Data Analysis & Data Science- Basic Difference
Data analysis uses available information to reveal possible data. Data analysis involves answering questions designed to make better business decisions. Data analysis focuses on specific areas with specific goals. On the other hand, data science focuses on discovering new questions that you may not have seen the answer needed to advance new ones. Unlike data analysis which involves theoretical testing, data science tries to build connections and shape questions to answer them in the future. If data science is the home of all methods and tools, data analysis is a small room in that house. Data analysis is much clearer and more focused than data science.
Data analysis focuses mainly on viewing historical data in context, while data science focuses mainly on machine learning and predictable comparisons. Data science combines many disciplines that include algorithm development, data interpretation, and predictable modeling to solve complex business problems. On the other hand, data analysis involves a few different branches of mathematics and comprehensive analysis.
Skills Required:
Data Science:
Medium Mathematical Literacy and excellent problem-solving skills as well
● Dexterity in Excel and SQL database to cut and sell data.
● Feel free to work with BI tools like Power BI to report
● Knowledge of mathematical tools such as Python, R , or SAS
To become a data analyst, one does not need to come from an engineering background but to have strong mathematical skills, data, modeling, and forecasting analysis comes as an added benefit.
Data Analyst:
Mathematics, Advanced Statistics, Predicted Modeling, Machine Learning, Planning and-
● Expertise in using big data tools like Hadoop and Spark
● Specialist in SQL and NoSQL databases like Cassandra and MongoDB
● Hear about data viewing tools like QlikView, D3.js, and Tableau.
● Dexterity in programming languages such as Python, R, and Scala.
Responsibilities:
Data scientist:
● Processing, cleaning, and verifying data integrity.
● Performing Data Test Analysis in large data sets.
● Performing data mining by creating ETL pipelines.
● Perform statistical analysis using ML algorithms such as Backbone, KNN, Random Forest, Decision Trees, etc.
● Coding automation code and building smart ML libraries.
● Getting business information using ML tools and algorithms.
● Identifying new trends in business prediction data.
Data Analyst:
● Collecting and translating data.
● Identify appropriate patterns in the database.
● Performing data queries using SQL.
● To experiment with various analytical tools such as forecasting statistics, descriptive statistics, and diagnostic statistics.
● Using data recognition tools such as Tableau, IBM Cognos Analytics, etc., to present the extracted information.
Job Opportunities:
In general, Data scientists are very sophisticated and need mathematical concepts, unlike data analysts who take a mathematical and analytical approach. From a job perspective, the Data Analyst role is above the entry-level level. Clients with a strong mathematical and editorial background can carry Data Analyst functions to companies.
Usually, when hiring Data Analysts, employers select people with 2-5 years of industry experience. In contrast, Data Scientists are experienced professionals with more than a decade of experience.
Both Data Science and Data Analytics pay very well when it comes to income. The average salary of a Data Scientist in India is between Rs. 8,13,500 – 9,00,000, and that Data Analyst is Rs. 4,24,400 – 5,04,000. And the best part about choosing to build a career in Data Science or Data Analytics is that their work trajectory is good, ever-increasing.