Top 10 Must-Read Data Analyst Course Books For Professionals

November 2, 2024 Tanu Yadav

Our ongoing period is the era of data, and every day, a wide range of data is produced. A single Instagram “like” also generates data. Therefore, it is very evident to imagine how much data is generated daily. However, this huge amount of data is baseless without a data analyst. This enormous amount of data offers a variety of useful information which will help the industry. A data analyst is needed to extract helpful information from a vast volume of data. Data analyst course books are therefore in great demand. If you you want to become a data analyst, you are taking the right step. Data analysis is unquestionably a profit-making career.

Who is a Data Analyst?

A data analyst is a specialist who gathers, compiles, and evaluates data from diverse sources to assist an organization in achieving its objectives. Data analysts transform numbers into knowledge that an organization may utilize to enhance workflow and business processes using logic, statistical methods, and computer programming.

Making a distinction between data that should be given more weight and data that should be given less is a key objective of analysis. In many businesses, data analysts are also in charge of maintaining the accuracy of the data and creating reports for both internal and external stakeholders.

Unlike data scientists, who are frequently tasked with creating new ones, data analysts handle data-related problems using mathematical methods and currently available technologies. To become a data analyst, one needs to read data analyst course books.

Typical Tasks Consist of

Utilizing specialized tools to gather the necessary data
Analyzing data to spot trends
Implementing systems and processes to increase the effectiveness of working with data
Looking into novel ways to use data
Creating graphs and reports describing data trends to non-specialists
Delivering data-driven information to clients and managers.

Skills to Work as a Data Analyst

There are three main categories of skills that every data analyst course books teach you.

These are:

Technical knowledge
Transferable and soft skills
Industry-specific know-how

Technical Knowledge

Technical proficiency is a required condition for all data analysts. Programming, comprehending various analytical models (and when to apply them), and other concepts and tools related to the data analytics process are included in this. If you conduct a fast online search, you’ll discover that the majority of discussions surrounding data analytics careers center on technical expertise. If you are unfamiliar with these kinds of talents or the lingo used in the profession, it can be rather scary.

Transferable and Soft Skills

If you lack the necessary transferable abilities, your technical knowledge may initially be more significant than it is. Employers are stating that they desire more soft skills, which traditional credentials (such as college degrees or authorized courses) typically don’t build or measure.

Communication, teamwork, a positive outlook, entrepreneurship, and a strong work ethic are examples of soft talents that will be upskilled with data analyst course books.

Industry-specific Know-how

A growing number of businesses, including government, energy, healthcare, retail, and others, are using data analytics. Therefore, any information you may have about a particular business will make you stand out to employers. For instance, it’s beneficial expertise if you spent several years working for an insurance company or if you have knowledge of supply chains through a summer internship at a department store.

All of this is not to argue that you require industry-specific knowledge; like technical expertise, it can be learned. In contrast to analysts who are experts from the start but may lack subject knowledge, research indicates that businesses are increasingly looking for businesspeople with analytical skills. Consider what you already know and how you can use it to your advantage as you forget your career in the field of data analytics.

Responsibilities of a Data Analyst

Many duties and responsibilities come with the title of data analyst. Understanding what a data analyst does is the first step toward becoming one! The following are some typical and anticipated duties that can be learned with data analyst course books:

Being Aware of the Objective

The organization’s goal must be first and foremost identified by a data analyst.

Inquiry

To collect, store, manage, and retrieve data from relational databases like MS SQL Server, Oracle DB, and MySQL, data analysts create intricate SQL queries and scripts.

Data Mining

To extract fresh information from data, it is categorized and mined from a variety of sources. As a result, data models are created to boost the system’s effectiveness.

Data Purging

A data analyst’s essential responsibilities include cleaning and data management. Initially collected data frequently contain missing values and is disorganized. Therefore, it is essential to clean the obtained data to prepare it for analysis.

