Teradata/jupyter-demos
Nov 2024 – Oct 2025
10 Months active
Languages Used
Jupyter NotebookPythonSQLTeradata SQLHTMLJSONTextCSS
Technical Skills
ClearScape AnalyticsData AnalysisData EngineeringData ModelingDatabase ManagementDatabase Operations
Nupur Lal
PROFILE
Nupur Lal
Nupur Lal developed a broad suite of data science and analytics solutions in the Teradata/jupyter-demos repository, focusing on end-to-end workflows for business intelligence, machine learning, and operational analytics. She engineered Jupyter notebooks and Python modules that enabled data exploration, model deployment, and visualization, integrating technologies such as Teradata Vantage, SQL, and ONNX Runtime. Her work included building feature stores, geospatial data pipelines, and customer journey analytics, as well as automating workshop content and enhancing reproducibility. By addressing both technical depth and workflow reliability, Nupur improved onboarding, maintainability, and the speed of data-driven decision-making for analysts and data scientists.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
106Total
Bugs
5
Commits
106
Features
57
Lines of code
40,483
Activity Months10
Your Network
35 people
Work History
October 2025
5 Commits • 2 Features
Oct 1, 2025October 2025 performance highlights for Teradata/jupyter-demos focusing on Telco Complaints Analysis notebooks. Delivered ONNX-backed model deployment and a new topic trends dashboard, along with substantial notebook hygiene and safety improvements to support scalable, reliable data analysis workflows.
5 Commits • 2 Features
Oct 1, 2025October 2025 performance highlights for Teradata/jupyter-demos focusing on Telco Complaints Analysis notebooks. Delivered ONNX-backed model deployment and a new topic trends dashboard, along with substantial notebook hygiene and safety improvements to support scalable, reliable data analysis workflows.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary focusing on key accomplishments: Delivered Telecom Customer Complaints Analysis Notebooks and Workflow (two new notebooks) in Teradata/jupyter-demos, enabling data-driven investigation of telecom complaints using Teradata Vantage for embeddings, semantic similarity, and clustering; combined data-driven insights with LLM-assisted topic generation for actionable trends; provides end-to-end workflow for extracting insights from customer feedback. Commit: 6999952732f95595ffde31dc0dfba7511a7f1f7e.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary focusing on key accomplishments: Delivered Telecom Customer Complaints Analysis Notebooks and Workflow (two new notebooks) in Teradata/jupyter-demos, enabling data-driven investigation of telecom complaints using Teradata Vantage for embeddings, semantic similarity, and clustering; combined data-driven insights with LLM-assisted topic generation for actionable trends; provides end-to-end workflow for extracting insights from customer feedback. Commit: 6999952732f95595ffde31dc0dfba7511a7f1f7e.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 highlights for Teradata/jupyter-demos: Delivered a new ServiceNow Data Analytics Notebook and BI export pathway, enabling analysts to analyze ServiceNow data (audit logs, case lifecycles, reassignments and resolution paths) using Teradata Vantage and ClearScape Analytics, with a JSON export for BI tools and cleanup of temporary tables to simplify downstream dashboards and reporting. This work provides operational insights, reduces manual data wrangling, and enables faster data-driven decisions for ServiceNow processes.
2 Commits • 1 Features
Aug 1, 2025August 2025 highlights for Teradata/jupyter-demos: Delivered a new ServiceNow Data Analytics Notebook and BI export pathway, enabling analysts to analyze ServiceNow data (audit logs, case lifecycles, reassignments and resolution paths) using Teradata Vantage and ClearScape Analytics, with a JSON export for BI tools and cleanup of temporary tables to simplify downstream dashboards and reporting. This work provides operational insights, reduces manual data wrangling, and enables faster data-driven decisions for ServiceNow processes.
August 2025
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly work summary for Teradata/jupyter-demos with focus on delivering analytics visuals and reliable notebook workflows that enable faster insight and decision-making.
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 monthly work summary for Teradata/jupyter-demos with focus on delivering analytics visuals and reliable notebook workflows that enable faster insight and decision-making.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly performance summary for Teradata/jupyter-demos. Key features delivered: an end-to-end NBA Basketball Data Analysis Notebook enabling database connection, exploration of draft history, and LLM-assisted data insights with RAG for game summarization; includes explicit setup and cleanup procedures to ensure reproducibility. Major bugs fixed: corrected an incorrect SQL table reference in the NBA_Drafting notebook, updating from DEMO_user.play_final_data to DEMO_BasketBall.Play_by_Play to target the correct basketball data source. Overall impact: improved reliability, data accuracy, and speed of basketball analytics workflows, enabling faster, data-driven decision making for analysts and stakeholders. Technologies/skills demonstrated: Python, Jupyter notebooks, SQL data querying, LLMs with RAG, data analysis workflows, and environment setup/cleanup for reproducibility.
