Data Analysis using Spreadsheets

The three-day academic and skill development session on “Data Analysis using Spreadsheets” was successfully conducted from 14th to 16th January 2026 at CHRIST (Deemed to be University), Pune Lavasa Campus, as a collaborative initiative of the Department of Computer Science and the Department of Statistics and Data Science. The primary objective of this program was to enhance students’ practical understanding of data analysis techniques using spreadsheet tools and to familiarize them with industry-oriented approaches to managing, processing, and interpreting data in real-world contexts.

The session was inaugurated with an introductory address that emphasized the growing importance of data literacy across academic disciplines and professional domains. The resource person, Ms. Mansha Sawal (CAPS), highlighted how spreadsheets continue to remain a foundational analytical tool in industry for data organization, validation, preliminary analysis, and reporting. The opening segment also focused on the role of structured data handling in ensuring accuracy, consistency, and reliability in analytical outcomes.

On Day 1, the session focused on building a strong conceptual and practical foundation. Students were introduced to the spreadsheet environment, including interface navigation, worksheet management, and best practices for data entry and formatting. Emphasis was placed on understanding different types of data, such as numerical, textual, and categorical data, and the significance of maintaining clean and well-structured datasets. The participants were trained in the use of essential formulas and functions, including arithmetic operations, logical functions, and basic statistical tools. Through guided hands-on exercises, students learned how to apply functions such as SUM, AVERAGE, COUNT, and IF to analyze simple datasets and verify data consistency. The day concluded with practical tasks that required students to organize raw datasets into meaningful and analyzable formats.

On Day 2, the session progressed into more advanced analytical techniques related to data cleaning and exploratory data analysis (EDA). Students were introduced to methods for identifying and handling missing values, eliminating duplicate entries, and applying data filters and sorting mechanisms to refine datasets. The use of conditional formatting was demonstrated as a visual aid to highlight trends, outliers, and anomalies within large data tables. The resource person also trained participants in the application of lookup and reference functions, such as VLOOKUP and XLOOKUP, to integrate and compare data across multiple sheets. Special focus was given to the creation and interpretation of pivot tables, enabling students to summarize large datasets and extract meaningful insights related to frequency, distribution, and basic statistical measures. The practical sessions were designed to simulate industry scenarios, where students worked with sample business and academic datasets to identify patterns, trends, and key performance indicators.

On Day 3, the emphasis shifted toward data visualization, reporting, and presentation of analytical findings. Students were trained to design professional charts and visual representations, including bar charts, line graphs, pie charts, and comparative plots, to communicate insights effectively to different audiences. The session highlighted the importance of selecting appropriate visualization techniques based on the nature of the data and the purpose of analysis. Participants were also introduced to basic dashboard creation techniques, where multiple visual elements were combined to provide a consolidated view of key metrics and trends. The final segment of the session involved a structured mini-project in which students prepared a comprehensive data analysis report using the techniques learned over the three days. This activity encouraged them to apply data cleaning, analysis, and visualization skills in an integrated manner, closely resembling real-world reporting and decision-support tasks.

Overall, the program successfully achieved its academic and professional development objectives by strengthening students’ analytical thinking, improving their proficiency in spreadsheet-based data analysis, and enhancing their ability to interpret and communicate data-driven insights. The session fostered an industry-oriented learning environment and provided valuable exposure to practical tools and methodologies that are essential for academic research, internships, and future professional roles in the fields of data science, analytics, and information technology.