We do data science! More seriously, we are computer programmers who specialize in dealing with huge amounts of information. Companies and governments collect a HUGE amount of information pretty easily. Sales, inventory, employees, user accounts. If you can name it, were probably collecting data on. The issue is that having a giant pile of data is useless if you can't interpret what it all means. A data scientists job is to go over that, and come out with meaningful information. This could be as simple as what products are selling the best or the most profitable, or they could be more complicated like determining the relationships between things, such as "People who buy beer tend to also purchase milk". What we do will vary wildly based on the job. If your in health care, maybe your doing molecule simulations looking for possible useful ones for drugs. If your in logistics, maybe its optimizing trade routes. Pick a field, and you'll find the influence of Big Data and Data Scientists. More on reddit.com

Between forty to a hundred thousand dollars More on reddit.com

Not everyone in the industry agrees on this, but here's my take: If you are applying the scientific method, then you're doing science. Some data scientists do this often: peforming controlled experiments or quasi-experiments using large volumes of data to gain new knowledge. ML is sometimes in this category, and sometimes not. We also do engineering: formulating problems, defining resources and constraints, and designing/building solutions within that space. IMO the Science label is reasonable, but can't realistically be applied to every DS job description. More on reddit.com

I run and hire for a 13 person data science organization. Generally the thing that people forget about DS is that it’s less about having insanely deep technical skills and more about having deep domain knowledge and really good data intuition and creativity. The tools required, python sql, etc are fairly easy to learn (as evidenced by all these bootcamp farms) but the skills that are rarer is a deeper intuition for answering difficult questions through data and putting those answers into production. When in interviewing a junior data scientist will always suggest complex stuff like neural nets even when it’s not required. A senior data scientist will suggest dead simple models and heuristics that are quick and easy to implement. As you’re studying think less about the types of tools you want to use and think more about the types of questions you want to answer. Take courses/do passion projects which give you experience answering those questions and over time you’ll learn the best practices for answering them. As you’re learning get used to working with bad and unclean data. Colleges/bootcamps have a tendency to give you perfectly clean datasets so that you can practice advanced techniques. Instead of starting with a dataset instead start with a question and make part of the exercise finding and cleaning the data. Maybe this involves tapping into public APIs, maybe this involves doing some web scraping, maybe it requires pulling in and coalescing data across multiple sources, regardless the hard part of doing data science is usually not the model itself (models can be written in a few lines of code using modern tooling) but picking useful questions and structuring the answer to that question in an achievable and effective way. Get a bunch of practice doing this and develop a love for the research and not the process and you’ll be well on track! More on reddit.com