The world is rapidly moving toward digitization. This has resulted in substantial developments, with data analytics being one of them.
Mastering data analysis abilities is an excellent place to start if you want to start a new job in this field. But, how to learn data analytics from scratch?
Knowledge is power in today’s competitive society. As a result, we’ve compiled a selection of books that will keep you informed and help you improve your data analytics skills.
Choose a few books from our list to keep studying your profession, whether you’re new to data or a seasoned veteran.
If you’re still trying to figure out how to become a Data Analyst, books are a wonderful place to start.
So, without any further ado, let’s start:
Which data analytics books are a must-read?
The Art of Statistics: How to Learn from Data
Data analysis necessitates a basic understanding of statistics. Learning statistics from textbooks might be a perplexing experience if you have never been exposed to them before. The Art of Statistics provides a high-level review of the most commonly utilized statistical principles. It teaches you to read and use statistical data analysis and what to expect from statistics.
The relative accessibility and extensive scope of this work are its strengths. The book teaches you how to derive knowledge from data and provides you with solid statistical and data literacy.
The book focuses on the concepts and relationships that underpin math and shows how they can be applied in real-life situations.
It also educates you on thinking like a data scientist by asking questions, stating hypotheses, setting expectations, and interpreting data using scientific methods. This book will be useful if you seek an accessible but well-thought-out guide to statistics.
Do you work for a startup or a startup-like company growing quickly and employing agile practices? Lean Analytics might be the ideal book for you.
Lean Analytics presents a high-level overview of the most frequent metrics at various phases of your organization and plenty of practical advice on how to use them. The writers wrote the book based on case studies and interviews with company founders and inventors.
Fast-growing companies go through several business phases in a short period. Because of the fast speed, the most important business metric fluctuates depending on the current scenario and goal. The book advocates focusing on the “one metric that matters” and tracking it diligently.
Data Analytics Made Accessible
This is the book that we would recommend to a complete newbie to the topic of data science. “Business Intelligence and Data Mining Made Accessible,” now updated for 2018, is unquestionably the best book on data analytics. It achieves exactly what its name implies: it straightforwardly presents data analytics, making it clear and consumable for the layperson.
The book encourages easy comprehension by providing specific, real-world examples at the start of each chapter. It’s organized to make sense, like a semester-long college course. Each chapter provides case studies to help weave the subject together.
“Data Analytics Made Accessible” has been adopted as a college textbook by numerous universities in the United States and worldwide due to its breadth of content and straightforward explanation.
The book also includes some “crowdsourced” content, as four chapters were added to the 2017 edition based on comments from reviewers and readers. This is a book that, at 156 pages on Kindle, you could finish in one (long) sitting if you wanted to, and that you can use as inspiration when developing your business intelligence plan.
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Predictive Analytics is included in our list of the most notable business intelligence trends because it is widely regarded as the method that allows big data to be unleashed.
Predictive analytics is used in business to examine current data and past facts to gain a better understanding of customers, goods, and rivals and identify future dangers and opportunities.
On the other hand, Predictive Analytics should not be limited to business professionals due to its broad application. Most people know that firms gather information such as our text messages, GPS location, credit card purchases, social network posts, and Google search history. This book will provide insight into their data collection techniques and motivations.
Eric Siegel’s data analytics book is a fascinating read for anyone interested in learning what predictive analytics is and how it may be used in various fields. A data scientist seeking instructions would be dissatisfied because it is not a manual. Although there is some mention of techniques such as linear regression and decision trees, it is simple enough for a layperson to understand.
Data Smart: Using Data Science to Transform Information into Insight
‘Data Smart‘ gives practical guidance on which analytic approaches to use while crunching data. It’s a good read for anyone with a basic understanding of applied mathematics and access to a spreadsheet tool on their computer. A business expert who needs to work with data sets is a well-thought-out and produced course with many easy-to-understand real-world examples.
Nonlinear programming and genetic algorithms, supervised AI through logistic regression, graph modularity, seasonal adjustments, clustering, forecasting, data mining in graphs, ensemble models, and prediction intervals through Monte Carlo simulation, as well as moving from spreadsheets to the R programming language, are covered in each chapter.
‘Data Smart’ provides comprehensive practical material to begin analyzing with good old Microsoft Excel. Its goal isn’t to use more software to transform your firm; rather, it’s to use accessible analytic approaches to make gradual gains in operations.
Excel may not be able to handle large enterprise-level data sets with vast numbers of rows and hundreds of columns of information once you start working with them. At this time, the most cost-effective and efficient solution is to use self-service business intelligence.
Storytelling with Data: A Data Visualization Guide for Business Professionals
While most data analytics books focus on how to get the most out of data and how to analyze it, Storytelling with Data takes a different approach. Cole Nussbaumer discusses data visualization software and how data can be turned into a compelling tale.
Not all data elements are useful. This book explains how to present facts to emphasize the most significant aspects. The book focuses on creating and constructing graphs with practical examples that can be easily implemented in real-world scenarios.
Although the book is primarily aimed at business professionals, any beginner-level data practitioner can benefit from learning about helpful data visualization tools from this resource.
