How Are Data Analysts Different From Data Scientists?
With the increase in data generated every day, data science professionals are highly sought-after job roles in every industry.
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With the increase in data generated every day, data science professionals are highly sought-after job roles in every industry. The two most common and popular job roles are data scientists and data analysts. But how do they differ?
While Both of their responsibilities may vary depending on the industry they are in, the Data Analyst will be concerned with what and why something happened, such as a sudden decrease in sales. In contrast, data scientists will be concerned with what will happen in the future using data modeling. Typically, a data analyst analyzes the existing data, whereas, in the case of a Data scientist, he finds a new way of collecting and analyzing data that the Analyst can use.
• An Analyst will be working on answering questions specified about their business, whereas a Data scientist works on new methods for asking questions and answering them.
• Even though each role focuses on analyzing the data, they can also be differentiated by their tools. Data analysts will be using relational database software, Statistical software, and Business intelligence. Data scientists will use Python, Java, Machine Learning, and Artificial Intelligence to analyze the collected data.
To get a clear understanding of Data Analyst Vs. Data scientist, here are some job responsibilities,
DATA ANALYST :
• SQL-based data querying.
• Using Excel for data analysis and forecasting.
• Using business intelligence (BI) software to create dashboards.
• Using several analytics techniques, such as descriptive, diagnostic, predictive, or prescriptive analytics.
DATA SCIENTIST :
• Up to 60% of a data scientist's effort may be spent cleaning data.
• Building ETL pipelines or leveraging APIs for data mining.
• Using programming languages for data cleansing (e.g., Python or R).
• Employing machine learning methods for statistical analysis, such as KNN, Random Forest, Logistic Regression, Natural Language Processing (NLP), and gradient boosting.
• Using data science tools like Tensorflow to design and train machine learning models, creating programming and automation strategies, such as libraries, that streamline daily operations.
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