The Integration of Data Analytics and Data Science


Data analytics processes the raw data and analyzes it to make certain decisions and to develop insights from the data. All of the steps of data analytics are automated and algorithms are built to model the learning of machines. 

Data analytics is used by almost all companies today to reveal the metrics and trends about the market and business processes. These trends are used to optimize business and marketing processes which increase  efficiency and company profits. 

People always get confused when attempting to understand the differences between data analytics and data science. Both of these terms are interrelated. Keep reading to understand the key differences and the similarities between data analytics and data science and the challenges faced by the data analyst.

Challenges faced by a data analyst

The challenges faced by a data analyst lie in day to day routines and listed below:

  • Find out the quality of the data and solve issues related to data partialities and acquisition. 
  • Perform statistical analysis
  • Perform deep data analytics to understand the hidden metrics of different parts and departments of the business
  • Discover patterns and identify correlations within the business data and analyze the various data points
  • Gather new data in collaboration with the engineering team
  • Perform SQL queries to find solutions to complex business problems.
  • Trace the informative insights using different models to improve the accuracy of the prediction.
  • Perform effective data visualization using reports, charts, and other representations to make helpful decisions.

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Comparison between Data Science and Data Analytics

The aim of the data analyst and the data scientist is the same, but the process used to achieve these goals is slightly different. Following are the key comparisons between data scientists and data analysts.

  • The data scientist has to formulate the business questions on his own in favor of the company and start solving them, while the business team provides the data analyst with the questions and the data analyst has to find answers to them.
  • Both data scientists and data analysts have to write SQL queries, collaborate with the engineering team, pre-process the data, improve its quality, and derive meaningful insights from it. However, a data analyst is more focused on the use of models, fire queries, and handles the database using business tools to find solutions to the problems, whereas data analysts are focused on building machine learning models.
  • Data scientists focus on preparing scenarios and business roadmaps whereas data analysts focus on data visualization and representation
  • Data analysts must have in-depth knowledge about business tools and SQL queries whereas data scientists must have in-depth knowledge about Hadoop.
  • Data scientists have a clear understanding of median function, and other analytical functions and data analysts have a clear understanding of storage and strong retrieval skills.
  • Data analysts must have good analytical skills, whereas data scientists must have good decision making skills and be knowledgeable of basic business operations.
  • Data scientists focus on machine learning models, while data analysts focus on data preparation techniques.

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