 3:	Explain Categorical measurement?
A:	Categorical measurement contains categories i.e. distinct entities. Example of categories of life on earth is plants, animals, and so on.
Q 4:	Explain Binary variables?
A:	Binary variables are those in which only two classes exist, like live or dead male or female on or off.
Q 5:	Explain Nominal measurement?
A:	Nominal measurements are there more than two classes. Such categories can be numbers too.
Q 6:	Explain Ordinal variable?
A:	These are nominal variables which have logical order. Examples include team ranks in cricket or football, merit list of students appearing for grade students.
Q 7:	Explain Continuous variables?
A:	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.
Q 8:	Explain Discrete variables?
A:	These are variables which can take fixed values in range. For example, number of customers in a bank.
Q 9:	Is it possible to convert continuous values to discrete and vice versa?
A:	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, ful...

Table of contents

• Cover
• Data Science with Machine Learning – Python Interview Questions
• Copyright
• Preface
• Foreword
• Dedication
• Table of Contents
• Data Science Basic Questions and Terms
• Python Programming Questions
• Numpy Interview Questions
• Pandas Interview Questions
• Scipy and its Applications
• Matplotlib Samples to Remember
• Statistics with Excel Sheet