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  • Social Media Analytics

    By : Hana M, Social media analytics is the practice of using data science techniques to analyze social media data in order to gain insights and make informed decisions. Social media platforms generate...

  • Emphasis on Actionable Data

    By : Gokula Nandhini K, What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business processes...

  • Emphasis on Actionable Data

    By : Gokula Nandhini K, What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business...

  • Hyper automation

    By : Gokula Nandhini K, Another dominant trend in data science in 2023 is hyper-automation, which began in 2020. Brian Burke, Research Vice President of Gartner, has once said that hyper-automation...

  • Automation of Data Cleaning

    By : Gokula Nandhini K, For advanced analytics in 2023, having data is not sufficient. We already mentioned in the previous points how big data is of no use if it’s not clean enough for analytics...

  • Big Data

    By : Janani R, Big data refers to extremely large and complex data sets that cannot be easily managed or analysed using traditional data processing tools or methods. These data sets are typically so large...

  • Ethics Data Science

    By : Janani R, Ethics in data science refers to the moral principles and standards that guide the collection, analysis, and use of data. As data science becomes more widespread, there is growing concern...

  • Datafication

    By : Hana M, Datafication is the process of turning various aspects of the world into digital data, which can be analyzed and used to gain insights and make decisions. This can include data collected...

  • Machine Learning Frameworks

    By : Hana M, Machine learning frameworks are software tools that provide pre-built libraries and modules for developing, training, and deploying machine learning models. These frameworks...

  • Data Acquisition and Wrangling

    By : Hana M, Data acquisition and wrangling are the processes of obtaining and cleaning data, respectively. These two steps are critical in preparing data for analysis, as data that is incomplete, inconsistent...