Update app.py

app.py

@@ -69,11 +69,81 @@ html_content = """
         const height = 800;
         // Define the goals and connections data
         const goals = [
-            // ... (your goals data here)
+            { id: 1, x: 100, y: 400, name: "Automate Data Import", description: "Develop scripts to automate exam data extraction from various sources (CSV, Excel, databases) using Pandas read_* functions." },
+            { id: 2, x: 200, y: 300, name: "Data Cleaning", description: "Implement robust data cleaning processes to handle missing values, outliers, and inconsistencies in exam data using Pandas methods like dropna(), fillna(), and apply()." },
+            { id: 3, x: 300, y: 200, name: "Data Transformation", description: "Utilize Pandas for complex data transformations such as pivoting exam results, melting question-wise scores, and creating derived features for analysis." },
+            { id: 4, x: 400, y: 300, name: "Statistical Analysis", description: "Develop functions to automate statistical analysis of exam results, including descriptive statistics, hypothesis testing, and correlation analysis using Pandas and SciPy." },
+            { id: 5, x: 500, y: 400, name: "Performance Metrics", description: "Create custom functions to calculate industry-standard exam performance metrics like item difficulty, discrimination index, and reliability coefficients using Pandas operations." },
+            { id: 6, x: 200, y: 500, name: "Data Filtering", description: "Implement advanced filtering techniques to segment exam data based on various criteria (e.g., demographic info, score ranges) using boolean indexing and query() method in Pandas." },
+            { id: 7, x: 300, y: 600, name: "Reporting Automation", description: "Develop automated reporting systems that use Pandas groupby() and agg() functions to generate summary statistics and performance reports for different exam cohorts." },
+            { id: 8, x: 400, y: 500, name: "Data Visualization", description: "Create interactive dashboards for exam data visualization using Pandas with Plotly or Bokeh, allowing stakeholders to explore results dynamically." },
+            { id: 9, x: 500, y: 600, name: "Time Series Analysis", description: "Implement time series analysis techniques using Pandas datetime functionality to track and forecast exam performance trends over multiple test administrations." },
+            { id: 10, x: 300, y: 400, name: "Data Integration", description: "Develop processes to merge exam data with other relevant datasets (e.g., student information systems, learning management systems) using Pandas merge() and join() operations." },
+            { id: 11, x: 600, y: 300, name: "Performance Optimization", description: "Improve the efficiency of Pandas operations on large exam datasets by utilizing techniques like chunking, multiprocessing, and query optimization." },
+            { id: 12, x: 700, y: 400, name: "Machine Learning Integration", description: "Integrate machine learning models with Pandas for predictive analytics, such as predicting exam success or identifying at-risk students based on historical data." },
+            { id: 13, x: 800, y: 500, name: "Custom Indexing", description: "Implement custom indexing strategies in Pandas to efficiently handle hierarchical exam data structures and improve data access patterns." },
+            { id: 14, x: 900, y: 400, name: "Data Anonymization", description: "Develop Pandas-based workflows to anonymize sensitive exam data, ensuring compliance with privacy regulations while maintaining data utility for analysis." },
+            { id: 15, x: 1000, y: 300, name: "Exam Item Analysis", description: "Create specialized functions using Pandas to perform detailed item analysis, including distractor analysis and reliability calculations for individual exam questions." },
+            { id: 16, x: 600, y: 500, name: "Longitudinal Analysis", description: "Implement Pandas-based methods for tracking student performance across multiple exams over time, identifying learning trends and progress patterns." },
+            { id: 17, x: 700, y: 600, name: "Adaptive Testing Analysis", description: "Develop analysis pipelines using Pandas to evaluate and optimize adaptive testing algorithms, including item selection strategies and scoring methods." },
+            { id: 18, x: 800, y: 700, name: "Exam Equating", description: "Create Pandas workflows to perform exam equating, ensuring comparability of scores across different versions or administrations of an exam." },