Its structure is aimed at practical data visualization, ideal for a business scenario. The chapters are short and to-the-point, focusing on communication and the relevance of context. It’s a book that might easily be sold as a classroom resource.
But its major strength could also be its main flaw; that is, it never digs that deeply into any of the essential issues of visualization because it covers them all in a language suited for a business audience.
It will be a fantastic resource if you want to convince clients and coworkers of the importance of well-designed graphs, charts, and reports. It excels at conveying rather difficult topics in a way that both designers and business people can comprehend.
Too Big to Ignore: The Business Case for Big Data
If you’re considering a career in data science, Too Big to Ignore is a must-read to give you an advantage over your peers. It’s also a valuable resource to share with others you know who might be skeptical of predictive data analysis or big data.
The author demonstrates how technical information may be simplified to offer you a competitive edge in today’s market through his case studies and from around the world.
Regardless of how big or small your company is, the proper use of big data may propel you to new heights. And it’s not only the private industry that can benefit from big data; even municipal governments can benefit.
The book, however, does not focus on cautions; instead, it has a pleasant tone. Phil Simon uses analogies, case studies, and extensive knowledge to help us understand a subject that may easily overwhelm us. The accessibility of the text is pleasing for most of the book.
This is not a technical big data how-to guide. Rather, it’s an approachable book that can help you gain confidence in the subject and widen your understanding of big data’s potential in your own company.
Analytics in a Big Data World: The Essential Guide to Data Science and its Applications
This is a real-world data analytics manual for readers who understand data mining and business intelligence and are looking for structural and technical guidance on how to apply big data analytics to real-world business management.
“Analytics in a Big Data World” begins by introducing the reader to basic vocabulary, the analytics process model, and its connections to other important disciplines such as statistics, machine learning, and artificial intelligence.
After that, the author goes over the most critical process model steps, such as sampling, missing value handling, and variable selection.
There are also various case studies on fraud detection, risk management, customer relationship management, and web analytics, all of which are thoroughly discussed. Backtesting and benchmarking methodologies are discussed, as well as data quality challenges, software tools, and model documentation practices.
This important big data book does not offer an extensive review of all analytical approaches because it is intended to be an accessible resource. Instead, it focuses on data analytics strategies that provide genuine value to business situations.
What purpose does a data analytics book serve?
Data is the new fuel for industries. The fourth industrial revolution is now underway. Big Data and Artificial Intelligence are the eras in which we live. There has been a significant data explosion, resulting in the emergence of new technologies and smarter products.
The statistics and computer science fields fused with the introduction of cloud storage, computing processes, and analytical tools. As a result of this, data science emerged.
Businesses require data. They require it to develop data-driven decision models and improve client experiences.
So, data analytics books help readers track all the emerging changes and advancements in data science. There is always room for knowledge and learning, so you can always learn more no matter how much information you acquire about data science.
How is reading helpful in data analytics?
The benefits of reading books are well known. It not only exercises the brain but also improves literacy. As a result, reading broadens our horizons. Though we may specialize in specific areas of data science, advances in tangential research are frequently beneficial.
You can keep yourself updated with the latest trends by reading and listening to the best data analytics podcasts. In the last decade, the area of data science has made significant progress. Nonetheless, we can swiftly catch up by reading the most important literature.
We become more effective at work when we are up to date, which takes less time and effort. As a result, we have more time to read and study, creating a positive cycle.
What are the advantages of reading data analytics books?
It would be best to research trends to prosper in this digital age that establishes a knowledge-based society. Everyone, from multinational corporations to small businesses, relies on data to develop better strategies for the future of their businesses.
The emergence of data is transforming businesses. Companies, large and small, now expect data-driven insight to inform their business decisions. Data professionals greatly influence marketing approaches and business strategies with good knowledge and experience.
Data professionals are in high demand, but supply is limited, resulting in excellent job chances for those in this industry.
Today, it’s hard to find a business that doesn’t have a social media presence; soon, every company will require data analysts. As a result, it’s a smart career option with a bright future in business. So, according to these future trends, you won’t be wrong to learn data analytics.
And while most employees are dissatisfied with their jobs because of a lack of decision-making authority, this is not the case for data professionals. You will play an important role in the company’s policies and future strategies, making it an extremely fulfilling career.
So, learning and reading data analytics books have not one but many benefits.
Frequently Asked Questions
What is the main purpose of a data analytics book?
A book on data analysis is a well-organized guide to comprehending the importance of data in today’s competitive corporate world. This book gives definitions, explanations, and answers to numerous data-related difficulties if you find the topic of data analytics intimidating or difficult to comprehend.
What are some good books on data analytics?
Some great books to learn data analytics are:
- The Art of Statistics: How to Learn from Data
- Lean Analytics
- Data Analytics Made Accessible
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
- Data Smart: Using Data Science to Transform Information into Insight
What is the possible benefit of understanding your data analytics?
Data and analytics may help determine inventory strategy and decision-making by providing transparency into present and anticipated inventory positions and providing insight into determinants of stock height, composition, and location.
What kind of study is data analysis?
Data analysis is the most important aspect of any research. The data analysis process summarizes the information gathered. It entails using analytical and logical reasoning to decrypt data and identify patterns, correlations, and trends.