What is the difference between A Data Analyst and A Data Scientist?

 

A data analyst or data scientist’s roles and responsibilities varies depending on the industry and location and also according to the environment. A data analyst’s responsibilities may involve figuring out how or why anything happened— for instance, why sales dropped—or how to create creative dashboards that support KPIs. Data scientists, on the other hand, are more concerned with predicting like what is going to happen, or using data modelling techniques and big data frameworks such as Spark.

 

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It is very helpful to read your job descriptions carefully so you could have a better understanding of the company’s expectations. In most of the cases, job postings for data scientists also involve the responsibilities of a data analyst and vice versa. To get a better idea of the differences between data analysts and data scientists, here are some of the common job responsibilities of data analysts and data scientists.

 

Key Roles of Data Analysts:

·       Data query using SQL,

·       Data analysis and forecasting the data using Excel,

·       Create dashboards using business intelligence software, and

·       Perform various types of analytics including descriptive, diagnostic, predictive or prescriptive analytics.

 

 

Key Roles of Data Scientists:

·       A data scientist involves spending up to 60% of their time scrubbing data.

·       Data mining using APIs or building ETL pipelines,

·       Data cleaning using programming languages like Python or R language,

·       Statistical analysis using machine learning algorithms like natural language processing, logistic regression, KNN, Random Forest or gradient boosting,

·       Creating programming and automation techniques, like libraries, which simplify day-to-day processes which uses tools like Tensorflow to develop and train machine learning models, and

·       Develops big data infrastructures using Hadoop and Spark and tools such as Pig and Hive.

 

 

Each role analyses data and also gains actionable insights to make business decisions. Data analysts use SQL, business intelligence software and SAS, a statistical software, while data scientists use Python, JAVA and machine learning to make sense of the data.

 

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