Hands-on Data Analysis using Excel & Jamovi with Real-World Datasets and AI Tools 22nd Feb 2026 скачать в хорошем качестве

Hands-on Data Analysis using Excel & Jamovi with Real-World Datasets and AI Tools 22nd Feb 2026

Title: Hands-on Data Analysis using Excel & Jamovi with Real-World Datasets and AI Tools (Under ageis of DBT STAR STATUS COLLEGE SCHEME) 1. Objectives: To train students from Science, Commerce, and Arts in handling real-world data. To develop skills in data cleaning, preparation, and visualization. To perform descriptive and inferential analysis using Excel and Jamovi. To introduce the use of Artificial Intelligence (AI) in streamlining statistical workflows. To enable students to prepare analytical reports and present data-driven insights. 2. Target Audience: Undergraduate Students and Faculty. Note: The course content is designed to be accessible to students from non-mathematical backgrounds (Arts/Commerce) while remaining rigorous enough for Science students. 3. Teaching Methodology: Hands-on practical sessions with real-world datasets. Interactive discussions and mini-projects. Demonstrations of AI tools for data interpretation. 4. Software Tools: MS Excel: For fundamental data manipulation. Jamovi: For advanced statistical analysis (Open source alternative to SPSS). AI Tools: For assisting in code generation and result interpretation. 5. Detailed Syllabus (20 Hours): Module 1: Data Fundamentals & Cleaning (Excel) Introduction: Types of data, sources, and the universal role of a data analyst across industries . Data Preparation: Handling missing values, outliers, duplicates, and data validation . Module 2: Exploratory Data Analysis (Excel) Descriptive Statistics: Measures of central tendency and dispersion. Visualization: Creating effective charts, pivot tables, and visual stories for diverse audiences. Module 3: Jamovi & Statistical Testing Jamovi Basics: Interface, data coding, and frequency analysis . Hypothesis Testing: T-tests, Chi-square tests, and ANOVA for research decision-making . Interpretation: Understanding p-values and statistical significance in simple terms. Module 4: Advanced Analysis & AI Integration Relationships: Correlation and Simple Linear Regression . AI in Statistics (New): Using AI to choose the right statistical test. Automating routine data cleaning tasks. Interpreting complex statistical outputs using AI prompts. Module 5: Project Work Mini Project: Independent analysis of a dataset relevant to the student's stream Reporting: Preparing a professional report and presentation. 6. Assessment & Certification: Minimum 75% attendance required. Submission of the final mini-project is compulsory for the certificate.