Reading Recommendations For Those Entering The Profession Of Data Science

The data science course covers various topics, from the technical building of models to philosophical and ethical issues like privacy and discrimination. The data science institute offers data science certification programs to students and conducts data science classes online and offline.

The studies and strategies in these books could be helpful for anyone who works with data.

Books about numbers and "Big Data."

1. The Data Detective: Ten Easy Rules to Make Sense of Statistics 

Hartford breaks down the complicated web of statistics by giving 10 ways to look at data that consider biases and fill in knowledge gaps.

2. Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are 

This book gives different points of view on the economy, sports, gender, and other things that are affected by numbers.

3. Naked Statistics: Stripping the Dread from the Data 

Wheelan explains basic ideas in statistics like inference, correlation, and regression analysis by making comparisons to popular culture and using language that is not too technical.

4. Algorithms of Oppression: How Search Engines Reinforce Racism 

Noble researches how data discrimination happens in search engines like Google, which use biased algorithms that favour white people and hurt women of colour.

5. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor 

Eubanks looks at how data mining, policy algorithms, and predictive risk modelling hurt the working class and the poor more than they break other groups. 

6. Race After Technology: Abolitionist Tools for the New Jim Code

Benjamin researches to determine if the rise of automation may have played a significant role in racism and white supremacy. She calls her idea "The New Jim Code," The instructions that go with it look at how biased design makes society more unequal.

7. Invisible Women: Exposing Data Bias in a World Designed for Men 

Perez looks at how data that doesn't take gender into account keep bias and discrimination against women alive in how society makes decisions about healthcare, education, and policy.

8. Data Feminism

This book guides how feminism and data can be used together for social justice. The title of the book is Data Feminism. It shows how statistics can be used to eliminate systemic biases and improve things for people who are often hurt by skewed data.

9. Brotopia: Breaking Up the Boys' Club of Silicon Valley

Chang's expose of the "bro" culture in venture capital firms. Tech companies are not limited to data science, but it does show situations that women often face when they work in places where men are in charge.

10. The Ethical Algorithm: The Science of Socially Aware Algorithm Design 

This set of solutions is based on the new field of socially aware algorithm design, which was made in response to the growing number of privacy issues and basic human rights violations caused by technology that goes too far.

11. The Algorithmic Foundations of Differential Privacy 

Dwork and Roth look at ways to analyze data that protect people's privacy and give an overview of the many problems and solutions related to people's privacy.

12. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power 

Zuboff says that surveillance capitalism is "a new economic system that uses people's experiences as free raw materials for extracting, predicting, and selling them in secret business operations." 

13. Product-Led Growth: How to Build a Product That Sells Itself

In the first book in Bush's Product-Led series, readers will find real-world examples, email scripts, and answers to some of the most challenging business questions in product marketing.

14. Storytelling with Data: Let's Practice! 

The book by Nussbaumer Knaflic and the exercises that go with it is a complete guide to data visualization and the practice of showing information using data. 

15. Data Smart: Using Data Science to Transform Information into Insight 

This book has nine lectures on different data science methods, such as linear programming, Nave Bayes classification, and using Excel spreadsheets to find outliers.