Books, Data Analysis
Meet the Author of Effective Data Analysis: Mona Khalil
The complete interview with Mona Khalil, the author of Effective Data Analysis, published by Manning Publications
Today I have the pleasure of interviewing Mona Khalil, author of the book Effective Data Analysis by Manning Publications. As always, before getting to the heart of the interview, I’ll reveal some behind-the-scenes secrets.
Browsing the book catalog on the Manning website (which is always full of new proposals), I found Mona’s fascinating book. As soon as I started reading it, I found everything I was looking for about data analysis. In fact, we often read books that deal with a single aspect of data analysis. Here, however, you also find aspects that are not normally analyzed. All in one book!
As usual, after reading the book, I contacted Mona on LinkedIn — because I really like being able to speak directly with the author of a book — and I proposed the interview to her. She immediately accepted.
To tell you the truth, I was slow in sending her the questions since I’ve been busy with a million things lately. But in the end, we did it.
Here’s the interview!
A small spoiler: read the answer to question number four carefully because I think it gives some interesting suggestions for all data analysts and aspiring data analysts.
Enjoy the reading!
1 Dear Mona, please introduce yourself briefly by highlighting your skills and interests.
Hi Angelica! I’m a data analytics professional with over 10 years of experience in the field. I’ve worked across industries, starting in academia and transitioning to the public sector, ed-tech, and eventually HR tech. I studied psychology and statistics in undergrad and grad, which informs my focus on data for strategic decision-making. In the past 3 years, I transitioned to a managerial career and have experience leading data science, machine learning, product operations, and analytics engineering teams.
2 What motivated you to write a book about “data analysis”?
After stepping back from my PhD program, I started my career as the sole data analyst at a school. I struggled to calibrate my communication style to my stakeholders, often rewriting reports multiple times until the findings made sense and met their needs. After a lot of mistakes, I discovered that my academic training didn’t prepare me for many of the realities of an analyst’s career.
I’ve since heard of so many similar stories from other analysts struggling to get started in their careers. It takes experience and guidance to learn the many skills of the job, and that’s sadly often missing in small organizations. Analysts with a technical education have to rapidly learn how to communicate with stakeholders who may have limited data literacy skills without the presence of colleagues or mentors to support them.
I wrote this book informed by the experiences of my colleagues, mentees, and myself. It’s designed to be a mentor and a growth resource to you–one I know that I would have benefitted from in my own career.
3 What challenges did you encounter while exploring and writing about this complex topic, and how did you address them?
One of my primary goals was to make this book relevant to analysts with different specializations and in different industries. To do so, I included at least one case study in each chapter of an analyst applying the concepts you’re reading about. I tried to vary the industry, type of organization, and specialization of the analyst in order to give my readers a comprehensive perspective on the field.
I have experience with some of these specializations, but definitely not all! I reached out to some amazing friends and colleagues in revenue, finance, marketing, and business analysis to inform these case studies and learn how they might apply the topics covered in the chapter. I made some new connections along the way, and learned a lot more about the field of data analysis as a whole.
4 In your opinion, what are the main characteristics that an effective data analyst must have (for example, curiosity, accuracy, etc.)?
I strongly recommend starting your learning journey by developing an analytical mindset. In practice, this means that you develop some key habits:
- Asking clear questions that can be measured and tested
- Questioning your assumptions and previously held beliefs
- Admitting when you don’t know something and taking steps to finding out
- Being curious about your data and looking at it in multiple ways–even if some of those ways don’t confirm your hypotheses.
The many skills you learn in analytics–statistics, SQL, Python programming, machine learning–are all mastered with an analytical mindset that guides how you use them.
5 In your book, you talk about statistics that the reader probably knows and statistics that they probably don’t know. On what basis did you make this distinction?
For years, I taught and tutored statistics courses that included parametric statistical tests (e.g., t-test, ANOVA) and regression in their curricula. I discovered how similar the topics were across institutions, focusing mainly on the computation of each test or writing code to do it for you. They rarely include information about the history and logic underpinning those tests and alternatives to use when that logic fails.
This is incredibly concerning, because students from many disciplines go on to use these skills in their professional lives without an understanding of the tests they’re running besides the fact that some comparisons are being performed on a dataset. When only those tests are used, you can easily generate misleading or inaccurate results.
As such, I start the fourth chapter by diving into the underlying meaning and interpretation of the statistical tests covered in academia. You’ll learn details that are often overlooked because they’re challenging to teach, as well as how to think strategically about what your results mean. The fifth chapter covers a broad range of non-parametric statistical tests that you can use when your dataset doesn’t meet the requirements necessary to use the tests covered in the previous chapter.
6 What takeaways do you hope readers will gain after finishing your book?
By reading this book, I hope that learners will build confidence in their ability to solve new problems and take on new types of work that they encounter in their career. I hope they can hone their communication skills, build a strong foundation that makes them a trusted member of an organization, and continually learn new skills that help them grow their career. I hope this book can be a guide and a mentor throughout their analytics journey.
Conclusions
Thank you, Mona, for your answers and your availability!
If you want to learn more about how to become an effective data analyst, you can find more information here.
You can also read my full review of the book here.
That’s all for today, see you next time!
