Notes

Everything regarding Data Is Data Science
The most significant part is Data Science's application, all types of applications. Yes, you read it right, all types of applications, for instance machine learning.

The Data Revolution

Around year 2010, having an abundance of data, it made it possible to train machines with a data driven approach rather than a knowledge driven approach. All the theoretical papers about recurring Neural Networks supporting vector machines became feasible. Something that can change the way we lived, how we experience things on the planet. Deep learning is no longer an academic concept that is based on a thesis paper. It became a tangible, useful class of learning that would affect our everyday lives. So Machine Learning and AI dominated the media overshadowing almost every other facet of Data Science like Exploratory Analysis, Metrics, Analytics, ETL, Experimentation, A/B testing and that which was traditionally called Business Intelligence.

Data Science - the overall Perception

So now, the general public thinks of data science as researchers focussed on machine learning and AI. However the industry is hiring Data Scientists as Analysts. So, there is a misalignment there. The reason behind the misalignment is that yes, most of these scientists can probably work on more technical problem but big companies like Google, Facebook and Netflix have so many low hanging fruits to boost their products that they do not need to acquire any more machine learning or statistical knowledge to get these impacts within their analysis.

An excellent Data Scientist is not just about complex models

Being truly a good data scientist isn't about how exactly advanced your models are. It is about how exactly much impact you might have on your work. You aren't a data cruncher, you are a problem solver. You're a strategist. Companies gives you the most ambiguous and hard problems plus they expect one to guide the company in the right direction.

A Data Scientist's job starts with collecting data. This consists of User generated content, instrumentation, sensors, external data and logging.

The next facet of a Data Scientist's role is to move or store this data. This involves the storage of unstructured data, flow of reliable data, infrastructure, ETL, pipelines and storage of structured data.

As you move up the required work for a Data Scientist, the next one is transforming or exploring. Business Analysts of work encompasses preparation, anomaly detection and cleaning.

Next in the hierarchy of work for a Data Scientist is Aggregation and Labelling of data. This work involves Metris, analytics, aggregates, segments, training data and features.

Learning and Optimizing forms another set of work with Data Scientists. This group of work includes simple machine learning algorithms, A/B testing and experimentation.

At the top of the set may be the most complex work of Data Scientists. It includes Artificial Intelligence and Deep Learning,

All this data engineering effort is important and it is not just about creating complex models, you will find a lot more to the work.
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