R Programming for Statistics

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The Department of Statistics and Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus – The Hub of Analytics, successfully conducted a five-day academic and skill development program titled “R Programming for Statistics” from 14th to 16th January 2026 for MSc Data Science students and 19th to 20th January 2026 for BSc Economics and Statistics students, between 9:00 AM and 4:00 PM at Room No. 1107, Central Block. The program was designed to strengthen students’ understanding of statistical computing using the R programming environment and to enhance their ability to apply theoretical concepts to real-world data analysis tasks. The focus of the event was on fostering industry-relevant technical skills and promoting a technology-driven learning approach in the field of statistics and data science.

The sessions were facilitated by Ms. Nisha Rao, CAPS Trainer and Mentor, CHRIST (Deemed to be University), Pune Lavasa, who guided students through both conceptual and practical aspects of R programming. The training followed a blended teaching methodology that combined conceptual explanations, live demonstrations, and guided hands-on practice. During the first three days, MSc Data Science students were introduced to the R and RStudio environment, basic syntax, data structures, and data handling techniques, followed by sessions on descriptive statistics, hypothesis testing, regression analysis, and advanced data visualization using appropriate R packages. The final two days, dedicated to BSc Economics and Statistics students, focused on building strong foundational skills in data import, cleaning, summary statistics, and basic graphical representation, with simple datasets used to help students clearly understand statistical concepts through computational tools.

A total of 52 students from MSc Data Science (Semester 2) and BSc Economics and Statistics (Semester 2) actively participated in the program. The interactive nature of the sessions encouraged students to engage in problem-solving activities and dataset-based exercises, which helped them develop confidence in working with real-world data. By the end of the program, students demonstrated improved analytical thinking, enhanced technical proficiency in R programming, and a better understanding of how statistical methods are applied in academic research and industry-oriented analytical workflows.

The program contributed positively to strengthening the teaching and learning ecosystem of the department by integrating open-source, industry-relevant tools into academic practice. It also supported the institutional vision of promoting analytics-based education and fostering a culture of continuous skill development. Overall, the “R Programming for Statistics” program was a successful academic initiative that effectively bridged the gap between theoretical knowledge and practical application, preparing students for future academic, research, and professional challenges in data-driven domains.


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