Data Analyst Vs Data Scientist - What's The Difference?Data Analyst Vs Data Scientist - What's The Difference?

Data Scientist:

The role of a data scientist is similar to that of a statistician, but unlike statistics, data science involves the use of advanced technologies such as machine-learning, predictive modeling, etc. to provide insights beyond statistical analysis. The demand for data science has grown significantly in recent years as companies look to garner critical insights from a huge volume of data related to the consumers or the markets. Since a majority of the tasks pertaining to this profile, which were carried out by statisticians are now performed by data science professionals, finding a person with the right skills and experience is difficult and has led to a demand-supply mismatch, and was, in fact, cited as one of the top 50 jobs in America.

The basic responsibilities of a data scientist include data accumulation and analysis using a different type of analytics to detect patterns, trends, and relationships in data sets. Companies usually have a team of data scientists who mine real-time data for extracting information that can be used to predict customer behavior and classify opportunities and hedge business risks accordingly. These professionals also hold the responsibility to develop statistical learning models for data analysis and ability to create complex predictive models using statistical tools.

Key skills and educational qualifications required to be a Data Scientist:

  • In-depth knowledge of Python coding. It is the most common language other than Perl, Ruby, etc.
  • Sound knowledge of SAS/R
  • Sound skill in SQL database coding
  • Data Scientist should have a good understanding of various analytical functions. For example, rank, median, etc.
  • In-depth knowledge of Machine-learning requires
  • A data scientist should be familiar with Hive, mahout, Bayesian networks, etc. In data science, knowledge of My SQL is just like an added advantage
  • The educational qualifications required for data scientists include a bachelor's degree in statistics, data science, computer science, or mathematics


Data Analyst:

Most people think that data science and data analytics are similar, but there is a slight variation in both the profiles. A data analyst collects data on sales, inventories markets, etc. and uses various statistical methods to make better business decisions in order to optimize costs and boost profitability. The difference being an analyst deals with raw data and does not use complex tools used by the data scientist such as data mining.

Skills needed to become Data Analyst: The following are the required data analyst skills-

  • Sound knowledge of R programming and Python
  • Communication and data visualization skills
  • In-depth knowledge of PIG, HIVE
  • Mathematics and Statistical skills
  • Communication Skills: Data analysts are often called to present their findings, or translate the data into an understandable document. You will need to write and speak clearly, easily communicating complex ideas.
  • Critical Thinking: Data analysts must look at the numbers, trends, and data and come to new conclusions based on the findings
  • Attention to Detail: Data is precise. Data analysts have to make sure they closely scrutinize data to come to correct conclusions
  • Math Skills: Data analysts need math skills to estimate numerical data

So to conclude, a data scientist is the one who can forecast future using the given data and with the help of developing new models, whereas a data analyst curates meaningful insights from the data. Hence it can be said that an analyst addresses business problems, but it is the data scientist who addresses the problems that create the most value once they are solved.

by Sandy Dsouza
References and Bibliography
This  article focuses on evaluating the differences between data analyst and a data scientist.

Sandy Dsouza is a freelance author and blogger and is significant contributions to BSR: Resume Examples.

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