Data Analyst vs Data Engineer vs Data Scientist

 

Do you feel like the companies want a Super-Human when you read their job description?

It is very important to know about the profile that you are applying for. The role names differ from company to company, for that reason, one should not fall for the role names but instead, insist on getting information about the responsibilities and the kind of project that the companies work on. 

Usually, while going through the job description, one can see that job profiles are not classified equally at every place. They tend to mix multiple profiles into one and one might not understand what actually the company requires. On occasions, I have also seen frontend technologies being mixed into Data Science profile, Big Data skillset being the required criteria for a Data Analyst.

In this post, I will try to cover in very short about what do these three profiles, namely, Data Science, Data Engineers, and Data Analysts have in common, the places or skillset they differ in, and what can be the right match if you have planned to enter this domain.

Data Analyst

The primary focus of a Data Analyst is to retrieve data and perform some kind of analysis. The analysis mainly refers to analyzing past trends in data and predicting future attributes that the data might possess based on their findings. They do not care much about feature extraction or modeling and work mainly with structured data. They are responsible for creating visualization and graphs in a more informative way and present it to the concerned audience. They are more or less similar to Business Analyst, it's just that Business Analyst has more information specific to the business domain where they work in.

Also known as - Business Analysts

Skills required -  Statistics, Domain Knowledge, Communication, Python Visualisation Libraries, SQL, Tableau, MSExcel

Data Engineer

The primary focus of a Data Engineer is to code, clean up data as per the requirements of a Data Scientist. They typically deal with a huge amount of data termed as Big Data which is either in semi-structured or unstructured form. They have knowledge related to how the data can be stored and retrieved efficiently in storage stacks which can help faster processing of data.

Also known as - Database Administrators or Data Architects

Skills required - Mathematics, Big Data, Hadoop, Spark, Hive, Pig, Python, NoSQL

Data Scientist

Coming to the sexiest job of the 21st century as quoted by Harvard Business Review, it is the hottest debate out in the market. Terming everything to a Data Scientist has created a major confusion in different roles. The task associated with a Data Scientist mainly comprises of EDA - Exploratory Data Analysis, feature extraction, finding the right Machine Learning algorithm to model the data and improve accuracy by testing the model followed by fine-tuning whenever needed. Usually, a Data Scientist does know about the job of a Data Analyst and he/she might use the visualizations to place their own findings and model performance but they might or might not know about the Big Data and task related to a Data Engineer.

Also known as - Data Managers or Statisticians 

Skills required - Mathematics, Statistics, Communication, Machine Learning, Python/R, SQL

To summarise this comparison, I would like to put the roles that one must ideally look for in a sequential manner. 

Data Analyst or Business Analyst → Data Engineer → Data Scientist or ML Engineer → AI Architect