20 Best Analytics Books (2023 Update)

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in python. updated for python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. you'll learn the latest versions of pandas, numpy, ipython, and jupyter in the process. – written by wes mckinney, the creator of the python pandas project, this book is a practical, modern introduction to data science tools in python. it's ideal for analysts new to python and for python programmers new to data science and scientific computing. data files and related material are available on github.use the ipython shell and jupyter notebook for exploratory computing – learn basic and advanced features in numpy (numerical python)get started with data analysis tools in the pandas library – use flexible tools to load, clean, transform, merge, and reshape data – create informative visualizations with matplotlib – apply the pandas groupby facility to slice, dice, and summarize datasets – analyze and manipulate regular and irregular time series data – learn how to solve real-world data analysis problems with thorough, detailed examples

Deliver enterprise data analytics success by following prashanth's prescriptive and practical techniques. this book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. it is intended for anyone who has a stake and interest in deriving insights from data analytics. the three key differentiating aspects of this book are: practicality . prescriptive and practical guidance. completeness . holistic view across the four key data analytics domains – data management, data engineering, data science, and data visualization. neutrality . technologically agnostic without any reference to commercial analytics products and technologies. dr. southekal proves why he is one of the leading thinkers on data and analytics today. 'analytics best practices' is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data. douglas b. laney, principal, data & analytics strategy, caserta, and author of "infonomics" dr. southekal's book is a treasure trove of best practices and practical examples from the field of data analytics. upen varanasi, ceo and co-founder, riversand technologies inc. what i like about this book is its focus on real-world best practices with an extensive set of practical tips and tricks. it provides an important bridge between the data management and business operations sides of a business. michael wade, professor of innovation and strategy, imd business school prashanth's book is accessible and practical – an excellent guide for corporate leaders who want to produce meaningful business results from the use of data and analytics to create true business value. ram kumar, executive head and senior vice president, quantium dr. prashanth southekal has created a practical guidebook for realizing business value from data and analytics investments. highly recommended. randy bean, founder and ceo, newvantage partners, and contributing author for forbes and harvard business review prashanth's second book starts with the right title – it is always about business value. the practices explored here will help anyone interested to achi …

This book describes, simply and in general terms, the process of analyzing data. the authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

The role of maths and statistics in the world of web analytics is not clear to many marketers. not many talk or write about the usage of statistics and data science in conversion optimization. this book has been written to fill this knowledge gap. this expert guide will teach you exactly what you need to know. it will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions which can quickly improve the bottom-line of your online business. every topic covered in this book has something to do directly with your day to day job. you don't need a math or stats background in order to understand this book. every topic has been explained in plain english. this book covers vital topics on maths and statistics which every internet marketer/web analyst should get familiar with in order to achieve optimum results from their analysis, marketing campaigns and conversion optimization efforts. it explains some of the most useful statistics terms/concepts one by one and will also show you their practical use in web analytics and conversion optimization, so that you can take advantage of them straightaway.

The text seamlessly threads the topics of data wrangling, descriptive analytics, predictive analytics, and prescriptive analytics into a cohesive whole. these revisions are based on the feedback of reviewers and users of our first edition. the greatly expanded coverage of the text gives instructors the flexibility to select the topics that best align with their course objectives

Written for anyone involved in the data preparation process for analytics, gerhard svolba's data preparation for analytics using sas offers practical advice in the form of sas coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from a business point of view. the tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various sas procedures and sas enterprise miner for scoring, creating meaningful derived variables for all data mart types, using powerful sas macros to make changes among the various data mart structures, and more!

Get to grips with pandas – a fast, versatile, and high-performance python library for data discovery, data manipulation, data preparation, and handling data for analytical tasks – key features: perform efficient data analysis and manipulation tasks using pandas 1.x – apply pandas to different real-world domains with the help of step-by-step examples – become well-versed in using pandas as an effective data exploration tool – book description: data analysis has become an essential skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. hands-on data analysis with pandas will show you how to analyze your data, get started with machine learning, and work effectively with the python libraries often used for data science, such as pandas, num – py, matplotlib, seaborn, and scikit-learn. – using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. in the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. – this updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making-valuable knowledge that can be applied across multiple domains. – what you will learn: understand how data analysts and scientists gather and analyze data – combine, group, and aggregate data from multiple sources – create data visualizations with pandas, matplotlib, and seaborn – apply machine learning algorithms to identify patterns and make predictions – use python data science libraries to analyze real-world datasets – solve common data representation and analysis problems using pandas – build python scripts, modules, and packages for reusable analysis code – who this book is for: this book is for data science beginners, data analysts, and python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. you'll also find this book useful if you are a data scientist looking to implement pandas in your machine learning workflow. working knowledge of the python programming language will assist with understanding the key concepts covered in this book; however, a python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