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly performance summary for Teradata/jupyter-demos. Key features delivered: an end-to-end NBA Basketball Data Analysis Notebook enabling database connection, exploration of draft history, and LLM-assisted data insights with RAG for game summarization; includes explicit setup and cleanup procedures to ensure reproducibility. Major bugs fixed: corrected an incorrect SQL table reference in the NBA_Drafting notebook, updating from DEMO_user.play_final_data to DEMO_BasketBall.Play_by_Play to target the correct basketball data source. Overall impact: improved reliability, data accuracy, and speed of basketball analytics workflows, enabling faster, data-driven decision making for analysts and stakeholders. Technologies/skills demonstrated: Python, Jupyter notebooks, SQL data querying, LLMs with RAG, data analysis workflows, and environment setup/cleanup for reproducibility.
April 2025
March 2025
11 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for Teradata/jupyter-demos. Delivered two major feature suites: 1) Vantage Function Usage Notebooks: end-to-end notebook recipes demonstrating Teradata Vantage functions (VectorDistance, WordEmbeddings, ROC, ClassificationEvaluator, ColumnTransformer, Hashing, image processing) with connection setup, data exploration, processing, and cleanup. 2) VAE Notebooks and ONNX Deployment: notebook suite for Variational Autoencoder workflows, including exporting to ONNX, loading and using ONNX models to generate images from latent vectors, and maintaining dependencies. Key work included a focused set of commits that expanded capability and maintained compatibility: - Vantage function notebooks: 8 commits adding new recipes and updates (e.g., vectordistance, wordembeddings, ROC, classification evaluator, columntransformer, hashing, image handling; plus link updates) and a JPEG/PNG matrix conversion feature. - VAE notebooks: 3 commits for VAE code, ONNX export/loading, and dependency upgrades. Maintenance and usability enhancements included dependency upgrades (tdml upgrade) and updated links to ensure smooth onboarding and reproducibility. Overall, the month delivered tangible, end-to-end data science demonstrations on Teradata Vantage, enabling rapid prototyping and model deployment capabilities while improving project maintainability and contributor onboarding.
March 2025
11 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for Teradata/jupyter-demos. Delivered two major feature suites: 1) Vantage Function Usage Notebooks: end-to-end notebook recipes demonstrating Teradata Vantage functions (VectorDistance, WordEmbeddings, ROC, ClassificationEvaluator, ColumnTransformer, Hashing, image processing) with connection setup, data exploration, processing, and cleanup. 2) VAE Notebooks and ONNX Deployment: notebook suite for Variational Autoencoder workflows, including exporting to ONNX, loading and using ONNX models to generate images from latent vectors, and maintaining dependencies. Key work included a focused set of commits that expanded capability and maintained compatibility: - Vantage function notebooks: 8 commits adding new recipes and updates (e.g., vectordistance, wordembeddings, ROC, classification evaluator, columntransformer, hashing, image handling; plus link updates) and a JPEG/PNG matrix conversion feature. - VAE notebooks: 3 commits for VAE code, ONNX export/loading, and dependency upgrades. Maintenance and usability enhancements included dependency upgrades (tdml upgrade) and updated links to ensure smooth onboarding and reproducibility. Overall, the month delivered tangible, end-to-end data science demonstrations on Teradata Vantage, enabling rapid prototyping and model deployment capabilities while improving project maintainability and contributor onboarding.
February 2025
42 Commits • 18 Features
Feb 1, 2025February 2025 highlights for Teradata/jupyter-demos: delivered a comprehensive suite of notebook recipes and feature enhancements that accelerate data science workflows, improve reproducibility, and broaden analytical capabilities across ML pipelines, preprocessing, NLP, and model evaluation. Improvements spanned notebooks for core ML functions, preprocessing/feature engineering, model tutorials, text processing utilities, and data I/O utilities, complemented by targeted documentation quality boosts.
42 Commits • 18 Features
Feb 1, 2025February 2025 highlights for Teradata/jupyter-demos: delivered a comprehensive suite of notebook recipes and feature enhancements that accelerate data science workflows, improve reproducibility, and broaden analytical capabilities across ML pipelines, preprocessing, NLP, and model evaluation. Improvements spanned notebooks for core ML functions, preprocessing/feature engineering, model tutorials, text processing utilities, and data I/O utilities, complemented by targeted documentation quality boosts.
