Presently we are in the era of automated machine learning (Auto ML), which helps automating most of the machine learning pipeline components such as data understanding, data engineering, feature engineering, model training, hyperparameter tuning, model monitoring, etc. and also there are plenty of such open source Auto ML frame works exists for data scientists to pick and play .so what is left to data scientist is data literacy.
In my view Machine learning is component of three major components of Representation===>Learning====> Optimization , out of which most of the learning and optimization tasks are completely automated by the Auto ML frame works. So we don’t have problem of How to learn and how to optimize due to these Auto ML frame works. The only problem which left to data scientist is how to represent the problem as well as data ,what is the business problem and what are the feature which solve the business problem and most importantly what to optimize ( what is the ideal output of the problem you are looking for and hoe to achieve it ) for this the analyst need to have one key important skill that is called “Data Literacy “ skill, This is going to be the one of the most key differentiator to evaluate the people in future.
Data literacy skill is the one of the critical thinking, which requires converting the business problem in to data problem as well as creating /finding the relevance data from the big data and also as being machine learning methods are heuristic in nature (There is no hard and fast rules for coding/modelling/performance) and repetitive, we need the data literacy skills to validate the solution and use them
Therefore for all the upcoming data scientists/Data Analytics please focus more on understanding domain( whatever you are working) and fundamentals of mathematics and statistics along with visualizations and storytelling capabilities as data science is not a code it’s a thought.