An incredible volume of data is generated at a very high speed within the supply chain and it is necessary to understand, use and effectively apply the knowledge learned from analyzing data using intelligent business models. however, practitioners and students in the field of supply chain management face a number of challenges when dealing with business models and mathematical modelling. supply chain analytics and modelling presents a range of business analytics models used within the supply chain to help readers develop knowledge on a variety of topics to overcome common issues. supply chain analytics and modelling covers areas including supply chain planning, single and multi-objective optimization, demand forecasting, product allocations, end-to-end supply chain simulation, vehicle routing and scheduling models. learning is supported by case studies of specialist software packages for each example. readers will also be provided with a critical view on how supply chain management performance measurement systems have been developed and supported by reliable and accurate data available in the supply chain. online resources including lecturer slides are available.

Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down. this book provides essential guidance to apply advanced analytics and data mining techniques to real-world business applications. the foundation of this text is the author's 20-plus years of developing and delivering big data and artificial intelligence solutions across multiple industries: financial services, pharmaceuticals, consumer packaged goods, media, and retail. he provides guidelines and summarized cases for those studying or working in the fields of data science, data engineering, and business analytics. the book also offers a distinctive style: a series of essays, each of which summarizes a critical lesson or provides a step-by-step business process, with specific examples of successes and failures. sales and marketing executives, project managers, business and engineering professionals, and graduate students will find this clear and comprehensive book the ideal companion when navigating the complex world of big data analytics.

Data pipelines are the foundation for success in data analytics. moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. this pocket reference defines data pipelines and explains how they work in today's modern data stack. – you'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. this book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. – you'll learn: what a data pipeline is and how it works – how data is moved and processed on modern data infrastructure, including cloud platforms – common tools and products used by data engineers to build pipelines – how pipelines support analytics and reporting needs – considerations for pipeline maintenance, testing, and alerting

Incorporating an innovative focus on data visualization and time series forecasting, data mining for business intelligence, third edition continues to supply insightful, detailed guidance on fundamental data mining techniques. this new edition guides readers through the use of the microsoft office excel add-in xlminer (now in a student version) for developing predictive models and techniques in order to describe and find patterns in data. the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods. the book includes free access to the student edition of xlminer, allowing readers to work hands-on with the provided data. throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. over a dozen cases that require use of the different data mining techniques are introduced, and a related web site features over two dozen data sets, exercise solutions, powerpoint slides, and case solutions. new to this edition are topics including text analytics, recommender systems, social network analysis, getting data from a database into the analytics process, and scoring and employing the results of an analysis to a database.

The 2nd edition of r for marketing research and analytics continues to be the best place to learn r for marketing research. this book is a complete introduction to the power of r for marketing research practitioners. the text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. hands-on chapters accelerate the learning curve by asking readers to interact with r from the beginning. core topics include the r language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis.later chapters cover more advanced topics yet are intended to be approachable for all analysts. these sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in r. the text uniquely presents bayesian models with a minimally complex approach, demonstrating and explaining bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. with its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in r for marketing applications.the 2nd edition increases the book’s utility for students and instructors with the inclusion of exercises and classroom slides. at the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.

Optimize your marketing strategies through analytics and machine learning – key features: understand how data science drives successful marketing campaigns – use machine learning for better customer engagement, retention, and product recommendations – extract insights from your data to optimize marketing strategies and increase profitability – book description: regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. with this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. this book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. – this is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. you will understand what drives sales and increases customer engagements for your products. this book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of a/b testing and implement it using python and r.by the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. – what you will learn: learn how to compute and visualize marketing kpis in python and rmaster what drives successful marketing campaigns with data science – use machine learning to predict customer engagement and lifetime value – learn how to use a/b testing for better marketing decision making – implement machine learning to understand different customer segments – who this book is for: if you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! it will be beneficial to have some basic knowledge of either python or r to work through the examples. this book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.

