Data Science and Data Engineering

So this is the world of technology. And This entire world is now almost relying on technology. And Yes! that is true. Our business our education our technical life each and everything is depending on technology and science of course. And when there are technology and science there is also a word appears behind which is DATA. Data is everywhere. We are using data to find results of anything.
No matter what is happening data is mainly responsible for everything. And thus Data has been introduced in the field of science and technology. And then it was divided into two more parts in terms of its usability and productivity, which is Data Science and Data Engineering.  
Most of the people out there often mix up between Data Science and Data  Engineering.
But the truth is these both are totally different. They both have their own task but they are both connected with each other.

So let's start with Data Science.

• What is Data Science?

Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them.

Data science is everywhere. It uses for Statistical research, Mathematics, Data processing and most importantly on Computer science as well as Machine Learning which is now a great game changer on technology. 

In terms of mathematics, data science contributes a lot. But in present in the field of computer technology and science machine learning and deep learning are ruling the most. 

Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. It is closely related to computational statistics. Besides, it used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.

Side by side Deep learning is one of the only methods by which we can circumvent the challenges of feature extraction in machine learning. This is because deep learning models are capable of learning to focus on the right features by themselves, requiring little guidance from the programmer.

Therefore, we can say that Deep Learning is:

 1. A collection of statistical machine learning techniques

 2. Used to learn feature hierarchies

 3. Often based on artificial neural networks.

Reference:  https://www.quora.com

• What is Data Engineering?

Data engineering includes what some companies might call Data Infrastructure or Data Architecture. The one who gathers and collects the data, stores it, does batch processing or real-time processing on it and serves it via an API (application programming interface) to a data scientist who can easily query it. And one who does all these is called a Data Engineer.

Data engineering means a good Data engineer has to gain good computing skills along with extensive knowledge on databases and best engineering practices. This includes some backend systematic skills like handling and login errors, monitoring the system, building human-fault-tolerant pipelines, understanding what is necessary to scale up, addressing continuous integration, knowledge of database administration, maintaining data cleaning, ensuring a deterministic pipeline and so on.

It all requires several types of software engineering skills and experience. 

Data Engineers' Responsibilities

The data engineer is someone who develops, constructs, tests and maintains architectures, such as databases and large-scale processing systems. The data scientist, on the other hand, is someone who cleans, massages, and organizes (big) data. 

Data engineers deal with raw data that contains human, machine or instrument errors. The data might not be validated and contain suspect records; It will be unformatted and can contain codes that are system-specific. 

Lastly, to deliver the data to the data science team, the data engineering team will need to develop dataset processes for data modeling, mining, and production. 

Data Scientists' Responsibilities

Data scientists will usually already get data that has passed the first round of cleaning and manipulation, which they can use to feed to sophisticated analytics programs and machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling. Of course, to build models, they need to do research industry and business questions, and they will need to leverage large volumes of data from internal and external sources to answer business needs. This also sometimes involves exploring and examining data to find hidden patterns.

Once data scientists have done the analyses, they will need to present a clear story to the key stakeholders and when the results get accepted, they will need to make sure that the work is automated so that the insights can be delivered to the business stakeholders on a daily, monthly or yearly basis. 

The data scientist needs to be aware of distributed computing, as he will need to gain access to the data that has been processed by the data engineering team, but he or she'll also need to be able to report to the business stakeholders: a focus on storytelling and visualization is essential. 

Finally,

Both are different in terms of task and perspective.

But Both are also connected with each other. A data scientist cannot do their job without a data engineer. Data engineers enable data scientists to do their jobs more effectively! And thus the entire technical process works on.

Reference:  https://blog.insightdatascience.com