The most important part is the Data Science app, a variety of applications. Yes, you read right, various applications, such as machine learning.
Around 2010, with rich data, it was able to train machines using data-driven methods rather than knowledge-driven methods. All theoretical papers on repetitive neural network support vector machines have become feasible. Things that can change our way of life, how we experience things in the world. Deep learning is no longer an academic concept in the paper. It becomes a tangible and useful learning course that affects our daily lives. As a result, machine learning and artificial intelligence dominate the media, masking other aspects of data science, such as exploratory analysis, metrics, analysis, ETL, experimentation, A/B testing, and what is traditionally known as business intelligence.
Data Science – General Perception
So now, the general public believes that data science is the focus of researchers on machine learning and artificial intelligence. But the industry is hiring data scientists as analysts. So there is a misplacement there. The reason for the mistake is that most scientists may solve more technical problems, but big companies like Google, Facebook and Netflix have a lot of low cost to improve their products, they don't need to get machine learning or statistical knowledge to analyze These effects were found.
A good data scientist is not just a complex model
Being a good data scientist is not about the advanced nature of the model. This is about how much you can have an impact on your work. You are not a data processor, you are a problem solver. You are a strategist. The company will give you the most vague and difficult problems, and they hope that you will guide the company in the right direction.
The work of data scientists begins with collecting data. This includes user generated content, instruments, sensors, external data and log records.
The next aspect of the role of data scientists is to move or store this data. This involves the storage of unstructured data, reliable data flow, infrastructure, ETL, pipeline and structured data storage.
As you ascend the data scientists to the work you need, the next one is changing or exploring. This special set of tasks includes preparation, anomaly detection and cleaning.
Next, the data scientist's working hierarchy is the aggregation and labeling of data. This work involves Metris, analysis, aggregation, segmentation, training data and functionality.
Learning and optimization are the next set of work for data scientists. This group of work includes simple machine learning algorithms, A/B testing and experiments.
The most important thing is the most complicated work of data scientists. It includes artificial intelligence and deep learning.
All of this data engineering work is very important, it is not just about creating complex models, there is still a lot of work to be done.