Data Analysis

For conducting a logical analysis of data, data analysts use analytical and statistical tools, including programming languages.

Data Trend Interpretation

Data analysts use a variety of tools and libraries to extract patterns and trends from large datasets and uncover previously unknown business insights.

Making Summaries of Reports

Data visualization tools are used by data analysts to create summary reports. The leadership team is guided by these reports to take prompt action.

Working Together with Other Teams

To ensure that business requirements are implemented properly and identify opportunities for process improvement, data analysts collaborate with the management team, development team, and data scientists.

And now that you are fully aware of the duties and roles of a data analyst, you are prepared to discover the key to learning how to become one.

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Data Analytics Scope

Data Analyst Interview Questions

What Tools Do Data Analysts Use?

Software for data analysis is widely available and is well explained in data analyst course books. This will assist you in comprehending the most popular data analysis tools on the market and assist you in developing the abilities required to secure an entry-level data analysis career.

Excel

One of the most widely used pieces of data analysis software is Microsoft Excel. Excel has operations for managing and organizing big data sets in spreadsheets, as well as graphing tools and calculating features like automated summing, or “AutoSum.” Additionally, Excel comes with Analysis ToolPak, a collection of data analysis tools that can perform statistical, regression, and variance analysis.

Excel is a sophisticated data analysis application appropriate for managing, sorting, filtering, cleaning, analyzing, and displaying data because of its simplicity and adaptability. This tool is widely explained in data analyst course books.

Tableau

Business analytics and business intelligence typically employ Tableau, a data visualization program. Tableau is unquestionably one of the most widely used systems for data visualization in the business sector, especially because it has an intuitive user interface and seamlessly converts data sets into understandable graphics.

Data analysts appreciate it because it has strong capabilities that can execute advanced analytics tasks like segmentation, cohort analysis, and predictive analysis, while business users like it because of how simple it is to use.

Python

According to numerous surveys, Python is currently the most widely used programming language in the world. Python, in contrast to other programming languages, is very simple to learn and use for a variety of activities, including data analysis and the building of software and websites.

Python is used to model, simplify, display, and analyze data utilizing its built-in data analytics capabilities in the field of data. Professionals in data analytics value Python’s many libraries, including Pandas and Numpy, which provide a range of potent tools for various analytics needs.

This is one of Python’s key selling points. To ensure that they have a solid understanding of one of the most essential programming languages used in data today, early professionals should learn Python.

MySQL

An open-source relational database management system (RDBMS) called MySQL is used to store application data, especially web-based application data. MySQL is widely utilized by websites, and it is used by well-known websites like Facebook, Twitter, and YouTube.

Relational database management systems, which use relational databases typically arranged into tables, are managed using a Structured Query Language (SQL) in the world of data. Because of this, data professionals use MySQL to securely store data and carry out regular data analysis.

Despite certain restrictions, MySQL often works well with the data systems already in place in many enterprises. If you want to work in technology developing web applications, you should think about studying MySQL in particular.

Jupyter Notebook

A web-based interactive platform called Jupyter Notebook is used to share computational documents, or “notebooks.” Jupyter Notebooks are used by data analysts for many different types of data analysis, including writing and running code, cleaning data, data visualization, machine learning, statistical analysis, and more.

Additionally, Jupyter Notebook gives users the ability to mix data visualizations, code, comments, and a variety of computer languages in one location, providing a better environment for recording and sharing data analysis processes. Whatever your professional data objectives are, you will probably gain from utilizing a program like Jupyter Notebook to solve data issues and collaborate with others.

Types of Data Analytics

There Are Four Types of Data Analytics:

Predictive Analytics:

Predictive analytics are used to convert the data into useful knowledge. Predictive analytics uses data to estimate the chance of a condition arising or the likely course of an occurrence. To predict future events, predictive analytics uses a variety of statistical techniques from modeling, machine learning, data mining, and game theory. These methods examine both historical and present-day data.

