Practitioner S Guide To Data Science
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Practitioner S Guide To Data Science
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Author : Nasir Ali Mirza
language : en
Publisher: BPB Publications
Release Date : 2022-01-17
Practitioner S Guide To Data Science written by Nasir Ali Mirza and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-17 with Computers categories.
Covers Data Science concepts, processes, and the real-world hands-on use cases. KEY FEATURES ● Covers the journey from a basic programmer to an effective Data Science developer. ● Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. ● Implementation of MLOps using Microsoft Azure DevOps. DESCRIPTION "How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. WHAT YOU WILL LEARN ● Organize Data Science projects using CRISP-DM and Microsoft TDSP. ● Learn to acquire and explore data using Python visualizations. ● Get well versed with the implementation of data pre-processing and Feature Engineering. ● Understand algorithm selection, model development, and model evaluation. ● Hands-on with Azure ML Service, its architecture, and capabilities. ● Learn to use Azure ML SDK and MLOps for implementing real-world use cases. WHO THIS BOOK IS FOR This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. TABLE OF CONTENTS 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models
The Practitioner S Guide To Data Quality Improvement
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Author : David Loshin
language : en
Publisher: Elsevier
Release Date : 2010-11-22
The Practitioner S Guide To Data Quality Improvement written by David Loshin and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-22 with Computers categories.
The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
Practitioner S Guide To Data Science
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Author : Hui Lin
language : en
Publisher: CRC Press
Release Date : 2023-05-24
Practitioner S Guide To Data Science written by Hui Lin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-24 with Business & Economics categories.
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!
Python Data Science Essentials
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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-10-28
Python Data Science Essentials written by Alberto Boschetti and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-28 with Computers categories.
Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Big Data Analytics With Spark
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Author : Mohammed Guller
language : en
Publisher: Apress
Release Date : 2015-12-29
Big Data Analytics With Spark written by Mohammed Guller and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-29 with Computers categories.
Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You’ll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies thatare commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost—possibly a big boost—to your career.
A Practitioner S Guide To Business Analytics Pb
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Author : Randy Bartlett
language : en
Publisher: McGraw Hill Professional
Release Date : 2013-01-25
A Practitioner S Guide To Business Analytics Pb written by Randy Bartlett and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-25 with Business & Economics categories.
Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.
The Practitioner S Guide To Graph Data
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Author : Denise Gosnell
language : en
Publisher: O'Reilly Media
Release Date : 2020-03-20
The Practitioner S Guide To Graph Data written by Denise Gosnell and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-20 with Computers categories.
Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you’ll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You’ll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system
Applied Machine Learning For Data Science Practitioners
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Author : Vidya Subramanian
language : en
Publisher: John Wiley & Sons
Release Date : 2025-04-01
Applied Machine Learning For Data Science Practitioners written by Vidya Subramanian and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-01 with Mathematics categories.
A single-volume reference on data science techniques for evaluating and solving business problems using Applied Machine Learning (ML). Applied Machine Learning for Data Science Practitioners offers a practical, step-by-step guide to building end-to-end ML solutions for real-world business challenges, empowering data science practitioners to make informed decisions and select the right techniques for any use case. Unlike many data science books that focus on popular algorithms and coding, this book takes a holistic approach. It equips you with the knowledge to evaluate a range of techniques and algorithms. The book balances theoretical concepts with practical examples to illustrate key concepts, derive insights, and demonstrate applications. In addition to code snippets and reviewing output, the book provides guidance on interpreting results. This book is an essential resource if you are looking to elevate your understanding of ML and your technical capabilities, combining theoretical and practical coding examples. A basic understanding of using data to solve business problems, high school-level math and statistics, and basic Python coding skills are assumed. Written by a recognized data science expert, Applied Machine Learning for Data Science Practitioners covers essential topics, including: Data Science Fundamentals that provide you with an overview of core concepts, laying the foundation for understanding ML. Data Preparation covers the process of framing ML problems and preparing data and features for modeling. ML Problem Solving introduces you to a range of ML algorithms, including Regression, Classification, Ranking, Clustering, Patterns, Time Series, and Anomaly Detection. Model Optimization explores frameworks, decision trees, and ensemble methods to enhance performance and guide the selection of the most effective model. ML Ethics addresses ethical considerations, including fairness, accountability, transparency, and ethics. Model Deployment and Monitoring focuses on production deployment, performance monitoring, and adapting to model drift.
Marketing Analytics A Practitioner S Guide To Marketing Analytics And Research Methods
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Author : Ashok Charan
language : en
Publisher: World Scientific Publishing Company
Release Date : 2015-05-20
Marketing Analytics A Practitioner S Guide To Marketing Analytics And Research Methods written by Ashok Charan and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-20 with Business & Economics categories.
The digital age has transformed the very nature of marketing. Armed with smartphones, tablets, PCs and smart TVs, consumers are increasingly hanging out on the internet. Cyberspace has changed the way they communicate, and the way they shop and buy. This fluid, de-centralized and multidirectional medium is changing the way brands engage with consumers.At the same time, technology and innovation, coupled with the explosion of business data, has fundamentally altered the manner we collect, process, analyse and disseminate market intelligence. The increased volume, variety and velocity of information enables marketers to respond with much greater speed, to changes in the marketplace. Market intelligence is timelier, less expensive, and more accurate and actionable.Anchored in this age of transformations, Marketing Analytics is a practitioner's guide to marketing management in the 21st century. The text devotes considerable attention to the way market analytic techniques and market research processes are being refined and re-engineered. Written by a marketing veteran, it is intended to guide marketers as they craft market strategies, and execute their day to day tasks.