Difference between Data Scientist and Data Analyst

Data is everywhere, in the digital universe it will be expanding to 180 zetta-bytes in 2025. It has only recently established to be analyzed to tease out insights that can help organizations increase their business. That’s why more organizations are seeking professionals who can make sense and work on all the data.

Data Science isn’t a rocket science. It is easy enough to become a data scientist. Once after getting to know deeper about data analysis, it is just a matter of practicing newly-found skills well enough to become proficient.

Who is a Data Scientist?

The Data Scientist is considered to be the Rock Stars of the IT. Those are the ones who understand the data from a business perspective. They establish a compact foundation of computer applications, modeling, statistics and math. They always have great communication skills along with brilliance in business.  They have the great problem-solving skills and choose the right problems, which add the value to the organization.

Based on the skill set and work they do, Data Scientists can be categorized into 4 types.

• Data Researcher
• Data Developers
• Data Creative
• Data Business people

Who is a Data Analyst?

Data Analysts also plays a major role in Big Data and Data Science. They perform a variety of tasks related to collecting, classifying data and obtaining analytical information out of them.  They use the database of the organizations.

Based on the skill set and work they do, Data Analyst can also be categorized into 4 different types

• Data Architects
• Database Administrators
• Analytics Engineer
• Operations

Qualification required for Data Analyst and Data Scientist

Data Analysts

• Hands-on experience with data warehousing and business intelligence concepts.
• In-depth exposure to SQL and analytics.
• Strong knowledge of Hadoop based analytics.
• Data Storing and retrieving skills and tools.
• Expertise in tools and components of data architecture.
• Proficiency in decision making
• Familiar with various ETL tools – for transforming  different sources of data into analytics data stores

Data Scientists

• Knowledge of database like MySQL.
• Knowledge of Java, Python MapReduce, R Programming.
• Knowledge of various analytical functions.
• Expertise in Mathematics, Statistics, correlation, data mining, and predictive analysis.
• Knowledge of Machine Learning, Clustering

Roles and Responsibilities of Data Analyst and Data Scientist

Data Analyst Responsibilities

• Writes convention SQL queries to complex business problems.
• Analyzing and mining business data to identify correlations and discover patterns from various data points.
• Identify any data quality issues and partialities in data acquisition.
• Implements new metrics for business.
• Map and trace the data from system to system for solving a given business problem.
• Design and create data reports using various reporting tools that help business executive to take better decisions.

 Data Scientist Responsibilities

• Taking better decisions by finding new features or products
• Data Cleansing and Processing
• Identify new business proposals.
• Develop innovative analytical methods and machine learning models.
• Correlate disparate datasets.
• Conduct causality experiments by applying A/B experiments or epidemiological approach to identify the root issues of an observed result.
• Data Storytellingand Visualization.

The average salary for a Data Analyst is around $62,000 whereas for Data Scientist it would be $117,000. However, despite considering many differences between the job titles, one cannot be successful without the other. There has never been a better time to learn and step into this Data-driven era. You can get started now with the specially curated Big Data and Data Science course by Digital Nest.