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10.22.2021
Top 10 Books to Boost Your Data Driven Outlook
I started my career as a Software Developer and switched to data science 8 years ago when big data software projects were difficult to predict and risky to conduct due to large volumes of unclassified data and many types of metrics. Using machine learning, data analysis, and visualization approaches was essential for facilitating informed decision-making throughout the software development and testing process.

Mastering data analysis was one of the most challenging experiences in my life. Wading through tons of books to figure out where to start and which methods and techniques to use in a particular case can be extremely daunting and time-consuming.

If you have been studying data analytics for some time, choosing the right educational resources is crucial to launching and advancing your career within this area.

In recent years, the volume of generated data has risen exponentially. Companies of all sizes are looking for the best methods to turn their data into a competitive edge. With organizations discovering the true value of data, a further increase in demand for data analysts in 2022 is expected.

In this post, I will cover the best 10 books for data analysts. These data analytics books will teach you about the power of big data and ways to harness it.
1.
Storytelling with Data: A Data Visualization Guide for Business Professionals
Author: Cole Nussbaumer Knaflic
As a data analyst, your aim is not just to retrieve data but also to make it intelligible, which requires you to be able to present the data in a certain way. However, presenting data does not imply dragging and dropping data fields into a chart. It entails creating a meaningful visual representation of the data. This book is based on real-life scenarios and will give you some idea on the difference between colorful visualization and intelligent visualization, explaining why you should closely examine each line and color on your visual interface.

This book provides excellent guidance, examines criteria, and presents examples of how to properly deal with data.
2.
Mastering Tableau 2021: Implement advanced business intelligence techniques and analytics with Tableau, 3rd Edition
Author: Cole Nussbaumer Knaflic
As a business analytics practitioner, I search for publications that can simplify complicated topics in a manner that everyone can understand. The book contains several tips and techniques that will assist you in understanding when to utilize particular chart styles, at what data granularity, and with what sort of presentation for the end user. You will begin this fascinating trip by learning essential strategies for using sophisticated math to tackle challenging situations. These strategies involve the inventive use of several sorts of computations, such as row-level, aggregate-level, and others. Besides, you will get concise instructions on using Tableau to solve practically any data visualization problem by knowing the tool's inner workings and thinking creatively about the possibilities.

After reading the book, you will be equipped with an arsenal of advanced chart types and methods that will allow you to display information to a range of audiences in an effective and engaging manner using clear, efficient, and engaging dashboards. Explanations and examples of effective and inefficient visualization approaches, well-planned and badly created dashboards, and compromise choices when Tableau users do not embrace data visualization, will expand your knowledge of Tableau, so that you get the most of this powerful tool.
3.
Machine Learning with the Elastic Stack - Second Edition
Authors: Rich Collier, Camilla Montonen, Bahaaldine Azarmi
This book is a one-of-a-kind resource for users using Elastic search. It focuses on the substantial growth of machine learning technology in Elastic search providing actual case studies and extensive explanation. This book is similar to having a one-on-one conversation with a subject matter expert. If you need to refresh your practical skills in machine learning, the book offers examples of how to apply Elastic ML in your environment, get valuable insight into your data, and how you can turn machine learning from static to intelligent. If you want to understand not just how to build tasks but also tap into the underlying models and variables, Machine Learning with the Elastic Stack is the ideal option for you.
4.
Data Analytics Made Easy: Analyze and present data to make informed decisions without writing any code
Author: Andrea De Mauro
With data literacy being such an important component of a data-driven mindset, this book is an excellent resource for data science students looking to obtain practical information and learn how to apply their analytical skills. The author does an excellent job of introducing readers to KNIME, a low-code data analytics framework that allows to instantly evaluate data. Furthermore, his presentation of machine learning is user-friendly, with an emphasis on theoretical knowledge and handling a variety of use cases. More significantly, De Mauro assists readers in comprehending the significance of becoming a great data presenter, a vital talent to cultivate in order to influence decision-making.
5.
Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies
Authors: John D.Kelleher, Brian Mac Namee, Aoife D'Arcy
Fundamentals of Machine Learning for Predictive Data Analytics is a detailed analysis of the most important machine learning methods used in predictive data analytics, encompassing both theoretical principles and actual implementations. Technical and mathematical knowledge is complemented with instructional practical examples, and case studies show how these models may be employed in a wider business setting.

Following a description of the journey from extracting data to gaining insights and making a prediction, the book delves into the most essential machine learning techniques: data-based learning, correlation-based learning, probability and error-based learning. Each of these strategies starts with a no- tech description of the core principle, followed by quantitative models and algorithms demonstrated with extensive practical examples.