Descriptive Analytics:

Descriptive analytics looks at the data and analyses past events to discover how to approach future events. It studies past performance and analyses performance by analyzing historical data to ascertain what led to past success or failure. Almost all management reporting, including that for sales, marketing, operations, and finance, uses this type of analysis.

The descriptive model quantifies links in data to group consumers or prospects into categories. Unlike predictive models, which focus on predicting a specific customer’s behavior, descriptive analytics reveals a variety of interactions between the customer and the product.

Prescriptive Analytics:

To make a prediction, prescriptive analytics automatically combine big data, mathematical science, business rules, and machine learning. They then propose a decision option to capitalize on the prediction. In addition to advising activities that will profit from the projections, prescriptive analytics also outlines the ramifications of each decision alternative for the decision maker.

Prescriptive analytics not only anticipates what will occur when and how, but also takes into account why it will occur. Prescriptive analytics can also suggest choices for how to take advantage of a future opportunity or reduce future risk, as well as explain the ramifications of each recommendation.

Diagnostic Analytics:

In this study, while seeking to answer a question or solve a puzzle, historical data is often favored over other data. We search the historical data for any dependencies and patterns relevant to the specific problem. For instance, businesses use this analysis because it provides excellent insight into a problem. They also maintain detailed information about their disposal because otherwise, data collection might be time-consuming and individual for each issue. Data analyst course books will surely help to get mastery of it.

Best Data Analyst Course Books

1. Data Analytics Made Accessible

By Anil Maheshwari

This book is ideal if you’re interested in learning more about the topic of data. Anil Maheshwari doesn’t utilize any baffling or difficult hypothetical situations, and his language is simple enough for beginners to understand. Anyone with a little understanding of data analysis can quickly understand the author’s viewpoint.

The book’s applicability to a variety of fields, including engineering, statistics, and business, is its finest feature. For readers to become familiar with the fundamentals before continuing with more in-depth study, it provides a comprehensive look into the discipline of data science. This book is already being used in several institutions’ data analytics courses because of its simple writing style. It is one of the best data analyst course books.

2. Developing Analytic Talent: Becoming a Data Scientist

By Vincent Granville

After reading this book, you’ll know how to create thorough analytics that can support your business objectives. The author examines the more complex facets of data science, as well as the necessary abilities and how to develop them. The book also looks at the abilities that businesses want and how the use of big data has increased the demand for data specialists.

This comprehensive book includes sample resumes, wage surveys, job advertising examples, and interview questions. The use of data science on Wall Street, in botnet detection, in digital advertising, and in other areas is demonstrated in case studies that readers can read.

3. Python for Data Analysis—Wes McKinney, 2011

Start learning the tools you’ll need to apply statistical models once you’ve mastered the fundamentals. Python for Data Analysis is here. This book, written by the software developer who created the Python data analysis package pandas, will teach you all there is to know about the most popular programming language in use today.

Using practical exercises so you may experiment with Python and its features while using the book as a guide, McKinney examines the process of altering, cleaning, collating, and analyzing data using Python.

The book is jam-packed with examples and case studies that cover everything from Python’s fundamental numerical features to making scatterplots and using the language to problem-solving in fields like social sciences and economics. An excellent way to introduce what would otherwise be a difficult subject.

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4. Too Big to Ignore:

By Phil Simon

This is one of the go-to big data analyst course books where the author investigates how corporations and even local governments are using big data to their advantage, regardless of whether you’re dubious about or excited by corporate uses for big data.

Too Big to Ignore: The Business Case for Big Data is a must-read for anybody thinking about working in the industry, with several case studies and statements from big data experts throughout the world. The process of transforming data into intelligence and that intelligence into something actionable will become much clearer to readers.

5. Artificial Intelligence: A Guide for Thinking Humans

By Melanie Mitchell

Melanie Mitchell, a renowned computer scientist, and award-winning author explores the tumultuous history of artificial intelligence (AI), as well as its accomplishments, hopes, and growing worries, in her book Artificial Intelligence. Mitchell looks at the most important problems that AI is now facing, including how they work.

