Data Science and Machine Learning Interview Questions Using R
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Get answers to frequently asked questions on Data Science and Machine Learning using R Key Features a- Understand the capabilities of the R programming language a- Most of the machine learning algorithms and their R implementation covered in depth a- 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 a- Get answers to the basics, intermediate and advanced questions on R programming a- Understand the transformation and filtering capabilities of R a- 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



Publié par
Date de parution 03 septembre 2020
Nombre de lectures 1
EAN13 9789389845853
Langue English

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Data Science and Machine Learning Interview Questions Using R

Crack the Data Scientist and Machine Learning Engineers Interviews with Ease

Vishwanathan Narayanan
Copyright © BPB Publications, India
ISBN: 978-93-89845-846
All Rights Reserved. No part of this publication may be reproduced or distributed in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication.
The information contained in this book is true to correct and the best of author’s & publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but cannot be held responsible for any loss or damage arising from any information in this book.
All trademarks referred to in the book are acknowledged as properties of their respective owners.
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Dedicated to
Dedicated to Goddess (Pratyangira, Bala, Durga), Mom, Dad, my aunt (Vijaya Chitti), my sister Ishwarya, Sridhar, my brother-in-law and to all my mentors, especially Shiv without whom this book would still be a dream. Also, the support extended by Shyam Sir, Khadak, and BPB Publications is very much appreciated. My niece Durga has been a great inspiration for this book. She has always been and will be my encouragement to write more books. Also, remember Sudarshan as a friend in need. Also it is dedicated to my students from whom I equally learned as I taught them. Along with all the blessing of almighty are also remembered here without which even a blade of grass does not move
About the Author
Vishwanathan Narayanan is an extreme programmer in various technologies, including Java, Python, and R, and has around 18 years of experience in the field of information technology and data science. Exposed to real-world data science and advanced analytics using big data technologies gives him a great advantage, which he tries to impart by using books.
As a passionate teacher, he likes writing books as a hobby.
It is not wrong to say that today’s dynamic world is driven totally by statistics. With decision making becoming important in being successful, the use of software, this task has become common, Thanks to the advancement made with respect to technology. While software applications always existed for doing the above task, the volume and ability of software programs to represent complex equations related to statistics and probability was limited. Thanks to R programming, the above problem faced has been removed to a great extent, and the problem is no more a challenge. With complex mathematical concepts easily convertible to algorithms, the life of data scientists and analysts has become quite easy. 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 the matter. Since data science is incomplete without mathematics, we have also included a part of the book dedicated to statistics. R has already taught us that small code does not mean lesser powerful the same concept has been adopted to keep the book a powerful weapon for anyone attending the interview.
About the Reviewer
Kaustubh has 11 years of experience in Architect solutioning for key business initiatives ensuring alignment with future state analytics architecture vision. He provides overall architect responsibilities including roadmaps, leadership, planning, technical innovation, security, IT governance, etc. Acting as a senior architect, provide technical and process leadership for projects, defining and documenting information integrations between systems and aligning project goals with reference architecture. He is a lead end-to-end Hadoop implementation at large enterprise environment integrating with multiple legacy applications in heterogeneous technologies
He is a professional engineer, enthusiast programmer, emerging data scientist and machine learning student. He is having total 5+ years of Industry & research experience in the field of Big Data / Data Science / Predictive Modeling.
First and foremost, I would like to thank God for giving me the courage to write this book. I would like to thank everyone at BPB Publications for giving me this opportunity to publish my book.
My hearty thanks to Kaustubh, who did a review of my book.
Also, I would like to thank Shiv Sir and Sukesh Sir for this immense trust in me.
My biggest inspiration is my Durga, who always encourages me to do something new. Also, remember the kind guidance given by Sridharji and Ishwarya on these books.
Vishwanathan Narayanan
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 tries to comprehend the ocean of data science into a small book, which is mainly intended to be used as a last-minute revision. Before the interview, all the important concepts have been given in a simple and understandable format. 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 an interview. Simple to use words have been used in the given answers for the questions to help ease of remembering and representation of same. Examples where ever deemed necessary have been provided so that the same can be used while giving answers in the interview. The 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, the author is sure that everyone who wants to pursue this field would like to keep this book as a constant companion. Soon, the author will be coming shortly with a new book on Big data, too, so that it makes a complete data science stack. Happy reading to all the readers, your feedback is highly appreciated.
The book is divided basically into various sections like following:
Section 1: Deals with concepts and definitions around data science
Section 2: R programming questions from basics to advanced
Section 3: Covers plotting using R ggplot libraries
Section 4: Deals with Excel related questions
Downloading the code bundle and coloured images:
Please follow the link to download the Code Bundle and the Coloured Images of the book:
We take immense pride in our work at BPB Publications and follow best practices to ensure the accuracy of our content to provide with an indulging reading experience to our subscribers. Our readers are our mirrors, and we use their inputs to reflect and improve upon human errors if any, occurred during the publishing processes involved. To let us maintain the quality and help us reach out to any readers who might be having difficulties due to any unforeseen errors, please write to us at :
Your support, suggestions and feedbacks are highly appreciated by the BPB Publications’ Family.
Table of Contents
1. Data Science Basic Questions and Terms
Learning objective
Key points
2. Programming Questions
Learning highlights
Key points
3. GGPLOT Questions
Learning objective
Key points
4. Statistics with Excel Sheet
Learning objectives
Key points
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! 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
A template of data set is as follows:
“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.

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