Building Analytics Teams. Harnessing analytics and artificial intelligence for business improvement

John K. Thompson, Douglas B. Laney

In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success.The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs.The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects.By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization.

Building ETL Pipelines with Python. Create and deploy enterprise-ready ETL pipelines by employing modern methods

Brij Kishore Pandey, Emily Ro Schoof

Modern extract, transform, and load (ETL) pipelines for data engineering have favored the Python language for its broad range of uses and a large assortment of tools, applications, and open source components. With its simplicity and extensive library support, Python has emerged as the undisputed choice for data processing.In this book, you’ll walk through the end-to-end process of ETL data pipeline development, starting with an introduction to the fundamentals of data pipelines and establishing a Python development environment to create pipelines. Once you've explored the ETL pipeline design principles and ET development process, you'll be equipped to design custom ETL pipelines. Next, you'll get to grips with the steps in the ETL process, which involves extracting valuable data; performing transformations, through cleaning, manipulation, and ensuring data integrity; and ultimately loading the processed data into storage systems. You’ll also review several ETL modules in Python, comparing their pros and cons when building data pipelines and leveraging cloud tools, such as AWS, to create scalable data pipelines. Lastly, you’ll learn about the concept of test-driven development for ETL pipelines to ensure safe deployments.By the end of this book, you’ll have worked on several hands-on examples to create high-performance ETL pipelines to develop robust, scalable, and resilient environments using Python.

Building Statistical Models in Python. Develop useful models for regression, classification, time series, and survival analysis

Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.

Codeless Time Series Analysis with KNIME. A practical guide to implementing forecasting models for time series analysis applications

KNIME AG, Corey Weisinger, Maarit Widmann, Daniele Tonini

This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques.This learning journey is organized in a crescendo of difficulty, starting from the easiest yet effective techniques applied to weather forecasting, then introducing ARIMA and its variations, moving on to machine learning for audio signal classification, training deep learning architectures to predict glucose levels and electrical energy demand, and ending with an approach to anomaly detection in IoT. There’s no time series analysis book without a solution for stock price predictions and you’ll find this use case at the end of the book, together with a few more demand prediction use cases that rely on the integration of KNIME Analytics Platform and other external tools.By the end of this time series book, you’ll have learned about popular time series analysis techniques and algorithms, KNIME Analytics Platform, its time series extension, and how to apply both to common use cases.

CompTIA Data+: DAO-001 Certification Guide. Complete coverage of the new CompTIA Data+ (DAO-001) exam to help you pass on the first attempt

Cameron Dodd

The CompTIA Data+ certification exam not only helps validate a skill set required to enter one of the fastest-growing fields in the world, but also is starting to standardize the language and concepts within the field. However, there’s a lot of conflicting information and a lack of existing resources about the topics covered in this exam, and even professionals working in data analytics may need a study guide to help them pass on their first attempt.The CompTIA Data + (DAO-001) Certification Guide will give you a solid understanding of how to prepare, analyze, and report data for better insights.You’ll get an introduction to Data+ certification exam format to begin with, and then quickly dive into preparing data. You'll learn about collecting, cleaning, and processing data along with data wrangling and manipulation. As you progress, you’ll cover data analysis topics such as types of analysis, common techniques, hypothesis techniques, and statistical analysis, before tackling data reporting, common visualizations, and data governance. All the knowledge you've gained throughout the book will be tested with the mock tests that appear in the final chapters.By the end of this book, you’ll be ready to pass the Data+ exam with confidence and take the next step in your career.

Cracking the Data Engineering Interview. Land your dream job with the help of resume-building tips, over 100 mock questions, and a unique portfolio

Kedeisha Bryan, Taamir Ransome

Preparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey.The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you’ll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you’ll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions.By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.

Dane grafowe w praktyce. Jak technologie grafowe ułatwiają rozwiązywanie złożonych problemów

Denise Gosnell, Matthias Broecheler

Komputer do pracy potrzebuje liczb i danych. Człowiek chętniej wysnuwa wnioski i wyodrębnia kontekst na podstawie relacji. Te dwa sposoby myślenia są tak odmienne, że komputery do niedawna z trudem wykonywały zadania związane z operowaniem na relacjach. Obecnie może się to zmienić dzięki grafom. Technologie grafowe łączą ludzkie postrzeganie świata i liniową pamięć komputerów. Ich wdrożenie na szerszą skalę będzie stanowić przełom i pozwoli osiągnąć nieznany dziś poziom. Ale najpierw trzeba nauczyć się stosować myślenie grafowe w rozwiązywaniu problemów technicznych. Dzięki tej książce opanujesz podstawy myślenia grafowego. Zapoznasz się z elementarnymi koncepcjami grafowymi: teorią grafów, schematami baz danych, systemami rozproszonymi, a także analizą danych. Dowiesz się również, jak wyglądają typowe wzorce wykorzystania danych grafowych w aplikacjach produkcyjnych. Poznasz sposób, w jaki można te wzorce stosować w praktyce. Pokazano tu, jak używać technik programowania funkcyjnego oraz systemów rozproszonych do tworzenia zapytań i analizowania danych grafowych. Opisano też podstawowe podejścia do proceduralnego przechodzenia przez dane grafowe i ich wykorzystanie za pomocą narzędzi grafowych. W książce: nowy paradygmat rozwiązywania problemów: dane grafowe wzorce wykorzystania danych grafowych przykładowa architektura aplikacji w technologiach relacyjnych i grafowych technologie grafowe a przewidywanie preferencji i zaufania użytkowników filtrowanie kolaboratywne i jego zastosowanie Grafy: przełomowa koncepcja w analizie danych!

Data Cleaning and Exploration with Machine Learning. Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Michael Walker

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results.As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book.By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.