Data Visualization Practitioner S Guide
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Data Visualization Practitioner's Guide
To harness the potential of data, it is crucial for data professionals to present it in a visual format that is both interactive and comprehensible to management and decision makers. The human brain has a strong inclination towards visual information, making data visualization an incredibly powerful tool for the efficient analysis, interpretation, and communication of data. The Data Visualization Practitioner’s Guide, functions as textual add on to a Certified Data Visualization Course. The EDF Data Visualization certification demonstrates qualified professionals that have mastered the required skills to visualize data effectively, ensuring that important results will not escape their notice. The training consists of six modules, each with its own weight towards the certification exam: Weight Topic Introduction - Introduction to the data visualization field and basic quantitative thinking. Human perception 20% About the principal components of visual perception, to optimize our visualizations for human consumption. Visualizing data 30% About applying the knowledge of our visual perception to data visualization and the introduction of the CHRTTS model to select chart types. Data viz design 25% About the detailed choices we need to consider when designing our visuals. We also look at the management dashboard and the important role of color. Storytelling 15% How to make use of our capacity to tell and consume stories to process data insights. Workflow 10% How to implement the effective data visualization practices in our daily work.
Data Visualization Made Easy: A Beginner's Guide for Everyone
Make your data sing with data visualization. In today's data-driven world, it's more important than ever to be able to communicate data effectively. Data visualization is the art of transforming data into visually appealing charts, graphs, and dashboards that convey your message clearly and persuasively. Data Visualization Made Easy is the perfect resource for anyone who wants to learn how to use data visualization to communicate their data. This comprehensive guide covers everything you need to know, from the basics of data visualization to advanced topics such as data storytelling and dashboard design. Whether you're a student, a business professional, or a data enthusiast, Data Visualization Made Easy is the essential resource for learning how to communicate data effectively. Here are some of the key topics covered in the book: Introduction to data visualization Choosing the right data visualization Creating effective data visualizations Data storytelling Dashboard design With Data Visualization Made Easy, you'll be well on your way to becoming a data visualization expert. #datascience #machinelearning #analyticsforeveryone #dataanalysisforbeginners #data #datavisualization #machinelearning #beginnersguide #learndata #GoogleAnalytics #Google #mobileapp #datavisualization #madeeasy #madesimple
Practitioner’s Guide to Data Science
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