This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. in order for today’s organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. similarly, today’s data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround hr data and hr systems. finally, traditional hr professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing hr analytics functions. and importantly, all these considerations are magnified by the introduction and acceleration of machine learning in hr. this book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. it will also introduce machine learning and where it fits within the larger hr analytics framework by explaining many of its basic tenets and methodologies. by the end of the book, readers will understand the skills required to do advanced hr analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy.

As they illustrate, big data analytics is the engine that drives marketing, providing a forward-looking, predictive perspective for marketing decision-making. the book presents actual cases and data, giving readers invaluable real-world instruction. the cases show how to identify relevant data, choose the best analytics technique, and investigate the link between marketing plans and customer behavior. these actual scenarios shed light on the most pressing marketing questions, such as setting the optimal price for one's product or designing effective digital marketing campaigns.

Lead your organization into the industrial revolution of analytics with the analytics revolution the topics of big data and analytics continue to be among the most discussed and pursued in the business world today. while a decade ago many people still questioned whether or not data and analytics would help improve their businesses, today virtually no one questions the value that analytics brings to the table. the analytics revolution focuses on how this evolution has come to pass and explores the next wave of evolution that is underway. making analytics operational involves automating and embedding analytics directly into business processes and allowing the analytics to prescribe and make decisions. the analytics revolution delves into the requirements for laying a solid technical and organizational foundation that is capable of supporting operational analytics at scale, and covers factors to consider if an organization is to succeed in making analytics operational. along the way, you'll learn how changes in technology and the business environment have led to the necessity of both incorporating big data into analytic processes and making them operational. the book cuts straight through the considerable marketplace hype and focuses on what is really important. the book includes: an overview of what operational analytics are and what trends lead us to them tips on structuring technology infrastructure and analytics organizations to succeed a discussion of how to change corporate culture to enable both faster discovery of important new analytics and quicker implementation cycles of what is discovered guidance on how to justify, implement, and govern operational analytics the analytics revolution gives you everything you need to implement operational analytic processes with big data.

Data science and big data analytics is about harnessing the power of data for new insights. the book covers the breadth of activities and methods and tools that data scientists use. the content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. this book will help you: become a contributor on a data science team deploy a structured lifecycle approach to data analytics problems apply appropriate analytic techniques and tools to analyzing big data learn how to tell a compelling story with data to drive business action prepare for emc proven professional data science certification get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

While the term big data is open to varying interpretation, it is quite clear that the volume, velocity, and variety (3vs) of data have impacted every aspect of computational science and its applications. the volume of data is increasing at a phenomenal rate and a majority of it is unstructured. with big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. the drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. in common usage, big data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. while there are challenges, there are huge opportunities emerging in the fields of machine learning, data mining, statistics, human-computer interfaces and distributed systems to address ways to analyze and reason with this data. the edited volume focuses on the challenges and opportunities posed by "big data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. big data has the potential to help companies improve operations and make faster, more intelligent decisions. review of big data research challenges from diverse areas of scientific endeavor rich perspective on a range of data science issues from leading researchers insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domains

Data analytics for the social sciences is an introductory, graduate-level treatment of data analytics for social science. it features applications in the r language, arguably the fastest growing and leading statistical tool for researchers. the book starts with an ethics chapter on the uses and potential abuses of data analytics. chapters 2 and 3 show how to implement a broad range of statistical procedures in r. chapters 4 and 5 deal with regression and classification trees and with random forests. chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. chapter 7 deals with neural network analysis, and chapter 8 deals with network analysis and visualization of network data. a final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. all empirical chapters have two "quick start" exercises designed to allow quick immersion in chapter topics, followed by "in depth" coverage. data are available for all examples and runnable r code is provided in a "command summary". an appendix provides an extended tutorial on r and rstudio. almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. it is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

The new marketing analytics a clear, common sense guide for implementing an effective data analytics strategy. in today's world, no matter the industry, businesses and organizations are competing in an information-based marketplace. to effectively compete, marketing leaders are tasked with timely analysis of mountains of customer and market information to make faster and better marketing decisions. it's not just the task of collecting information. it's the development of new insights and the ability to implement this new found knowledge to drive more profitable marketing – that's what counts. marketers get paid to produce results. this book will demonstrate how the degree of success is in direct proportion to the ability to successfully analyze both traditional and big data. the information and wisdom derived from data is the ultimate competitive advantage.