Data Science Vs Data Analysis Vs Big Data

Data Science Vs Data Analysis Vs Big Data

data science vs Big data Vs Data analytics

The amount of digital data that exists, that we create, is growing exponentially. According to estimates, in 2021 there will be 74 zettabytes of data generated. That is expected to double by 2024. In this data-driven world, terms like Data analytics, Big Data, and Data Science are very important these days. These three fields are generating enough interest that it is worth discussing.

What is Data Science?

Data science is a multidisciplinary approach that extracts information from data by combining:

Scientific methods

Mathematics and statistics


Advanced analytics

ML and AI

Deep learning

Data science will deal with everything from analyzing complex data, creating new algorithms and analysis tools to data processing and purification, and even creating powerful and useful visualizations.

What is Data Analysis?

Data analysis seeks to provide operational information on complex business situations. The main concern of a data analyst is to examine historical data from a modern perspective and then find new and challenging business scenarios. Subsequently, apply methodologies to find better solutions. Not only this, but a data analyst also predicts the next opportunities that the company can take advantage of.

What is Big Data?

Big data relates to large data sets, which are created from a variety of sources and with great speed (also known as speed). Any data set that has one of the attributes can be called Big Data. It is also about data with truthfulness and value.

Data Science application

  • Fraud and risk detection
  • Health care
  • Internet search
  • Targeted advertising
  • Website recommendations

Data Analysis application

  • Health care
  • Trip
  • Gaming
  • Energy management

Big Data application

  • Customer analysis
  • Compliance analysis
  • Fraud analysis
  • Operational analytics

Skills Needed to Become a Data Scientist

  • Statistical and analytical skills
  • Data mining activities
  • Machine learning
  • Deep Learning Principles
  • Deep knowledge of programming

Skills Needed to Become a Big Data Professional

  • Technologies like Hadoop, Spark, Hive, etc.
  • Work with unstructured data
  • General-purpose programming
  • SQL / Database encoding
  • Familiarity with MATLAB

Skills Needed to Become a Data Analyst

  • Data storage
  • Hadoop-based analytics
  • Adobe and Google Analytics
  • Programming skills
  • SQL / Database encoding

Ultimately, Big Data, Data Analytics, and Data Science help people and organisations tackle huge data sets and extract valuable insights from them. As the importance of data grows exponentially, it will become an essential component of the technology landscape. After all, Data is the future.

Content By : Brainware University


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