Take a look at our Data Science books. Shulph carries a great selection of Data Science books, and we are always adding more.
Explore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary.
Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify the exact problem to be solved Visualize data using different graphs Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you'll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter Notebook running Python Use Matplotlib to create data visualizations Fit a model using scikit-learn Use lasso and ridge regression to reduce overfitting Fit and tune a random forest model and compare performance with logistic regression Create visuals using the output of the Jupyter Notebook Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.
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.
Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.
Discover one of the most complete dictionaries in data science. Key Features - Simplified understanding of complex concepts, terms, terminologies, and techniques. - Combined glossary of machine learning, mathematics, and statistics. - Chronologically arranged A-Z keywords with brief description. Description This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. What you will learn - Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. - Keep yourself technically strong and sound-minded during data science meetings. - Strengthen your knowledge in the field of Big data and business intelligence. Who this book is for This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. Table of Contents 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z About the Authors Mohamed Sabri is the Director of Practice in Data Science and Artificial Intelligence in a business consulting firm. Thanks to his experience in the IT world, he is able to deliver end-to-end solutions in the field of AI. He is very strong in communication and well versed in technology popularization for complex projects. He has participated as a data scientist in several AI projects for large organizations such as banks and manufacturers. He has graduated in Economics and Mathematics from the University of Ottawa. Blog links: https://www.datalyticsbusiness.ca/ LinkedIn Profile: https://www.linkedin.com/in/mohamed-sabri/
BRONZE RUNNER UP: Axiom Awards 2018 - Business Technology Category (1st edition)Data is an integral strategic asset for all businesses. Learn how to leverage this data and generate valuable insights and true business value with bestselling author and data guru Bernard Marr.Data has massive potential for all businesses when used correctly, from small organizations to tech giants and huge multinationals, but this resource is too often not fully utilized. Data Strategy is the must-read guide on how to create a robust, data-driven approach that will harness the power of data to revolutionize your business. Explaining how to collect, use and manage data, this book prepares any organization with the tools and strategies needed to thrive in the digital economy.Now in its second edition, this bestselling title is fully updated with insights on understanding your customers and markets and how to provide them with intelligent services and products. With case studies and real-world examples throughout, Bernard Marr offers unrivalled expertise on how to gain the competitive advantage in a data-driven world.
Book with a practical approach for understanding the basics and concepts of Data Structure Key Features This book is especially designed for beginners, explains all basics and concepts about data structure. Source code of all data structures are given in C language. Important data structures like Stack, Queue, Linked List, Tree and Graph are well explained. Solved example, frequently asked in the examinations are given which will serve as a useful reference source. Effective description of sorting algorithm (Quick Sort, Heap Sort, Merge Sort etc.) Description Book gives a full understanding of the theoretical topic and easy implementation of data structures through C. The book is going to help students in self-learning of data structures and in understanding how these concepts are implemented in programs. Algorithms are included to clear the concept of data structure. Each algorithm is explained with figures to make student clearer about the concept. Sample data set is taken and step by step execution of the algorithm is provided in the book to ensure the in-depth knowledge of students about the concept discussed. What You Will Learn New features and essential of Algorithms and Arrays. Linked List, its type and implementation. Stacks and Queues Trees and Graphs Searching and Sorting Greedy method Beauty of Blockchain Who this book is for This book is specially designed to serve as textbook for the students of various streams such as PGDCA, B.Tech. /B.E., BCA, BSc M.Tech. /M.E., MCA, MS and cover all the topics of Data Structure. The subject data structure is of prime importance for the students of Computer Science and IT. It is practical approach for understanding the basics and concepts of data structure. All the concepts are implemented in C language in an easy manner. To make clarity on the topic, diagrams, examples and programs are given throughout the book Table of Contents 1. Algorithm and Flowcharts 2. Algorithm Analysi 3. Introduction to Data structure 4. Functions and Recursion 5. Arrays and Pointers 6. String 7. Stack 8. Queues 9. Linked Lists 10. Trees 11. Graphs 12. Searching 13. Sorting 14. Hashing About the Author Dr. Brijesh Bakariya, working as an Assistant Professor in Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University (IKGPTU) Jalandhar (Punjab) has his Ph. D. from Maulana Azad National Institute of Technology (NIT- Bhopal) , Madhya Pradesh and MCA Degree from Devi Ahilya Vishwavidyalaya, Indore (Madhya Pradesh) in Computer Applications. He has been teaching since 2009 and guiding M.Tech/ Ph.D students. He has also published many research papers in the area of Data Mining and Image Processing.
Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.
Information and how we manage, process and govern it is becoming increasingly important as organizations ride the wave of the big data revolution. Ethical Data and Information Management offers a practical guide for people in organizations who are tasked with implementing information management projects. It sets out, in a clear and structured way, the fundamentals of ethics, and provides practical and pragmatic methods for organizations to embed ethical principles and practices into their management and governance of information. Written by global experts in the field, Ethical Data and Information Management is an important book addressing a topic high on the information management agenda. Key coverage includes how to build ethical checks and balances into data governance decision making; using quality management methods to assess and evaluate the ethical nature of processing during design; change methods to communicate ethical values; how to avoid common problems that affect ethical action; and how to make the business case for ethical behaviours.
Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key Features Manipulate location-based data and create intelligent geospatial data models Build effective location recommendation systems used by popular companies such as Uber A hands-on guide to help you consume spatial data and parallelize GIS operations effectively Book Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learn Learn how companies now use location data Set up your Python environment and install Python geospatial packages Visualize spatial data as graphs Extract geometry from spatial data Perform spatial regression from scratch Build web applications which dynamically references geospatial data Who this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.