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A Collection Of Data Science Interview Questions Solved In Python And Spark Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of A Collection Of Data Science Interview Questions Solved In Python And Spark book. This book definitely worth reading, it is an incredibly well-written.
Author : Antonio Gulli
Publisher : CreateSpace
Page : 84 pages
File Size : 48,9 Mb
Release : 2015-09-22
Category : Electronic
ISBN : 1517216710
BigData and Machine Learning in Python and Spark
Author : Antonio Gulli
Publisher : Createspace Independent Publishing Platform
Page : 106 pages
File Size : 41,5 Mb
Release : 2015-11-18
Category : Electronic
ISBN : 1518678645
A collection of Machine Learning interview questions in Python and Spark
Author : Antonio Gulli
Publisher : Createspace Independent Publishing Platform
Page : 198 pages
File Size : 44,9 Mb
Release : 2016-07-02
Category : Electronic
ISBN : 1534795715
Special Edition Data Science and Machine Learning Interview Questions Solved in Python and Spark with Deep Learning and Reinforcement Learning Bonus Questions
Author : Vishwanathan Narayanan
Publisher : BPB Publications
Page : 491 pages
File Size : 50,6 Mb
Release : 2020-05-08
Category : Computers
ISBN : 9789389845785
ÊKnowÊ Data science with numpy, pandas, scipy, sklearn DESCRIPTION ÒData science and Machine learning interview questions using Python,Ó 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. Ê 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 Ê 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 Ê 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.Ê Ê 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
Author : Vishwanathan Narayanan
Publisher : BPB Publications
Page : 122 pages
File Size : 50,8 Mb
Release : 2020-06-23
Category : Computers
ISBN : 9789389845846
Get answers to frequently asked questions on Data Science and Machine Learning using R KEY FEATURESÊÊ - 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 DESCRIPTIONÊÊ This book prepares you for the Data Scientist and Machine Learning Engineer interview w.r.t. R programming language.Ê 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 LEARNÊ - 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 FORÊ 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 CONTENTSÊÊ 1. Data Science basic questions and terms 2. R programming questions 3. GGPLOT Questions 4. Statistics with excel sheet
Author : Zack Austin
Publisher : Lulu.com
Page : 119 pages
File Size : 50,8 Mb
Release : 2017-12-09
Category : Databases
ISBN : 9781387431960
Here's what you get in this book: - 300 practice questions and answers spanning the breadth of topics under the data science umbrella - Covers statistics, machine learning, SQL, NoSQL, Hadoop and bioinformatics - Emphasis on real-world application with a chapter on Python libraries for machine learning - Focus on the most frequently asked interview questions. Avoid information overload - Compact format: easy to read, easy to carry, so you can study on-the-go Now, you finally have what you need to crush your data science interview, and land that dream job. About The Author Zack Austin has been building large scale enterprise systems for clients in the media, telecom, financial services and publishing since 2001. He is based in New York City.
Author : Sheela Evangeline,M.R. Rajkumar,Saritha G. Parambath
Publisher : CRC Press
Page : 512 pages
File Size : 49,8 Mb
Release : 2019-05-14
Category : Technology & Engineering
ISBN : 9781351227537
These proceedings present a selection of papers presented at the 3rd International Conference on Materials Mechanics and Management 2017 (IMMM 2017), which was jointly organized by the Departments of Civil Engineering, Mechanical Engineering and Architecture of College of Engineering Trivandrum. Developments in the fields of materials, mechanics and management have paved the way for overall improvements in all aspects of human life. The quest for meeting the requirements of the rapidly increasing population has led to revolutionary construction and production technologies aiming at optimum management and use of natural resources. The objective of this conference was to bring together experts from academic institutions, industries, research organizations and professionals for sharing of knowledge, expertise and experience in the emerging trends related to Civil Engineering, Mechanical Engineering and Architecture. IMMM 2017 provided opportunities for young researchers to actively engage in research discussions, new research interests, research ethics and professional development.
Author : Jonathan Rioux
Publisher : Simon and Schuster
Page : 716 pages
File Size : 48,8 Mb
Release : 2022-04-12
Category : Computers
ISBN : 9781638350668
Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the reader Written for data scientists and data engineers comfortable with Python. About the author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Table of Contents 1 Introduction PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK 2 Your first data program in PySpark 3 Submitting and scaling your first PySpark program 4 Analyzing tabular data with pyspark.sql 5 Data frame gymnastics: Joining and grouping PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE 6 Multidimensional data frames: Using PySpark with JSON data 7 Bilingual PySpark: Blending Python and SQL code 8 Extending PySpark with Python: RDD and UDFs 9 Big data is just a lot of small data: Using pandas UDFs 10 Your data under a different lens: Window functions 11 Faster PySpark: Understanding Spark’s query planning PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK 12 Setting the stage: Preparing features for machine learning 13 Robust machine learning with ML Pipelines 14 Building custom ML transformers and estimators
Author : Ivan Marin,Ankit Shukla,Sarang VK
Publisher : Packt Publishing Ltd
Page : 276 pages
File Size : 49,7 Mb
Release : 2019-04-10
Category : Computers
ISBN : 9781789950731
Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. Key FeaturesGet a hands-on, fast-paced introduction to the Python data science stackExplore ways to create useful metrics and statistics from large datasetsCreate detailed analysis reports with real-world dataBook Description Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or querying formatsPrepare data for statistical analysis, visualization, and machine learningPresent data in the form of effective visualsWho this book is for Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.