Should we worry that they will pass us by? While weaving tales about the science of AI and the individuals who have contributed to it, Mitchell also gives readers a clear understanding of the profound gap between the hype and actual achievements in AI.

6. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, Or Die

By Eric Siegel

What Nate Silver did for poker and politics, according to author David Leinweber, this book does for everything else. With some excellent reviews under its belt, this book on data analysis holds a significant position in the field of data science. It is a broad, well-written book that is easily accessible to non-nerd readers.

Eric Siegel outlines why and how predictive analytics functions in our day-to-day lives. Alongside the best of modern technologies, forecasting human behavior works wonders, and Siegel describes data as a gold mine that analysts may mine to generate remarkable results.

The strength and plausibility of Siegel’s arguments are highlighted by well-known instances like Chase Bank, Obama’s election, the NSA, the BBC, Hewlett-Packard, Wikipedia, Netflix, and MTV. The book offers a thorough grasp of the significance of prediction in the data science sector.

7. Storytelling with Data: A Data Visualization Guide for Business Professionals

By Cole Nussbaumer

While the majority of data analyst course books on data analytics will concentrate on methods for maximizing the use of data and its analysis, this book has a somewhat different approach. Cole Nussbaumer discusses data visualization tools and how information can be turned into a compelling narrative.

Not all data components are beneficial. This book demonstrates how to present data in ways that emphasize its key elements exclusively. The emphasis of the book is on creating and designing graphs, with examples that are simple to apply in everyday settings.

The book is primarily aimed at people who work in corporate settings, although any data practitioner at the beginning level can learn about practical data visualization methods from this resource. It is one of the best data analyst course books.

8. Doing Data Science: Straight Talk from the Frontline

By Cathy O’Neil and Rachel Schutt

This data analyst book offers definitions, explanations, and solutions to numerous data-related conundrums if you find the topic of data analytics to be intimidating or difficult to understand. It describes the development of this expanding topic and how it might be applied in the contemporary economy.

The writers have viewed data science as an interdisciplinary field with a vast array of potential applications. Their ideas are brought to life by real-world examples and historical and socio-cultural insights that aren’t typically found in works of this kind.

9. Python Tricks: A Buffet of Awesome Python Features

By Dan Bader

It takes more than just studying the language’s theory to become an expert Python programmer. It also entails being aware of and abiding by the customs and standards followed by the community. By using clear examples and a step-by-step narrative, author Dan Bader helps readers learn about tried-and-true approaches and the potential of Python in this book.

This book contains best practices and obscure tips to flesh out your knowledge, whether you’re learning the language for the first time or hoping to improve your abilities. It is one of the best data analyst course books.

10. Rebooting AI: Building Artificial Intelligence We Can Trust

By Gary Marcus and Ernest Davis

A persuasive critique of the state of data science today is provided in this book by two thought leaders in the area, who also outline the measures necessary to develop a truly powerful artificial intelligence sector.

The authors describe what is required to progress AI to the next level while building an AI we can trust – in our homes, our automobiles, and our physicians’ offices — by drawing inspiration from the human mind. This book provides a modern, approachable, and fair introduction to the area of artificial intelligence if you’re interested in it. It is one of the best data analyst course books.

FAQs About Data Analyst Course Books

Q1. What challenges are usually faced by data analysts?

You can respond to the question in several different ways. When there isn’t enough data to work with when clients submit data that has been purportedly cleaned but has been made worse, when data isn’t updated, or when there are factual or data input problems, the data may be very poorly formatted.

Q2. What are some of the common issues that data analysts face?

The following are some typical issues data analysts run into:

regular misspelling
redundant entries
Absent values
Illegal standards
different value representations
locating overlaps in data

Q3. Is data analytics a rewarding career?

Yes, it is a rewarding career.

Vaibhav Kakkar