Author: Emily Robinson, Jacqueline Nolis ISBN: 1617296244
I wanted to read this book since a lot of people sometimes comes to me for advice on how to start their career. Since I started mine in a "pretty conventional" way, that was my only way to respond, which wasn't particularly helpful to all the people who asked, my hope with this book was twofold: knowing what else is out there and knowing if this was a book that I could point people to; and it is.
As people can guess from the book's title, this book is written and tailored towards someone who is taking the first steps into their data science career; however, there are some bits that even those with experience may find helpful.
The book is neatly organised in chapters with very descriptive titles, each one of them representing a stage in a data scientist career, and they are divided into sections:
- Getting started with data science
- What is data science?
- Data science companies
- Getting the skills
- Building a portfolio
- Finding your data science job
- The search: Identifying the right job for you
- The application: Resumes and cover letters
- The interview: What to expect and how to handle it
- The offer: Knowing what to accept
- Settling into data science
- The first months on the job
- Making an effective analysis
- Deploying a model into production
- Working with stakeholders
- Growing your data science career
- When your data science project fails
- Joining the data science community
- Leaving your job gracefully
- Moving up the ladder
Though you can read the whole book from beginning to end, I feel you can also get value from keeping it and reading it when the specific stage of your career.
An exercise that I really liked was the creation of "fake" companies to explain in broad terms most of the possible environments where a person may end up working on. It is good to give an idea of how diverse a data scientist's responsibilities can be, based on their environment.
I found it interesting that they put their two cents into, what seems to be a highly controversial topic: traditional universities or bootcamps, and even adding discussing other two ways in which someone can learn: self learning and doing data science at your current job. I enjoyed that discussion.
An excellent addition to the book's topics are the short-form interviews included at the end of each chapter. You get to know how it is out there for other data scientist, and you get some tips from well-known people who are actively doing data science in the industry.
There is zero code in this book as it is more of a "soft skills" kind of book, so don't expect any deep technicalities inside. But don't get discouraged; as a bonus, there is an appendix with some common interview questions for a data science role.
In reality, data scientists spend a lot of time cleaning and preparing data, as well as managing the expectations and priorities of other teams.
Data science is the practice of using data to try to understand and solve real-world problems.
Something good to remember is that you are best positioned to teach the people a few steps behind you.
Think about how attached you are to the data scientist title. If you decide not to concern yourself with what you are called and to instead focus on the work that you're doing, you'll have a lot more flexibility finding jobs.
- Having a good portfolio is not just about having a lot of code on your GitHub account, but also being able to explain, that's where blog posts come in handy
Well the book has links and references at the end of each chapter, I found all of them interesting.