Author : Denny Lee,Tomasz Drabas
Publisher : Packt Publishing Ltd
Page : 321 pages
File Size : 54,9 Mb
Release : 2018-06-29
Category : Computers
ISBN : 9781788834254
Combine the power of Apache Spark and Python to build effective big data applications Key Features Perform effective data processing, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for efficiently combining Python and Apache Spark to process data Book Description Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications. What you will learn Configure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environments Create DataFrames from JSON and a dictionary using pyspark.sql Explore regression and clustering models available in the ML module Use DataFrames to transform data used for modeling Connect to PubNub and perform aggregations on streams Who this book is for The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.
Author : Richard Wilson
Publisher : Unknown
Page : 248 pages
File Size : 48,5 Mb
Release : 2019-09-16
Category : Electronic
ISBN : 1693541378
★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★Data Science is present in our lives: newspapers talk about viral news, companies look for data scientists, businesses offer us personalized offers based on our customs and we grease the system by offering free personal information from our social networks, Internet searches and even from smart devices to control our daily physical activity.This book presents the knowledge and technologies that will allow us to participate in this new era of information, governed by Big Data and machine learning, the life of the data is analyzed step by step, showing how to obtain it, store it, process it, visualize it, and draw conclusions from it: that is, show the data analysis as it is: a fascinating area, It requires many hours of careful work. Likewise, the Python programming language is analyzed, the most used in data Science due to the multitude of libraries that it facilitates, but is not limited to the standard, but presents current technologies that, with Python as an interface, will allow scaling the size of the data to the maximum. Therefore, our journey with the data will take us, for example, to know the Mongo DB database and the Spark processing environment.In this book, you will discover: What is a data scientist?What languages should be learned?The three musketeers of Data SciencePython introductionLanguages do you need to learn for data scienceThese are some of the topics covered in this book: Machine Learning AlgorithmsK NN - Nearest Neighbor MethodSVC - Support vector machineMathematics for Data AnalysisWorking with Threads in PythonWorking with processes in PythonThe book contains detailed examples of how to perform the different tasks in Python; and in addition, for the convenience of the reader of the included fragments, the access of the readers to a repository where they will find the code ready to be executed is facilitated. Also each chapter presents recommended readings to be able to deepen in those aspects that are more interesting. We invite you to immerse yourself in the exciting world of data Science in Python and explore the mysteries of Big Data and machine learning!Get fit, happy, and stress-free life by ordering your copy right away! also, Don't miss out on this Data Science from Scratch with Python!Just Scroll Up and Click the Buy Now Butto
Author : Vishwanathan Narayanan
Publisher : Unknown
Page : 0 pages
File Size : 53,7 Mb
Release : 2020
Category : Databases
ISBN : OCLC:1249446511
Presenting the important concepts and various terminologies in a very simple and understandable format, this book provides answers to most asked questions in data science and machine learning interviews. --
Author : Isaac Triguero,Mikel Galar
Publisher : Cambridge University Press
Page : 395 pages
File Size : 45,6 Mb
Release : 2023-11-30
Category : Computers
ISBN : 9781009318259
A hands-on textbook for courses on large-scale data analytics and designing machine learning solutions.
Author : Mahmoud Parsian
Publisher : "O'Reilly Media, Inc."
Page : 438 pages
File Size : 54,7 Mb
Release : 2022-04-08
Category : Computers
ISBN : 9781492082354
Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns
Author : Aman Kharwal
Publisher : Thecleverprogrammer
Page : 45 pages
File Size : 41,7 Mb
Release : 2021-06-28
Category : Computers
ISBN : 8210379456XXX
This book is specially written for those who know the basics of the Python programming language as well as the necessary Python libraries you need for data science like NumPy, Pandas, Matplotlib, Seaborn, Plotly, and Scikit-learn. This book aims to teach you how to think while solving a business problem with your data science skills. To achieve the goal of this book, I started by giving you all the knowledge you need to have before you apply for your first data science job. The technical skills and soft skills you need to become a Data Scientist are also discussed in this book. Next, you'll find some of the best data science case studies that will help you understand what your approach should be while solving a business problem. Ultimately, you will also find some of the most important data science interview questions with their solutions at the end. I hope this book will add a lot of value to your data science skills and that you will feel confident in your entire journey to become Data Scientist.
