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BTMR Solutions' Book Club Recommends: 5 Must-Read Books on Data Science and Gender Bias

Updated: Apr 9, 2023

BTMR Solutions’ Book Club recently met to discuss and review five must-read books on data science. These books were chosen to cater to both beginners and experts in the field, and to provide a diverse range of perspectives on the topic. Here are the five books and our club’s thoughts on them:


  1. Invisible Women: Data Bias in a World Designed for Men (2019) by Caroline Criado Perez - This book highlights the bias and discrimination that is baked into our systems due to the failure of much data to take into account gender. It dives into women’s lives at home, the workplace, the public square, the doctor’s office, and more. It’s an eye-opening read that sheds light on the root cause of gender inequality.

  2. Data Science from Scratch: First Principles with Python (2019) by Joel Grus - This book is a practical guide that helps beginners build a strong foundation in data science using Python. The author has a great amount of knowledge on the fundamentals of machine learning, and the book provides readers with a crash course in Python.

  3. Fundamentals of Data Engineering: Plan and Build Robust Data Systems (2022) by Joe Reis and Matt Housley - This book takes a deep dive into the world of data engineering and provides readers with a new point of view, especially of data consumers. It’s a practical guide that covers basic concepts such as data generation, data storage, data ingestion, and data transformation.

  4. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016) by Cathy O’Neil - This book highlights how big data can be misused to increase inequality and threaten democracy. It’s a thought-provoking read that provides a balanced tour of the many benefits and downsides of our continuing infatuation with data.

  5. Doing Data Science: Straight Talk from the Frontline (2013) by Cathy O’Neil and Rachel Schutt - This book provides an insider view of what it’s like to be a data scientist. It’s a great starting point for beginners, and those who find books on data science too technical or difficult to understand. It’s a valuable resource full of real-life examples.



1. Invisible Women (2019) Data Bias in a World Designed for Men by Caroline Criado Perez


During our book club meeting, we discussed Caroline Criado Perez’s (2019), Invisible Women: Data

Bias in a World Designed for Men. This enlightening read fostered difficult but necessary conversations surrounding the inherent gender bias and discrimination that is present in our society due to the failure of much data to take into account gender. While not surprising, we were discouraged to be reminded about the cost that women pay for this bias in various aspects of their lives, including their finances, time, and sometimes even their lives.

As a data science consulting firm, this book raised important questions for us about how we approach data analysis and how we can ensure that our work is inclusive and does not perpetuate gender bias. We discussed the importance of incorporating diverse perspectives in our work to ensure that we are not overlooking any biases in our analysis. We also talked about the need to consider the potential implications of our work on different groups of people and to ensure that we are not perpetuating any harmful stereotypes or biases.

Moreover, this book highlighted the need for us to be aware of potential biases in the data that we receive from our clients. We discussed the importance of engaging in open and honest conversations with our clients to ensure that we are aware of any potential biases in the data we are working with. This allows us to address any biases and ensure that our analysis is accurate and inclusive.

In conclusion, Invisible Women is an eye-opening read that sheds light on the root cause of gender inequality. It is a call to action for all of us to be aware of gender biases in our work and interactions and to take steps to ensure that we are not perpetuating them.

2. Data Science from Scratch: First Principles with Python (2019) by Joel Grus


As a practical guide for beginners, "Data Science from Scratch" (2019) by Joel Grus provides a great foundation for those starting in the field of data science. The author's expertise on the fundamentals of machine learning is evident throughout the book, and his explanations are easy to understand. The book also offers a crash course in Python, which is essential for anyone looking to learn data science. We found this book to be an excellent starting point for those new to the field, and its practical approach helped us build a strong foundation for our work in data science. Not quite a book club selection but important to include in BTMR Solutions' bookshelves (especially for interns).



3. Fundamentals of Data Engineering: Plan and Build Robust Data Systems (2022) by Joe Reis and Matt Housley


During our book club discussion of Fundamentals of Data Engineering: Plan and Build Robust Data Systems (2022) by Joe Reis and Matt Housley, we explored the importance of data engineering in building robust data systems. We discussed the various aspects of data engineering that are covered in the book, such as data generation, storage, and transformation, and how they are critical in the process of extracting value from data.

One of the key takeaways from the book was the importance of planning and building data systems with scalability and flexibility in mind. We discussed how companies and organizations must be prepared to handle an increasing amount of data, and how data engineering plays a critical role in enabling this scalability.

Another interesting point that was raised in our discussion was the significance of understanding the needs and requirements of data consumers. We talked about how data engineering involves not only managing and processing data, but also delivering it to the right people at the right time, in a format that is useful and actionable for them.

As data scientists and professionals working in the data industry, we also reflected on how the knowledge and concepts covered in the book relate to our work and interactions with clients. We discussed how understanding data engineering concepts can help us communicate more effectively with clients and build data systems that meet their specific needs.

Overall, Fundamentals of Data Engineering is an essential read for anyone working in the data industry, whether you are a data scientist, data engineer, or data analyst. It provides valuable insights into the key concepts of data engineering and how they can be applied to build robust and scalable data systems that deliver value to organizations and their consumers.


4. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2016) by Cathy O’Neil


During our BTMR Solutions' book club meeting, we also discussed Cathy O’Neil’s (2016), “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy”. The book provided a lot of food for thought, and though it was a bit sensational in places, it did raise some important points and questions.

One of the main takeaways was that data can be used to perpetuate inequalities, and that many algorithms used by institutions such as banks and schools are inherently biased. The book gave many examples of how algorithms can exacerbate the effects of poverty, racism, and other social problems, often without us even being aware of it.

We also discussed how these issues affect our work culture and interactions with clients. Many of us work in the data science field and are responsible for designing algorithms and systems that are fair and unbiased. We reflected on the importance of being aware of the potential biases in our data and algorithms, and of being willing to challenge assumptions and biases when designing our systems.

Overall, while the book was a bit alarmist in places, it did raise some important questions about the role of data in our society and the need for ethical considerations in the data science field. We would definitely recommend this book to anyone interested in the ethical implications of big data and machine learning.

5. Doing Data Science: Straight Talk from the Frontline (2013) by Cathy O’Neil and Rachel Schutt


Doing Data Science: Straight Talk from the Frontline (2013) by Cathy O’Neil and Rachel Schutt - Our book club found this book to be a great starting point for beginners, as well as those who find books on data science too technical or difficult to understand. It provides an insider view of what it’s like to be a data scientist and is full of real-life examples. The book raises important questions about ethics and transparency in data science and sparked a lively discussion about how we can ensure that our work as data scientists is responsible and unbiased. We also appreciated the authors' focus on practical applications and tips for effective communication with both technical and non-technical audiences. Overall, a valuable resource for anyone interested in data science.

Join us!

Through our book club's discussions, we found that these books, despite their different approaches, all highlight the importance of being mindful of the impact that data science has on society. We realized that the advancements in data science require an increased awareness of data privacy, ethics, and inclusivity. These issues are especially important in today's world where data is becoming increasingly pervasive.

We highly recommend these books to anyone interested in data science or wanting to learn more about the impact it has on society. Our book club found these books to be both insightful and informative, raising important points and questions that sparked lively discussions. We invite our readers to join our book club to continue these discussions, remotely and once a month, and to also like this blog post to show support. Thank you!





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