+            { id: 19, x: 900, y: 600, name: "Response Time Analysis", description: "Utilize Pandas to analyze exam response times, identifying patterns that may indicate guessing, test-taking strategies, or item difficulty." },
+            { id: 20, x: 1000, y: 500, name: "Collaborative Filtering", description: "Implement collaborative filtering techniques using Pandas to recommend study materials or practice questions based on exam performance patterns." },
+            { id: 21, x: 400, y: 700, name: "Exam Fraud Detection", description: "Develop anomaly detection algorithms using Pandas to identify potential exam fraud or unusual response patterns in large-scale testing programs." },
+            { id: 22, x: 500, y: 800, name: "Standard Setting", description: "Create Pandas-based tools to assist in standard setting processes, analyzing expert judgments and examinee data to establish performance standards." },
+            { id: 23, x: 600, y: 700, name: "Automated Reporting", description: "Implement automated report generation using Pandas and libraries like Jinja2 to create customized, data-driven exam reports for various stakeholders." },
+            { id: 24, x: 700, y: 800, name: "Cross-validation", description: "Develop cross-validation frameworks using Pandas to assess the reliability and generalizability of predictive models in educational assessment contexts." },
+            { id: 25, x: 800, y: 300, name: "API Integration", description: "Create Pandas-based interfaces to integrate exam data analysis workflows with external APIs, facilitating real-time data exchange and reporting." },
+            { id: 26, x: 900, y: 200, name: "Natural Language Processing", description: "Implement NLP techniques using Pandas and libraries like NLTK to analyze free-text responses in exams, enabling automated scoring and content analysis." },
+            { id: 27, x: 1000, y: 100, name: "Exam Blueprint Analysis", description: "Develop Pandas workflows to analyze exam blueprints, ensuring content coverage and alignment with learning objectives across multiple test forms." },
+            { id: 28, x: 100, y: 600, name: "Differential Item Functioning", description: "Implement statistical methods using Pandas to detect and analyze differential item functioning (DIF) in exams, ensuring fairness across different demographic groups." },
+            { id: 29, x: 200, y: 700, name: "Automated Feedback Generation", description: "Create Pandas-based systems to generate personalized feedback for test-takers based on their exam performance and identified areas for improvement." },
+            { id: 30, x: 300, y: 800, name: "Exam Security Analysis", description: "Develop analytical tools using Pandas to assess and enhance exam security, including analysis of item exposure rates and detection of potential security breaches." }
         ];
         const connections = [
-            // ... (your connections data here)
+            { source: 1, target: 2 },
+            { source: 2, target: 3 },
+            { source: 3, target: 4 },
+            { source: 4, target: 5 },
+            { source: 5, target: 7 },
+            { source: 6, target: 7 },
+            { source: 7, target: 8 },
+            { source: 8, target: 9 },
+            { source: 9, target: 16 },
+            { source: 10, target: 13 },
+            { source: 11, target: 12 },
+            { source: 12, target: 20 },
+            { source: 13, target: 16 },
+            { source: 14, target: 21 },
+            { source: 15, target: 17 },
+            { source: 16, target: 18 },
+            { source: 17, target: 19 },
+            { source: 18, target: 22 },
+            { source: 19, target: 21 },
+            { source: 20, target: 29 },
+            { source: 21, target: 30 },
+            { source: 22, target: 23 },
+            { source: 23, target: 25 },
+            { source: 24, target: 12 },
+            { source: 25, target: 23 },
+            { source: 26, target: 15 },
+            { source: 27, target: 15 },
+            { source: 28, target: 22 },
+            { source: 29, target: 23 },
+            { source: 30, target: 21 },
+            // Additional connections for more interconnectivity
+            { source: 1, target: 10 },
+            { source: 2, target: 6 },
+            { source: 3, target: 13 },
+            { source: 4, target: 15 },
+            { source: 5, target: 28 },
+            { source: 8, target: 23 },
+            { source: 11, target: 25 },
+            { source: 14, target: 30 },
+            { source: 24, target: 17 },
+            { source: 26, target: 29 }
         ];
+
         
         // Create the SVG container for the goals and connections
         const svg = d3.select("#goalSpace")