Data Scientist Vs Data Visualizer Vs Business Intelligence Vs Machine Learner Vs Stastistician Vs DBA all have at least something in common and gap.
Business Intelligence
Business Intelligence is particular approach to a particular problem. BI engineer is not expected to consume their own data products, and perform their data analysis, and make the business decisions themselves. Usually they build tools for others to make decisions with.
Data Scientist
Statistics are at heart of what a data scientist does, day-to-day. They are comfortable for any data they encounter with will be available at single place as the most information possible from a very sparse, expensive to collect and, work on a small data set.
Database Programmer
Database programmers bring a lot of skills and expertise. However, looking the diverse data from different sources like graph nodes, and vectors, there is less chance of incoming relational data, which may or may not be right tool and even the concepts that transcend in particular system.
Like Statisticians, they are concerned with limited data to visualise from single source rather than multiples.
Machine Learner
Machine Learners helps bring reality to the concept of performing predictions on the data. However, it is more like engineering process where technique, and applying it, and running it is a fairly small fraction.
Data Scientists does both.
Data Scientist is a new engineering to work on massive datasets and perform their analysis on disparate data rather than do a sampling, or on assumption. However, model and methods remain same.
Data scientists work on disparate and diversified datasets including non-relational and different formats other than text – audio, video, binary format, helping them to perform deep analytical and bring insights.
Data Scientists conceptualize how the final data should be interactive which help business decision makers to decide based on available historical data.
Data Scientists prepare model, methods, and techniques, how machine learning will help in their data application. There is a lot of work involved before machine learning stage – data manipulation, data wrangling, cleaning, massaging, data jujitsu, data mugging.
The Gap between Data Scientist, Data Visualiser, Business Intelligence, Machine Learner, Stastistician and DBA
Learn how to do statistical modelling and communicate results with business group and decision makers.
Learn to deal with data that does not fit from a single source.
Learn to deal with the unstructured data.
Learn about algorithms and tradeoffs at scale.
Learn to do statistical analysis, data wrangling from multiple sources before performing machine learning process.
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