The authors discuss the procedures in a straightforward and succinct way, without referring to any specific programming frameworks or languages. They do a fantastic job of introducing the main concepts before diving deeper into the complexities of the logic and math underpinning the algorithms.
6.
Analytics Stories: Using Data to Make Good Things Happen
Author: Wayne L. Winston
Analytics Stories: How to Make Good Things Happen is a serious, intelligent, and entertaining look at how analytics can tackle real-world problems and situations. Analytics Stories fills the gap between data analytics and the particular challenges it solves, with topics ranging from sports to finance, politics, healthcare, and commerce.

The author does an outstanding job of conveying the notion of data storytelling to the reader. He develops around 50 business cases on topics ranging from education to sports. Dr. Winston mostly utilized MS Excel to interpret, analyze, display, and successfully convey the data.
7.
Data Pipelines Pocket Reference: Moving and Processing Data for Analytics
Author: James Densmore
A data science pipeline is a set of procedures that transform raw data into meaningful business responses. Data science pipelines streamline data validation, extract, transform, load machine learning and modeling, revision, so their implementation is crucial for data analytics success. The difference between having data and truly deriving value from it is moving data from various sources and processing it to create context.

This helpful reference describes common pipeline failures and key decision factors like batches vs. streaming data input and building vs. purchasing. This book delves into fundamental concepts that apply to open source systems, consumer applications, and homegrown solutions, as well as the most common decisions made by experts.

Data Pipelines Pocket Reference is a precious resource for all of the everyday problems and activities you are likely to encounter, if you work in data analysis or a related field that will assist you in making data-driven decisions for many years to come.
8.
Data Modeling for Azure Data Services
Author: Peter ter Braake
Data Modeling for Azure Data Services begins with an overview of databases, entity analysis, and data normalization. This book is a good mix of database theory and practical advice for students and data professionals who work with Azure.

As you progress through the chapters of the book, you delve into the intricacies of Azure fully managed databases, Azure Data Lake, and Azure SQL Data Warehouse. The author will guide you through intricacies of dimensional modeling, data vault modeling, along with designing and implementing a Data Lake. You will receive instructions on how to pick the best database for your use case and how to run them in the Azure environment. The book offers several ideas and techniques to assist you in managing your costs and performance so that you may set things up effectively from the outset, with the potential to grow later on. Moreover, there will be various practical assignments that will help you implement what you've learned right away, as well as a GitHub repository with all the essential code and documentation.

Upon completion of this book, you'll have a firm grasp on which Azure data services are most suited to your model and how to apply the optimum solution architecture.
9.
Azure Databricks Cookbook
Authors: Phani Raj, Vinod Jaiswal
The Azure Databricks Cookbook is a recent best-seller that covers pivotal subjects, such as integrating, creating, and productionizing big data systems on Azure, and provides up-to-date solutions for dealing with massive datasets.

The book gives valuable insights into ingesting data from multiple batch and streaming sources, as well as building an Azure Databricks instance leveraging the Azure interface, Azure CLI, and ARM templates. You will learn about Databricks clusters and procedures for importing data from sources such as files, databases, Apache Kafka and EventHub. The book provides an exhaustive description of the capabilities that Azure Databricks supports for creating powerful end-to-end data pipelines. You will also get detailed instructions on how to utilize Delta tables and Azure Synapse Analytics to create a contemporary data warehouse.
10.
Cloud Scale Analytics with Azure Data Services
Author: Patrik Borosch
Cloud Scale Analytics with Azure Data Services is your guide to understanding all of the features and functionality of Azure data services for data intake, storage, processing, and distribution. As a data analytics practitioner, I found this book to be quite helpful for anyone looking to establish their data and analytics platform on Azure. This book guides you through the fundamental principles of data architecture, analysis, and delivery using visualization technologies such as Power BI. It describes additional Azure components that may be used in this process, such as Synapse Analytics, Cognitive Services, Azure functions, and so on.

The book also teaches you how to deal with numerous issues and complexities related to productivity and scalability. With understanding of these principles, you will be able to manage large volumes of data and create secure, scalable data estates by using a cloud-based data warehouse architecture.
Conclusion
Having a thorough grasp of data analytics and knowing how to gain actionable data-driven insights are essential for a successful career in data science. Anyone interested in expanding their knowledge of data analytics can benefit from the books mentioned in this article, since they provide the most recent industry information illustrated by examples of best practices.
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