What is the difference between a data analyst and a data scientist?
Data Analyst and Data Scientist jobs are some of the most sought-after jobs in the IT industry today. They are mentioned interchangeably in discussions, and because of this, people frequently confuse a few features of one with the other. Data scientists and data analysts deal with data, but each position requires a unique set of skills and tools. Both are required to develop queries, collaborate with technical teams to acquire the appropriate, and extract information from data. Let us explain the difference between data analysts and data scientists:
Difference between data analysts and data scientists:
Who are they?
Data scientists
A data scientist is a specialist who analyses and interprets massive volumes of data. The function of a data scientist is an extension of numerous conventional technical occupations, such as mathematician, scientist, statistician, and computer expert.
Data analysts
A data Analyst combines theory and practice to find and present data-driven insights that enable managers, stakeholders, and other leaders to make more informed choices in a company.
One of the biggest differences between data analysts and data scientists is that a data scientist knows computer programming, statistics, and mathematics whereas a data analyst generally collects data in order to discover trends that will assist company executives in making strategic choices.
Also, read Data Science Course Eligibility
Technical skills
Data scientists
- Python, R, JAVA, Scala, SQL, Matlab, Pig
- Economics
- Big Data/Hadoop
- Machine Learning
- Data Mining
- Data Warehousing
- Math, Statistics, Computer Science
- Tableau and Data Visualization/Storytelling
Data analysts
- SQL
- Business Intelligence
- SAS
- Advanced Excel skills
- Data Mining
- Data Warehousing
- Math, Statistics
- Tableau and Data Visualization
Responsibilities
Data scientists
- Machine learning techniques are used in statistical analysis.
- Creating Hadoop and Spark-based big data infrastructures.
- Developing programming and automation approaches, such as libraries, to streamline day-to-day operations.
- Programming languages are used to clean data.
- Data mining
Data analysts
- Descriptive, diagnostic, predictive, and prescriptive analytics are examples of analytics.
- Forecasting and data analysis
- Querying data via SQL.
The difference between data analysts and data scientists is that a data scientist has strong data visualization and the ability to transform data into business insights whereas data analyst positions do not necessitate specialists transforming data and analysis into a business scenario and plan.
Compensation
Data scientists
A Data Scientist with less than one year of experience may expect to earn an average total pay of ₹524,861. A Data Scientist in their early career with 1-4 years of experience makes an average total salary of ₹786,822. ( according to www.payscale.com )
Data analysts
An entry-level Data Analyst with much less than one year of experience may expect to earn a total salary of ₹341,757 on average. A Data Analyst in their early career with 1-4 years of experience makes an average total salary of ₹425,244. ( according to www.payscale.com)
There isn’t much of a difference between data analysts and data scientists. Data cleaning and analyzing are some common factors between a data analyst and a data scientist. Whether you want to be a data scientist or a data analyst, you only need to evaluate your talent level and educational requirements to know where to focus your attention. This is why, at Praxis, we put an emphasis on giving the students a comprehensive learning experience that ensures hands-on lab experience. Praxis is a business school that offers 9-month Post Graduate Programs in Data Science. The PGP in Data Science with ML and AI aims to offer students the help & expertise required for a smooth transfer into the field of Analytics and advancement into Data Scientist positions.
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