About This Course
Research is a continuous process which leads to new ideas, improvements and development. An efficient researcher needs to be informed about the different methods and tools of research. One such aspect is quantitative analysis of data collected. In dribs and drabs, acceptability of research across disciplines has become conditional on its backing by empirical studies so as to make a strong case for proposed arguments. SPSS (Statistical Package for the Social Sciences) is a software program used by researchers in various disciplines for quantitative analysis of complex data. SPSS is one of the most popular software used by researchers widely as it has enabled academicians and researchers to obtain valuable information from the data. Basic knowledge of SPSS is vital for all users before they proceed to surveys and data analyses. Using this as a reference point, the current course is an endeavor to bridge the gap between humanities and science by complementing ‘thick data’ with ‘big data’. In other words, the course emphasizes on the use and application of quantitative and computational methods to make better sense of the information at hand. The current course has been designed using the insights from the emergent disciplines of Digital Humanities and Data Science and Public Policy. The objective is to integrate the techniques of Data Science, which are majorly focused on the interpretability of information through Machine Learning models, with a human centred approach. The course, therefore, will use primary data sets from the fields of social sciences and further it through computationally innovative methods and technologies. With this framework, the course will be useful for improving quantitative skills of teaching faculty and research scholars in both sciences and social sciences. It will also equip the participants across disciplines to work in a new environment by providing them with knowledge about the tools, methods, and theoretical issues central to the field of Digital Humanities. Participants of this faculty development program will learn about the different techniques of data collection and cleansing, data management and visualization, and will also receive training needed to apply these tools to humanistic questions.
- 1. Data Collection and Cleansing
- 2. Data Managing and Visualization
- 3. Estimation procedures
- 4. Applied Regression Analysis
- 5. Time Series Analysis
- 6. Applied Machine Learning
- 7. Introduction to Artificial Intelligence and Deep Learning
Upon the successful completion of the programme, participants will be awarded blockchain based graded certificates. These certificates will be available for download within 15 working days of the completion of the programme.