Close look at Data Scientist vs Data Engineer

Data science is now one of the most influential topics all around. Companies and enterprises are focusing a lot on gathering data science talent further creating more viable roles in the data science industry. It has also been stated that data science and data scientist are the two most popular career tracks as of now.

Since the advent of big data industry, the roles were very blurred since the main objective was to get the insights. But due to a recent change in perspectives, a lot has been written about the difference between the different data science roles, and more specifically about the difference between data scientists and data engineers.

The role of the data scientist and that of a data engineer will now be discussed thoroughly with intricacy.

Work and Responsibilities

Data engineer’s responsibilities

A data engineer is he/she who indulges in the art of construction, development, and maintaining the architecture of databases and large-scale processing systems. They also have to deal with working along with all sorts of raw data which contain all sorts of errors. These data contains codes that are system-specific, and unformatted. It is up to the data engineer to implement ways to improve data reliability, efficiency, and quality. The data engineer must improvise and be aware of the opportunities in order to fetch data which gets procured constantly. This information will, in turn, be processed as data for the scientists to work on. They are also responsible for taking care of the architecture that supports the scientists. So that the data set is possible to be mined, modeled and used for other production purposes.

To summarize, they work on

Data Analysis
Statistics Machine Learning

Data Mining
Statistical Modelling
Research
Algorithm
Programming
R & D
Maintaining The Architecture

Role of A Data Scientist

The processed and filtered data are handed to them which are then fed to various analytics programs and machine learning with statistical methods to generate data which will soon be used in predictive analysis and other fields. The method of building a model might include thorough scrutiny of large volumes of data from internal and external sources. Then they might further explore for more cryptic patterns to procure proper insights.

The analysis is then submitted to the stakeholders where they present a model which will provide them with steady insights on a daily, monthly or yearly basis.

Visual representation plays a vital role because they will need to report to the respective stake holders. They must also have the flexibility to compute the processed data produced by the engineers.

So, Data Scientists works on

Data Warehousing
ETL
Databases
Business Intelligence
Procuring Insights

Tools Required By A Data Engineer

The tools and skills that are utilized by data engineers depend on which end they are working on. If he is building APIs for data consumption, integrating datasets from external sources and analyzing how the data is used to nurture business growth – then knowing a language like Python is enough. Python is a robust language and can talk to any data store like NoSQL or RDBMS. Data engineers might have to use big data technologies like Hadoop and Spark to suggest improvements based on how data is consumed.

Important Tools

Hadoop and related tools like Pig, Hive, HBase, etc.
Spark
NoSQL databases like MongoDB and Cassandra
Pentaho
JavaScript

Tools Required By A Data Scientist

Languages such as SPSS, R, Python, SAS, Stata, and Julia are being extensively used by the data scientists to create models.

Python and R might be the most important tool of all since one often resorts to packages such as ggplot2 to make amazing data visualizations in R or the Python data manipulation library Pandas.

Important Tools