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Data Science VS Big Data

  • Amruta Bhaskar
  • Sep 18, 2020
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Big data approach cannot be easily achieved using traditional data analysis methods. Instead, unstructured data requires specialized data modelling techniques, tools, and systems to extract insights and information as needed by organizations. Data science is a scientific approach that applies mathematical and statistical ideas and computer tools for processing big data. Data science is a specialized field that combines multiple areas such as statistics, mathematics, intelligent data capture techniques, data cleansing, mining and programming to prepare and align big data for intelligent analysis to extract insights and information.

Currently, all of us are witnessing an unprecedented growth of information generated worldwide and on the internet to result in the concept of big data. Data science is quite a challenging area due to the complexities involved in combining and applying different methods, algorithms, and complex programming techniques to perform intelligent analysis in large volumes of data. Hence, the field of data science has evolved from big data, or big data and data science are inseparable.

This concept refers to the large collection of heterogeneous data from different sources and is not usually available in standard database formats we are usually aware of. Big data encompasses all types of data namely structured, semi-structured and unstructured information which can be easily found on the internet.

Big data includes:

  • Unstructured data – social networks, emails, blogs, tweets, digital images, digital audio/video feeds, online data sources, mobile data, sensor data, web pages, and so on.
  • Semi-structured – XML files, system log files, text files, etc.
  • Structured data – RDBMS (databases), OLTP, transaction data, and other structured data formats.

Therefore, all data and information irrespective of its type or format can be understood as big data. Big data processing usually begins with aggregating data from multiple sources.

To better understand the differences between these courses, one should try to look at some of the key dimensions such as the kind of tools and technologies that can be learnt and the extent of big data concepts that will be covered in each of them. Building a comprehensive working knowledge and expertise around various analytical and database tools is a key step to excel in Big Data and Data Science fields.

The Data Science course is entirely taught in R software which is an open-source statistical programming language and one of the essential tools that are a part of any Data Scientist’s Tool Kit. Due to its extensive package repository around statistical and analytics applications, R is tremendously growing in popularity around the world and many firms are on the lookout for R programmers.

Statistics and advanced analytics techniques knowledge are crucial for implementing successful data analytics projects. The Data Science course comprehensively covers these topics with applications of R programming. Typically, an analytics project consists of various phases such as manipulation, preparation, exploration, and visualization on different kinds of business data. Along with training modules on these phases, predictive analytics techniques like regression models, clustering and decision trees are covered using real-time case studies. Additional training modules around time series techniques and text analytics are also covered which helps in processing specific kinds of data such as text and social media content.

In the Big Data course, the emphasis will be more on handling and analysing huge volumes of data to generate insights through summarization and visualization techniques. Instead of advanced analytics techniques, this course puts more emphasis on BI aspects such as exploratory analysis, building dashboards and visualizations. Since Big Data technologies like Hadoop is a complex system compared to traditional SQL based systems, most of the learning modules will focus on data handling and processing using various components of Hadoop ecosystem such as MapReduce programming using Java, querying using HiveQL or scripting using Pig.

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