5-Day online Faculty Development Programme on “Statistical analysis and Machine Learning using R Proramming”
A 5-Day online Faculty Development Programme (FDP) on “Statistical analysis and Machine Learning using R Proramming” was organized by the Quality Improvement cell (QIC) and Institute Research Committee (IRC) during 27-31 March 2023. The FDP received an overwhelming response with 34 participants from various Universities and Institutes such as BITS-Pilani, Gautam Buddha University, Aligarh Muslim University, Jaypee Institute of Information technology, Sharda University, NMIMS-Mumbai, Ramaiah University of Applied Sciences, Sikkim Manipal Institute of Technology and so on.
This FDP was intended to provide hands-on training in applying various statistical and machine learning techniques using R programming. A total of 10 teaching sessions were conducted during the FDP via ZOOM platform, out of which Sessions 1-9 were conducted by Dr. Kriti Priya Gupta, Professor - SCMS NOIDA and Session 10 was conducted by Dr. Sunita Dwivedi, Associate Professor - SCMS NOIDA. Each day of the FDP had 2 teaching sessions and 1 practice session wherein participants were given hands-on practice exercises.
Day 1 stated with Session 1 wherein the participants were introduced to the interface of R and the differences between R and R Studio. They were also introduced to the basic data types that are used in R. In Session 2, the concept of objects in R was discussed and the participants learnt how to create and manipulate various R objects.
Sessions 3 and 4 were conducted on Day 2, that focussed on performing descriptive statistical analysis using R programming. The participants learnt how to create frequency distributions and charts in R studio. They also learnt to import datasets in R Studio and to calculate statistical measures and perform normality testing and reliability testing on the imported data.
During the Session 5 on Day 3, the participants learnt to conduct various statistical tests (such as t-tests, ANOVA, correlation and chi-square test) in R Studio. Session 6 focussed on performing linear regression analysis using continuous as well as categorical independent variable.
Day 4 focussed on supervised machine learning techniques. During the Session 7, the participants learnt about regression-based techniques and in Session 8, they learnt about classification-based techniques such as Logistic Regression, Naïve Bayes algorithm and Decision trees. They also learnt about the “caret’ package in R that is used for implementing machine learning techniques.
On Day 5, Session 9 focussed on unsupervised machine learning techniques wherein participants learnt perform Principal Component Analysis and k-means Clustering using R. Session 10 focussed on performing Social Network Analysis (SNA) using R programming. The participants learnt the theoretical concepts of SNA as well as the implementation of SNA in R Studio.
During the FDP, the participants were involved in a continuous learning process through hands-on examples and practice exercises. The FDP received very good feedback from the participants. All the participants had a great learning experience during the FDP.
