Top 10 Books on Decision Making to Read in 2023

Data science uses complicated business models, data visualisation and writing codes to create maximum impact for an organisation. Now, this impact could be in the form of multiple things. It could be in insights, data products or even product recommendations for a company. 

A data scientist needs to create models, make data visualisation, and write codes to create tools. However, the essence of the job lies in taking the company ahead by taking crucial decisions and impacting its business. Essentially, the data science field concerns solving real problems of a company by analysing real-time data using advanced tools. First, let us see the topmost books for data science enthusiasts. 

Top 10 books on Decision Making

Working as a Data Scientist is super exciting and enriching. But the catch is to know where exactly to start and how to climb the leaderboard when it comes to decision making. 

Hence, we have compiled for you the following list of the ten most popular books for people keen on excelling in the field of Data Science and decision making:

1. Business analytics – the science of data-driven decision making by U. Dinesh Kumar 

This fantastic in-depth book discusses the theory and practical applications to provide a well-rounded understanding. The author takes a sophisticated approach and gives numerous simple case studies to learn, comprehend, and follow. It includes all you must know about data science, including economics, statistics, and finance. Plus, it contains statistical and analytical tools and machine learning approaches, effectively combining fundamental and high-level topics.

2. Algorithms to Live By: The Computer Science of Human Decisions: by Brian Christian & Tom Griffiths:

Would you be willing to apply computer algorithm based metaphors to your decision-making abilities? 

This book takes the same approach to decision-making. It will help you master critical aspects of decision-making through extensively researched inputs, unique perspectives, and practical ideas. You’ll figure out the impact computer algorithms have on precise decision making. 

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3. Data Science for Business and Decision Making by Patricia Belfiore and Luiz Paulo Favero

This is the best book you will ever read if you want to learn more about data science and decision making. While most textbooks focus on either one of these concepts, Data Science for Business and Decision Making covers both statistics and operations research. So, learners wanting to use quantitative methods in their job can rely on the well-described concepts of business analytics. Its focus reflects the relevance of regression, optimisation, and simulation to business analytics practitioners.

4. Thinking with data by Max Shron

This book gives the reader much important information and encourages critical business thinking. It allows you to understand why things are the way they are. As you progress through the chapters, you’ll learn how to ask intelligent, relevant questions, jot down vital elements of a concept, and focus on key facts. 

Data-specific reasoning patterns encourage you to consider “why” rather than “how.” It covers the CoNVO (context, needs, vision, and outcome) framework. Each chapter is organised in a didactic fashion with problems and answers. This book also teaches business analytics fundamentals by combining statistics and operations research models.

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5. The Decision Maker’s Handbook to Data Science by Stylianos Kampakis

The second edition of The Decision Maker’s Handbook to Data Science will teach you how to think like a seasoned data scientist and approach business problems in a completely new light. Author Stylianos Kampakis introduces you to the skills and tools needed for creating a sound data strategy that delivers excellent results in the long run. The frequent traps that Kampakis helps you avoid while helping you build a thriving company culture are ethical and legal difficulties surrounding data collecting and algorithmic bias.

6. Behind Every Good Decision by Puneet Sharma and Piyanka Jain 

The authors show how professionals can analyse the available information to make actionable decisions to boost revenue, decrease costs, improve products, enhance customer satisfaction, etc., in five simple steps—using only Excel as a tool. This book discusses the four essential analytic strategies that can help business leaders solve 80% of all business problems, with plenty of examples and exercises. It doesn’t take a mathematician to figure out that you’ll need a formula!

7. Data Science for Decision Makers & Data Professionals: Creating an Intelligent, Data-Driven Organization with AI, BI & Big Data by Daan Van Beek:

Over 200,000 copies of this top decision-making book have already been sold. This book covers every facet of data science implementation, including the algorithms that make your judgments more refined and effective to the people, skills, culture, and mentality required to make it happen. 

How do you choose the correct KPIs and targets for your business? What are the organisational structures of the most data-driven companies? What is the purpose of a data warehouse or data lake? What is the best way to handle a data science project? This book answers every question about putting data science into practice.

8. Data Science and big data analytics

This concise and well-structured book allows you to discover what works and what doesn’t in business and life. It introduces big data and its importance in today’s technologically competitive world gently. The complete data analytics lifecycle is detailed and discussed, along with a case study and engaging visuals, so you can see how the system works in practice.

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9. Data-Driven Decision Making using Analytics by Surabhi Bhatiya, Parul Gandhi, and Kapal Dev

This book seeks to explain Data Analytics in terms of models and algorithms, theoretical principles, applications, and experiments in relevant areas or focused on specific concerns to help people make better decisions.

It talks about database technology, machine learning, knowledge-based systems, high-performance computing, information retrieval, identifying patterns buried in massive datasets, and data visualisation. Pattern mining, clustering, classification, and data analysis are among the paradigms covered. The overall goal is to deliver technological data analytics and data mining solutions.

10. Storytelling with Data by Cole Nussbaumer Knaflic 

This book goes into data visualisation and communication to help you tell compelling stories based on factual data. The author provides a good mix of theory and real-world examples applicable to your daily work. This book teaches how to draw insights from data and display them in ways legible to both tech and non-tech stakeholders using both standard and non-conventional approaches.


The demand for data scientists is considerably high across a variety of sectors. Data scientists and data analysts help improve a company’s core business processes for the better. They help decision-makers make crucial business decisions that can deliver tangible results. Together, data science professionals and business leaders solve modern world business problems. 

If you’re keen on exploring the data science domain, check out the Professional Certificate Program in Data Science for Business Decision Making on upGrad. This course gives you a specialisation in java, python and many latest programming languages. Curated by top-notch experts in the field, including IIIT Bangalore and LJMU alumni, this course is a total game-changer concerning its syllabus, quizzes and study material.

upGrad has a giant global learner base with 40,000+ subscribers. It is spread across approximately 90 countries. Every learner gets 360-degree assistance with career prospects and dedicated support throughout the course. In addition, skilled mentors organise engaging and live interaction sessions for learners. Students also can indulge in peer learning opportunities that expose them to new ideas and opportunities. 

Q1: Which is a mandatorily required skill that a data scientist needs to have?

Answer: Apart from Machine learning and AI, decision making is a skill that a data scientist needs compulsorily to be proficient in problem-solving abilities.

Q2: Can someone pursue a data scientist learning on their own?

Answer: Honestly, the answer is No. To become a data scientist, the person must have a set of professional education qualifications from recognised universities or colleges. Learners can enrol in online classes on programming languages like Java, SQL, or Python.

Q3: What is the approximate duration required to become an expert in decision making?

Answer: The ideal time required is 2-3 years of a full-time course. However, you can also opt for workshops and boot camps that last for a shorter period.

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