The roles of data analyst, data scientist and data engineer might look similar but they are not. Most of the people get confused in identifying the differences between them. In this blog we can discuss about these three roles and their differences in terms of their work.
These 3 roles are trending in the industries. Since data is everywhere, there is a need of people who is capable to work with the data. So, it is important to know the basic differences between these 3 roles.
DATA ANALYST- the reporter
Data analysts have been in the industries for a very long time and hence this field is more common among the three. So, what do these data analysts do? They work with the data to draw insights from them and provide data-driven solutions. They put forth their insights in the form of story telling and data visualizations. The analysts can work in business related industries and research related industries. As long as data is available to understand and learn from it , there is a need for data analysts.
Data analysts perform data cleaning, data conversion and data modelling. They use statistics and inferential statistics. In a nutshell, they use SQL and other scripting languages to extract and wrangle the data. They use their analytical skills to draw insights and present them in a meaningful way using data visualization.
Background of data analysts:
- A Bachelor or Masters degree in computer science, math or statistics
- It does not completely depend on education but depends on your ability to learn and adapt yourself to the work.
KEY COMPONENTS OF THIS ROLE
Languages and tools used :- R, python, HTML, Javascript, C/C++, SQL
- Business intelligence and reporting
- Statistical analysis
- A/B testing
- Data mining
- Data visualization
- Predictive modelling
- Maintaining Database systems
- Working with UI and spreadsheets
Data scientist- the scientist
Data scientist is the most ambiguous job among others. Data scientist is considers as a “Sexist job of 21st century”. Data scientist lies anywhere between the spectrum of data analyst and data engineer. It takes aspects from both the job with additional responsibility that is ‘Machine Learning’. Like data analysts, they extract , manipulate and draw insights from the data but they also work with machine learning models. These scientists are expected to have a deep understanding on machine learning to apply some advanced methods to get solutions from the data. The algorithms and models they build using the data must provide efficient solutions and predict the future events. Hence data scientist would be involved in building machine learning model from scratch, doing researches in machine learning and use deep learning. Compared to data analyst they work less with UI based systems. Instead, they work on the code to implement their models using data. They must have an advanced computer science knowledge to develop complex models. This might mean having knowledge of working with cloud computing and distributed systems.
BACKGROUND OF DATA SCIENTIST
Data scientists are expected to have a graduation in computer science and math.
KEY COMPONENTS OF THIS ROLE
Languages and tools used:- R, Python, SAS, SQL, MATLAB, Hive, Spark .
- Machine learning
- Statistical analysis
- A/B testing
- Data mining
- Data visualization
- Predictive modelling
- Strong computer science knowledge
- Ability to work with computer systems
- Programming languages and frameworks to build and integrate computationally heavy models into broader systems.
DATA ENGINEER- the Data builder
Data engineer is a person who prepares the data for analytical uses. It is the most defined role among the three. It is a special kind of software engineering. Data engineering focuses mainly on building and maintaining data on data systems. They set and maintain data warehouses, data pipelines and database systems from which the analysts and scientists extract the data. Data engineers work with both structured and unstructured data. Hence they require the knowledge of SQL and NoSQL. At smaller companies data analyst , data scientist or software engineer would be doing this data engineering work.
BACKGROUND OF DATA ENGINEER
They must have a background in software development. It can be academic or self-taught.
KEY COMPONENTS OF DATA ENGINEER:-
Language and tools used:- SQL, Hive, Pig, R, SAS, SPSS, MATLAB, JAVA, C/C++, Ruby
- Build and maintain ETL pipelines and data infrastructure.
- Cloud computing
- Bigdata and distributed computing framework.
- ML deployment and integration.
So, I hope you got a basic understanding about the job roles of data analysts , data scientists and data engineers . Remember, data is everywhere and hence these jobs are present in all kinds of industries. Make good use of the data , draw insights from them to make good decisions. Here is a picture that will provide you some more differences between these roles.
Happy learning and stay tuned for more blogs on data science.
My Little World 
Very informative! Thank you …
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Had a good understanding about the job roles
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Crisp and clear information
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very informative 👌!!
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