What first interested you in data analysis, Python and pandas?

I started my career working in ad tech, where I had access to log-level data from the ads that were being served, and I learned R to provide insights to clients. I wanted to focus more on data analysis, so I switched jobs. After joining another company, I participated in an internal hackathon, during which my hackathon team developed an alerting system. We won the hackathon, which gave me confidence to continue on this path.

I shifted my focus towards coding and transitioned to another team, where I developed and delivered training on R and how to use R for data analysis. I saw people benefit from this work, plus creating the training helped me fill in the gaps in my own knowledge and skill set. We eventually switched to Python, and I learned that on my own, along with machine learning. Through this, it became clear that a working knowledge of pandas is essential for these kinds of data-rich analyses.

In your words, what is your book about? Who is your target audience?

My book is about pandas – you simply can’t do data science in Python without pandas – and it covers data analysis and machine learning. Since data skills have become essential in a variety of fields, the target audience is anyone who has prior data science experience and now wants to move to Python or someone who has experience programming in Python and wants to learn data science.

The book assumes that readers have some Python knowledge, which can be easily learned from a tutorial. It is designed to help someone conduct data analysis and machine learning in Python through examples that use interesting data sets. There are various examples of code and applications of that code rather than just the mathematics and theory.