Data Science

The most important part is Data Science’s application, a myriad of applications. Yes, you see clearly right, lots of applications, for instance machine learning.

The Data Revolution

Around year 2010, by having an abundance of information, it made it feasible to train machines that has a data driven approach instead of a knowledge driven approach. All the theoretical papers about recurring Neural Networks supporting vector machines became feasible. Something that may change the way we lived, how you experience things on the planet. Deep learning is not an academic concept that is in a thesis paper. It became a tangible, useful class of learning that might affect our everyday lives. So Machine Learning and AI dominated the media overshadowing another aspect of Data Science like Exploratory Analysis, Metrics, Analytics, ETL, Experimentation, A/B testing and the thing that was traditionally called Business Intelligence.

Data Science – the General Perception

So now, the public thinks of internet data science as researchers focussed on machine learning and AI. But the marketplace is hiring Data Scientists as Analysts. So, there exists a misalignment there. The reason for the misalignment is always that yes, these types of scientists can probably work with more technical problem but big the likes of Google, Facebook and Netflix have a lot of low hanging fruits to further improve their products which they do not need to acquire anymore machine learning or statistical knowledge to discover these impacts inside their analysis.

A good Data Scientist is not merely about complex models

Being a great data scientist isn’t about how advanced your models are. It is about how exactly much impact you could have on your work. You are not a data cruncher, you’re problem solver. You are a strategist. Companies will provide you with the most ambiguous and hard problems and so they expect you to slowly move the company inside right direction.

A Data Scientist’s job starts off with collecting data. This includes User generated content, instrumentation, sensors, external data and logging.

The next component of a Data Scientist’s role is usually 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 within the required help a Data Scientist, the next is transforming or exploring. This particular list of work encompasses preparation, anomaly detection and cleaning.

Next within the hierarchy of be employed by a Data Scientist is Aggregation and Labelling of internet data. This work involves Metris, analytics, aggregates, segments, training data and features.

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

At the top of the set is essentially the most complex work of Data Scientists. It is made of Artificial Intelligence and Deep Learning,

All in this data engineering effort is critical and it isn’t just about creating complex models, there’s a lot more for the job.

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