Data Scientist vs Data Analyst vs Data Engineer – What are the difference?

Data Scientist vs Data Analyst vs Data Engineer – What are the difference?

These are the terms most people often get confused with. Who are Data Scientists, Data Analysts and Data Engineers? Are they analogous to each other? What do they do? What are the roles of each individual? Which technologies do they use? What skills are required to become a data scientist, data analyst, and data engineers?

Data Analyst

As the title suggests, data analysts are the professionals who analyze data. Data analyst takes the raw data, cleans and organizes them, analyzes them, produces a valuable result and delivers it to the company which helps the company to make better decisions. Data analysts go by various other names depending on the industry like business analyst, database analyst and business intelligence analyst. Data analysts are responsible for cleaning and organizing data, performing analysis on it, creating visualization and presenting the result to internal team and business clients of the company. Data Analyst helps the company to make better business decisions in the future.

A data analyst needs to have an understanding of the skills like data visualization, statistics, data mugging, data analysis and should be familiar with the tools like  Microsoft Excel, SPSS, SPSS Modeler, SAS, SAS Miner, SQL, Microsoft Access, Tableau, SSAS.

Data Scientist

Data scientists are the specialists who are expert in statistics, mathematics, programming and building machine learning algorithms to make predictions and answer key business questions. They are an advanced version of a data analyst. A data scientist still needs to be able to clean, analyze, and visualize data but they will have more depth and expertise in these skills, and will also be able to train and optimize machine learning models. Data scientists are responsible for evaluating statistical models, building better predictive algorithms using machine learning, testing and continuously improving the accuracy of machine learning models, building data visualizations to summarize the conclusion of an advanced analysis.

A data scientist needs to have an understanding of the skills like machine learning, deep learning, neural network, statistics, predictive modeling, Hadoop, R, SAS, Python, Scala, Apache Spark and should be familiar with the tools like RStudio, Jupyter, Matlab.

Data Engineer

Data engineers are the designers, builders, and managers of the “big data” infrastructure. In simple words, data engineers clean, prepare and optimize data for consumption, so that once the data becomes useful, data scientists can perform a variety of analysis and visualization techniques to produce meaningful results. They also make sure the system is working smoothly. Data engineers work closely with data scientists.

A data engineer needs to have an understanding of the skills like database systems, SQL, NoSQL, Hive, Data APIs, Data modeling, data warehousing solutions, ETL tools and should be familiar with the tools like MongoDB, Cassandra, DashDB, R, Java, Python, SPSS.

What is Business Analytics?

What is Business Analytics?

Business Analytics (BA)


Before answering this question, think what you do before you make an important decision. You do some study, research, make notes, look at statistics and charts, compare things, and after all the calculations, you finally get some result that may help you to take a right decision. Like this, business organizations also look at their data, analyses them using various tools, techniques, and methodologies to produce a valuable result that would help the organization to take a right decision, predict things and grow.


So, Business Analytics (BA) is the combination of skills, technologies, applications, and processes used by business organizations to gain insight into their business based on data and statistics to drive business planning. Business Analytics is not only used by companies to make decisions and predict things but also to optimize and automate business processes and also to evaluate an entire company.

Business Analytics is used even more in such companies where it contains lots of data, for example, e-commerce, banks, etc. Such data-driven companies treat their data as a corporate asset and use it for competitive advantage. Business analytics is a vital part of any business.

Business analytics can be broken down into two parts – Business Intelligence and Statistical Analysis.


Business Intelligence involves examining past data to get an insight of how a business department, team, individual, product, project or the company itself, performed over a particular time.

Statistical Analysis involves predictive analysis by applying statistical algorithms to past data to make a prediction about future performance of a product, service or company itself.