Minor in Data Visualization and Analytics | Data Analytics

Data drives applications across all domain and places itself as key strategic asset for any organization. Developing and extracting meaningful information from them is more important than ever before. Visual analytics provides a much better understanding data and use them more effectively. Charts, graphs, and maps are now seen as more impactful ways to communicate complex data and complement analytical interpretations on them.

This course creates an opportunity for learners interested in data visualization and visual analytics to select the best visualization and develop interpretion for the given data set. It enables you to master most plot types like histograms, scatter plots, line plots and bar plots to more complex visualization. The course gives you an opportunity to understand theoretical and mathematical foundations of data analysis and allows you learn about best practices of data visualization, and also avoiding common pitfalls. The course gives equal importance to hands-on practical exercises by visually exploring real world datasets from domain of medicine, scientific experiments, finance and other topics of computational sciences. This course is interdisciplinary in nature and can be taken by wide range learners interested in data analysis and visualization.

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Key Information

  • Duration
    3 Years
  • Programme Code
    ST3134
  • Course Type
    Minor Degree
  • Mode of study
    Full time
  • Campus
    Vidyavihar - Mumbai
  • Institute

Objectives

  • Explore and understand need of data visualization, analysis and analytics of data.
  • Prepare student to perform Analysis and visualization of data.
  • Explore Big data features and handling big data.
  • Learn Machine learning techniques and algorithm to generate model.

Learning Outcomes

At the end of successful completion of the Minor in “Data Visualization and Analytics” the student will be able to

  • CO1.Perform Coding in R Programming.
  • CO2.Gain knowledge of Data Analysis and Visualization techniques.
  • CO3.Insights of features of Big data and handling of big data.
  • CO4.Apply machine learning algorithm on the data set.
  • CO5.Work on Case Study and Project.
Assessment Method

Class participation, Presentation, Practical, Projects, Viva/ test, End Semester Exam

List of Courses
(total credit of 20, 4 credit for each course)
  • Learning R Programming
  • Data Analysis and Visualization
  • Big Data Analytics
  • Machine Learning
  • Case Study and Project