Presentation - Summer Internship at Samatrix.io_template_2.pptx
- 1. Presented by Hari Patel, 23AIML049 Summer Internship Report on Data Analysis Summer Internship at Samatrix.io
- 2. About Samatrix.io Innovating Data Solutions Samatrix.io specializes in consulting, AI/ML, cloud services, and data analytics to empower organizations.
- 3. Internship Overview: Objectives and Insights Leveraging real-time analytics enhances decision- making speed and accuracy, allowing businesses to respond promptly to market changes. Real-time Decision Making Engaging with live datasets provided a practical understanding of the analytics workflow, bridging theory and real-world applications. Hands-on Learning Experience Interdisciplinary Collaboration Working across various domains fostered a comprehensive perspective on data utilization, highlighting its impact on different sectors. 01 02 03
- 4. Learning Objectives Engaging with real datasets enhances the practical understanding of data manipulation and analysis techniques. Hands-on Datasets Following a structured analytics workflow helps streamline the process from data collection to analysis and visualization. Analytics Workflow Tools Used Utilizing tools like Python, Jupyter, and pandas equips interns with essential skills for modern data analysis tasks. 01 02 03
- 5. DATA COLLECTION Collect data using various sources and reliable datasets for analysis. DATA PREPROCESSING Clean and prepare the data for further processing and analysis. DATA ANALYSIS Analyze the data using statistical methods and data visualization techniques. INSIGHTS GENERATION Generate actionable insights and present findings to stakeholders.
- 6. Projects Undertaken Analyzed financial data to assess risk metrics and improve decision-making. Finance Analysis Conducted A/B testing to enhance conversion rates for digital campaigns. Marketing Optimization Healthcare Insights Developed analytical models to improve patient outcomes and streamline operations. 01 02 03
- 7. Finance & Marketing Projects Overview Understanding the Value at Risk (VaR) is crucial for managing financial investments effectively. Risk Assessment Techniques Leveraging analytics to enhance conversion rates and optimize marketing strategies for better ROI. Data-Driven Marketing A/B Testing Insights Implementing A/B testing allows for informed decision-making by comparing the effectiveness of different strategies. 01 02 03
- 8. Project 1: Portfolio Risk Assessment and 95% VaR Goal: Quantify the daily risk of losing money in a simple four-stock portfolio • Stock and portfolio returns are non- normal with fat tails, as shown by the Jarque-Bera test, implying higher likelihood of extreme events. • Average daily volatility is ~2%, while diversification reduces portfolio risk compared to individual stocks. • Traditional Normal VaR tends to underestimate large losses. • Fat-tail–aware methods (Historical VaR, Student’s t VaR, Expected Shortfall) provide more realistic risk estimates.
- 9. Project 2: Website A/B Testing Goal: To evaluate whether Variant B of the website leads to a statistically significant improvement in conversion rates compared to Variant A. • Variant B showed a higher conversion rate than Variant A. • Statistical tests (95% CI, z-test) confirmed the improvement is significant. • Results were consistent with real-time monitoring. • Decision: Adopt Variant B for better conversions. Confidence Interval Plot
- 10. Project 3: Call Center Operations Optimization Goal: To simulate and optimize call-center performance using queuing theory by modeling incoming calls and agent handling, and to evaluate key metrics such as average wait time, system utilization, and agent allocation in order to improve operational efficiency. • The optimal staffing level was determined to be 5 agents. • This setup keeps average wait times below the 5-minute target. • Customer abandonment is minimized, ensuring better service reliability. • Balances operational costs with service quality, avoiding overstaffing. • Delivers a cost-effective and efficient call- center operation.
- 11. Overview of Operations & Healthcare Projects Leveraging real-time data analysis has led to significant improvements in operational efficiency within healthcare settings. Improved Efficiency Data-driven insights empower healthcare professionals to make informed decisions, leading to better patient outcomes. Enhanced Decision-Making Cost Reduction Streamlined processes and analytics have resulted in substantial cost reductions, allowing for reallocation of resources for improved care. 01 02 03
- 12. Project 4: Clinical-Trial Survival Analysis for Treatment Efficiency Goal: To apply survival analysis methods on clinical data in order to evaluate patient outcomes over time, compare treatment effectiveness, and assess the impact of covariates and complex risk patterns on survival. • Survival curves and log-rank tests confirmed significant differences across treatments. • Cox models highlighted treatment type, cancer cell type, and patient condition as key survival factors. • Time-varying and competing risks models captured changing risks and alternate causes of death. • Final Decision: Personalized treatment is essential to improve patient survival outcomes.
- 13. Manufacturing, Sports & Governance Projects Implemented control charts to monitor processes, ensuring consistent quality and reducing defects in manufacturing. Enhanced Quality Control Analyzed player statistics to improve performance metrics, enabling teams to make strategic decisions based on data. Data-Driven Sports Insights Voter Engagement Analysis Mapped voter turnout for different demographics, providing insights for better engagement strategies in governance. 01 02 03
- 14. Project 5: Manufacturing Quality Control With Control Charts Goal: To detect shifts in assembly line defect rates using p- charts and hypothesis testing, ensuring early identification of quality issues and guiding corrective actions. • Control charts (p-chart, CUSUM, EWMA) consistently indicated an upward shift in defect rates starting around day 35. • P-chart showed a gradual increase, CUSUM detected accumulating small shifts, and EWMA confirmed the process moving out of control. • The manufacturing process has deteriorated from a stable state to producing higher defect levels. • Final Decision: Immediate corrective action and root cause investigation are required. Process adjustments must be implemented to restore stability and maintain product quality.
- 15. About Samatrix.io Innovating solutions in data analytics Samatrix.io specializes in AI/ML and data analytics, providing insights that drive decisions across various industries.
- 16. Results & Insights Leveraging real-time insights has enhanced decision-making processes across various departments. Improved Decision-Making Collaborative tools facilitated effective communication and knowledge sharing among team members. Enhanced Team Collaboration Valuable Industry Experience Gained hands-on experience with practical applications of data analytics in real-world projects. 01 02 03
- 17. Key Lessons Learned Effective data storytelling helps convey insights clearly, making complex data accessible and engaging for stakeholders. Data Storytelling Maintaining clean data is crucial for accurate analysis, reducing errors, and enhancing the reliability of insights drawn. Clean Data Teamwork Collaborating with diverse teams fosters innovation, learning, and the successful integration of varied perspectives in data projects. 01 02 03
- 18. Expressing Gratitude The guidance from my mentors has been instrumental in shaping my analytical skills and professional growth. Supportive Mentorship Working with the team at Samatrix.io fostered a dynamic learning experience that enhanced my project contributions. Collaborative Environment Academic Foundation CSPIT, CHARUSAT provided a strong educational background that prepared me for practical challenges in data analysis. 01 02 03
- 19. Blog & GitHub Showcase Discover detailed analysis and findings from my internship through engaging blog posts and GitHub repositories. Comprehensive Insights Learn how data analysis techniques can be applied to solve real-world problems effectively and efficiently. Real-world Applications Open Source Contribution Explore my contributions to open-source projects, fostering collaboration and innovation within the data analysis community. 01 02 03
- 20. CONNECT WITH ME Reach out for inquiries and collaborations on data analysis projects. FOLLOW MY JOURNEY Stay updated through our social media platforms and blog posts. EXPLORE OUR WORK Check out our GitHub for projects and code samples. STAY ENGAGED Join our community for discussions and networking opportunities.
- 21. JOIN ME IN MAKING A DIFFERENCE TODAY! Embrace the future with Machine Learning and Impact