Data Analytics Hackathon

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學生限制

不限學生身分

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Overview: This is a build-and-analysis-focused Data Analytics Hackathon designed for students, aspiring analysts, data professionals, and problem-solvers who want to turn raw data into meaningful insights and actionable recommendations. What You'll Do: On 6th September , participants will work with a real-world dataset provided by the organizers and perform an end-to-end data analysis by: Understanding the business problem and dataset. Cleaning, transforming, and preparing the data. Exploring patterns, trends, and relationships. Performing quantitative and statistical analysis. Building meaningful visualizations and dashboards. Identifying key insights and actionable recommendations. Presenting your analysis and findings. The challenge is centered around using data to solve real-world problems by turning raw datasets into clear insights and data-driven decisions. By the end of the Hackathon, you'll have a complete analysis project that can be showcased in your portfolio, discussed during interviews, or further developed into a real-world analytics solution. Who Can Participate? Individuals and teams are welcome. Recommended team size: Max 2 members & Individualy can also Participate. Beginners and experienced analysts can participate. Participants can use Python, SQL, Excel, Power BI, Tableau, and other analytics tools. Project Requirements: Every team must: Perform the analysis using the dataset provided during the event. Clearly document the analysis process and methodology. Explain the problem statement, approach, findings, and recommendations. Submit a complete and functional analysis with supporting visualizations. Submission Checklist: To complete your submission, provide: Final analysis report or presentation. Dashboard and/or data visualizations. Source code/notebook or analysis file, where applicable. Brief explanation of the methodology and key findings. Key insights and actionable recommendations. Rules & Judging Criteria: Event Rules: Participants must use the dataset provided by the organizers. Pre-built analyses or previously completed projects are not permitted. External tools, libraries, and publicly available resources may be used. Any form of plagiarism or unauthorized copying will result in disqualification. Participants must disclose the use of AI tools in their analysis or submission. All submissions must be completed and submitted before the official deadline. Evaluation Areas: Projects will be assessed based on: Data cleaning and preparation. Analytical approach and problem-solving. Quality and accuracy of insights. Data visualization and storytelling. Depth of analysis. Business relevance and actionable recommendations. Overall presentation and completeness. Turn data into insights, uncover the story behind the numbers, and show how analytics can drive better decisions.

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