- Data Journalism Handbook 2 – Online beta access to the first 21 chapters
- Select Star SQL – A book that is also a walk-through interactive tutorial for learning SQL
- Dive Into Deep Learning – A very detailed and up-to-date book on Deep Learning; used at Berkeley. It also includes Jupyter notebooks.
- R for Data Science – Just like the title says, learn to use R for data science.
- Advanced R – A work in progress for the second edition of the book.
The field of data science is moving fast. People are claiming to be data scientists; yet the knowledge, experience, and backgrounds of those people can be very different. Different is not bad. However, there a little standards around what exactly a data scientist is.
Sticking with this week’s theme of “What is a Data Scientist”, an organization titled, Initiative for Analytics and Data Science Standards (IADSS) has kicked-off a research study at global scale. The study aims to gain insight about the analytics profession in the industry and help support the development of standards regarding analytics role definitions, required skills and career advancement paths. This will help set some industry standards which in turn could support the healthy growth of the analytics market.
If you want to be a part of this initiative and help collectively define industry standards, I encourage you to take part in the research. The survey takes approximately 5 minutes and answers for the survey will be kept anonymous. More details are provided at introduction pages of the survey at Data Science Industry Standards Research Survey
Currently, over 12 million users on LinkedIn claim to have data science and analytics capabilities. The field could use some standards around different roles and necessary skills.
The profile of a data scientist is changing slightly as the profession becomes more solidified. Data Science 365 conducts a study to determine some of the characteristics of a “typical data scientist.” The below infographic covers a wealth of information from programming languages used to educational backgrounds to locations. It is definitely worth looking at to understand the attributes of a data scientist in 2019.