Difference Between Data Analyst, Data Engineer, and Data Scientist

At the beginning of my carrier, I was really confused among these three terms “Data Scientist” “Data Engineer” and “Data Analyst”.Since all three terms come under the filed of “Data analytics” but all have different work rolls. So in this blog, we are going deeply understand the basic difference so that you can choose your carrier wisely.

1. Definition

Data Analyst

  • Data analyst usually looks at data from a single source.
  • Gathers and represent data in an understandable form.
  • Responsible for cleaning, organizing, and translating raw data.
  • Data visualization is a part of their professional day to day routine.

Data Engineer

  • Works as an intermediate between a data analyst and data scientist.
  • Optimize the system for data scientists and analysts.
  • Ensure the quality of data so that anyone can work over it.
  • Built the infrastructure and the framework necessary for data generation.

Data Scientist

  • Handel the structured and unstructured data.
  • Predict the data using machine learning models.
  • Usually collects data from multiple disconnected and sources.
  • Resolves business solving problems.

2. Job Rolls

Data Analyst

  • Collect and interpret data from the source.
  • Analyzing the data using the visualization technique.
  • Maintain database and data system.
  • Create dashboards, visualization, and graphs.

Data Engineers

  • Implement the way to improve the reliability, efficiency, and quality of the data.
  • Develop, construct, test, and maintain the architecture of the data.
  • Built the data pipeline.
  • Creating and integrating APIs.

Data Scientist

  • Select Features to built and optimizing classifiers by using machine learning models.
  • Performing data mining and data analysis by using the latest techniques.
  • Develop data algorithm and data model.
  • Performing predictive analysis using predictive modeling.

3. Skills Require

Data Analyst

  • Basic programming Knowledge Like R, Python, SAS, etc
  • Database Knowledge (SQL).
  • Data Visualization Tool like Tableau, PowerBI , excel etc.

Data Engineer

  • Experience in Hadoop, MapReduce, Pig, Hive Programming, Data streaming.
  • Database system, knowledge of SQL, NoSQL are important.

Data Scientist

  • Requires strong knowledge of the domain.
  • Strong Visualization skills like Tableau, Excel, and PowerBI.
  • Advance Programming skills like Python, R, SAS.
  • Strong statistics and other mathematics skills.
  • Knowledge of Big data, Hadoop, Machine learning, deep learning.

This is a very basic difference between the three data-related field. I hope You will be clear about them now.

Data science | Machine learning | Deep learning | Artificial intelligence. LinkedIn : https://www.linkedin.com/in/ritikasingh17

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