Everything related to data is data science

The most important part is the Data Science application, all kinds of applications. Yes, you have read correctly, all kinds of applications, for example machine learning.

The data revolution

Around the year 2010, with a large amount of data, it made it possible to train machines with a data-driven approach instead of a knowledge-based approach. All theoretical papers on recurrent neural networks supporting vector machines became feasible. Something that can change the way we live, how we experience things in the world. Deep learning is no longer an academic concept found in a thesis paper. It became a tangible and useful learning class that would affect our everyday lives. Thus, machine learning and artificial intelligence dominated the media, eclipsing all other aspects of data science, such as exploratory analytics, metrics, analytics, ETL, experimentation, A/B testing, and what traditionally It was called business intelligence.

Data Science: The General Perception

So now, the general public thinks of data science as researchers focused on machine learning and AI. But the industry is hiring data scientists as analysts. So, there’s a misalignment there. The reason for the misalignment is that yes, most of these scientists can probably work on more technical problems, but big companies like Google, Facebook, and Netflix have so much at their fingertips to improve their products that they don’t need to acquire more learning. automatic. or statistical knowledge to find these impacts in your analysis.

A good data scientist is not just about complex models

Being a good data scientist is not about how advanced your models are. It’s about how much impact you can have on your work. You are not a data shredder, you are a problem solver. You are a strategist. Companies will give you the most ambiguous and difficult problems and hope that you will lead the company in the right direction.

The job of a data scientist begins with collecting data. This includes user-generated content, instrumentation, sensors, external data, and logging.

The next aspect of a data scientist’s role is to move or store this data. This involves unstructured data storage, reliable data flows, infrastructure, ETL, pipelines, and structured data storage.

As you progress through the job required for a data scientist, the next one is to transform or explore. This particular set of work encompasses preparation, anomaly detection, and cleanup.

Next in a data scientist’s job hierarchy is data aggregation and labeling. This work involves Metris, analysis, aggregates, segments, training data, and features.

Learning and optimization form the next work set for data scientists. This working set includes simple machine learning algorithms, A/B testing, and experimentation.

At the top of the pack is the more complex job of data scientists. It consists of Artificial Intelligence and Deep Learning,

All this data engineering effort is very important and it is not just about creating complex models, there is much more at work.