Book Review #1: Confident Data Skills by Kirill Eremenko
Looking for ways to help people in taking first steps in a Data Science career, I decided to share some books I can recommend to newbies in Data. When you are starting your journey with Data probably you will meet two guys: Kirill Eremenko and Jose Marcial Portilla and I won’t be wrong If I’ll say, that they are two of the most known trainers in the Data Science world. Kirill besides creating great courses, also wrote a book — “Confident Data Skills” that I had the pleasure to read recently.
About the book, I heard it in a non-typical way. I haven’t found it in a bookstore or on a blog. It was recommended by one of my lecturers at Da Vinci College, where I am currently a Bachelor's Degree student in Data Science. It was recommended as a great guide for a start in the career. Despite having 5 years of experience, a recommendation from the lecturer, and Kirill’s reputation, convinced to buy the book.
What is the book about?
Everything!…
…
…
Joking.
But about everything that you need to know at the beginning of your career. The book is divided into 3 parts that are divided in total into 10 chapters. Trying to group the topic of the chapter into one, they are about:
- Approach to the data, what is data, data future
- How to gather, prepare data and analyze it, then how to visualize it
- Tips and tricks for a career start
What is great, the book is written in a very simple and friendly language that makes it understandable for everybody. You don’t need a deep Data Science experience to get through it and that is a big plus. In the end, it is a book about the start of a career. Another great input is real-life examples of use cases of certain topics in the book from top industry companies. It makes you sure, that theory is not only a theory, but a real world problem.
Part 1: What is Data Science?
In the first part of the book, Kirill is going through the basic definitions of data science and data history and how we got to our point of talking about “Big Data”, “Artificial Intelligence” etc. It is not only a story about data, but as well about Computer Science (well, these worlds are connected). Mentioned is also a role of data in our everyday life. To be honest, I did not enjoy in 100% division of data needs by Maslov Piramid, it makes me feel that it is quite exaggerated in its esthetic approach to the topic, but it is an author's creation. The value of content in this part is in the end worthy to read because it is touching examples, of how data is touching parts of our life.
In the end, we can find an explanation of Artificial Intelligence subject — what it is, and how it works. I personally think that it is always worth reading about it because, at the beginning of this journey, a definition of Data Science and Artificial Intelligence might be quite misunderstood.
Part 2: Working with Data
The longest and main part of the book is describing the whole process of Data analysis (in this term, I mean the whole part from preparing data, analyzing, and visualizing). Personally, despite working already in the data industry I learned a lot from this part.
In the beginning, we have a description of issues and possible approaches to the dirty data problem, problems with access to data, etc. Despite that to be honest, this part was quite tiresome for me, I really enjoyed the part about “Saying No”. That was something that no one teaches you and you don’t know if you can sometimes say it. This part was something when I said to myself — “Now I know that this guy was working in this industry”. I mentioned being tired of this part. Well. Cleaning and preparing data is not the most entertaining part. It is important, we all do it but well…
On the other hand in the beginning, together with the “Saying No” part we had a whole description of how to approach the problem. It is great to master this skill cause it will save You a lot of time in the future.
The most entertaining part was the chapters about classic and modern data analysis. I can always recommend this part because it is touching the most entertaining, fun and some “THAT’S MY JOB!” moments. Even today I am coming back from time to time to this part during my work. Kirill is going through the most known methods of data analysis and is also mentioning advanced samplings, Deep Learning, and probability methods. It is a great way to advance your knowledge.
The data visualization and presentation part is in part 3 of the book, but because of the topic's alignment, I will write about this as well. Kirill is mentioning about most important thoughts about how to visualize the data, what we should remember (tip — about stakeholders), and how to gain attention during the presentation. In the end, in this industry, we are paid for introducing data insights, so doing every perfectly without a good presentation of results is nothing.
Part 3: Career Tips
This part makes this book complete as a book about the beginning of the journey in the data industry. How to find a job? How to prepare yourself for the interview? How to develop your career? Answers to these questions are here.
This part is focused on career growth IN the company, and how to use it in case of future job switch. Kirill is right here and is introducing his tips and experience from his career. He is showing how to grow in the company, by which initiatives, and how to talk about your success.
Sum Up!
I can rate this book a strong 9/10. You need to buy this book when you are starting your career in Data Science.
Pros:
- The book is written in a very easy and understandable language
- It is covering all of the topics that you need to know when you are starting your career in Data Science
- Book is going through all of the data science analytics
- There is a great bonus added to the book! But I won’t say what bonus.
- The theory is supported by real-life examples
Cons:
- Not much, some people with experience in industry might get this book as obvious
- Classic and Modern methods of data analytics I would organize in different way, but it is just a minor comment.
Check out my other posts:
- What you need to know to be a Data Analyst — Technical Skills
- 12 Soft Skills Every Product Analyst Needs to Succeed
- 6 findings after introducing ChatGPT to my Data Analyst work
