Data science is a very broad term from building data infrastructure to running ML algorithm to solve business problems(i.e. Label, Rank, Forecast). Very few can do it all well. The favorite word for this people in the industry is Unicorn. (I rather use the world Renaissance).
I remembered my 1st data analyst interview when I was kindly advised against doing A to Z. The takeaway of the interview is I got to focus.
In bigger organisations, there exists separate roles in the field of data analytics. Each data profession contributes his or her area of expertise from Architecting to Modeling. In smaller outfit, the same data analyst might be doing end to end work.
Architecting
Creating a system that can take in data in its raw form, process it, store it, use it and get the results to the program or end user that needs them. Open-source technologies like Hive, Pig and Spark are the tools of trade. For smaller entity, this architecting part might be outsource to the cloud. I do not see myself working in this area. However, I am compiling a high level guide to be comfortable with.
Experiment design
Creating a plan to collect enough data of the right type to give meaningful results.
Data Wrangling
Creating clean and useful data from messy real world collections from disparate sources and concatenate them together .
Data Modeling
Creating a mathematical representation of your data.
What is my focus?
I would love to do Experiment Design, Data Wrangling and Data Modeling all together. It a multi-step process of data analysis. These steps brings variety to the job.
[1] https://www.datacamp.com/community/tutorials/data-science-industry-infographic
[2] https://www.betterbuys.com/bi/comparing-data-science-roles/