February 2025
January 2025
30 Commits • 26 Features
Jan 1, 2025January 2025 performance summary for Teradata/jupyter-demos: Delivered a broad set of recipe notebooks and quality improvements that accelerate end-to-end demonstrations of statistical and data processing functions, improve reproducibility, and enable scalable model deployment workflows. Key features delivered include 15+ recipe notebooks for ANOVA, ChiSq, Ftest, UnivariateStatistics, GetRowsWithMissingValues/WithoutMissingValues, Histogram, QQNorm, whichmax/whichmin, ColumnSummary, CategoricalSummaryTable, SimpleImpute fit/transform, plus additional notebooks for GetFutileColumns, NPath visualizer, antiselect, bincode, function fit and transform, attribution, nonlinearcombine, and npath. Added the Helpof function to ease introspection. ModelOps enhancements include a new Datarobot BYOM notebook and a deployment notebook for hypersegmented model pipelines. Codebase housekeeping and fixes were performed to streamline maintenance and remove unused files. Overall impact: faster, reproducible demos; improved data prep and modeling workflows; and a clearer path to production deployments.
January 2025
30 Commits • 26 Features
Jan 1, 2025January 2025 performance summary for Teradata/jupyter-demos: Delivered a broad set of recipe notebooks and quality improvements that accelerate end-to-end demonstrations of statistical and data processing functions, improve reproducibility, and enable scalable model deployment workflows. Key features delivered include 15+ recipe notebooks for ANOVA, ChiSq, Ftest, UnivariateStatistics, GetRowsWithMissingValues/WithoutMissingValues, Histogram, QQNorm, whichmax/whichmin, ColumnSummary, CategoricalSummaryTable, SimpleImpute fit/transform, plus additional notebooks for GetFutileColumns, NPath visualizer, antiselect, bincode, function fit and transform, attribution, nonlinearcombine, and npath. Added the Helpof function to ease introspection. ModelOps enhancements include a new Datarobot BYOM notebook and a deployment notebook for hypersegmented model pipelines. Codebase housekeeping and fixes were performed to streamline maintenance and remove unused files. Overall impact: faster, reproducible demos; improved data prep and modeling workflows; and a clearer path to production deployments.
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for Teradata/jupyter-demos: Delivered two core features with clear business value, alongside repository improvements that enhance maintainability and future work throughput. The work emphasizes end-to-end geospatial data workflows and workshop material automation, aligning with data science enablement and training goals.
5 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for Teradata/jupyter-demos: Delivered two core features with clear business value, alongside repository improvements that enhance maintainability and future work throughput. The work emphasizes end-to-end geospatial data workflows and workshop material automation, aligning with data science enablement and training goals.
December 2024
November 2024
5 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for Teradata/jupyter-demos: Delivered two customer-ready capabilities that advance inventory optimization and feature governance. The Inventory Management with Teradata Vantage enables data exploration, phantom inventory handling, out-of-stock detection, and zero-sales period analysis, with updated schema and naming conventions to improve data quality and consistency. The Teradata Enterprise Feature Store (EFS) Python notebook and tooling demonstrates end-to-end EFS usage—repository setup, feature/entity/data source/group creation and management, search/modify/archive/delete workflows—as well as governance integration and readability-focused documentation. No major defects were reported; review feedback was incorporated to tighten quality. Impact includes faster data-driven decision-making, improved data quality and governance, and a clearer path to ML workflow adoption.
November 2024
5 Commits • 2 Features
Nov 1, 2024November 2024 performance summary for Teradata/jupyter-demos: Delivered two customer-ready capabilities that advance inventory optimization and feature governance. The Inventory Management with Teradata Vantage enables data exploration, phantom inventory handling, out-of-stock detection, and zero-sales period analysis, with updated schema and naming conventions to improve data quality and consistency. The Teradata Enterprise Feature Store (EFS) Python notebook and tooling demonstrates end-to-end EFS usage—repository setup, feature/entity/data source/group creation and management, search/modify/archive/delete workflows—as well as governance integration and readability-focused documentation. No major defects were reported; review feedback was incorporated to tighten quality. Impact includes faster data-driven decision-making, improved data quality and governance, and a clearer path to ML workflow adoption.
Activity
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Quality Metrics
Correctness96.6%
Maintainability96.0%
Architecture95.6%
Performance90.6%
AI Usage23.0%
Skills & Technologies
Programming Languages
CSSHTMLJSONJavaScriptJupyter NotebookPythonSQLTeradata SQLText
Technical Skills
AI IntegrationBYOMBusiness IntelligenceClearScape AnalyticsClusteringCode CleanupCode GenerationCustomer Journey MappingData AnalysisData AnonymizationData EngineeringData ExplorationData ManagementData ManipulationData Modeling
Repositories Contributed To
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