Data Science Project & Notebook Tracker

Data science projects are uniquely difficult to manage. They're non-linear, experimental, and full of dead ends that still produce valuable insights. You're juggling datasets, notebooks, model experiments, feature engineering, stakeholder requests, and production deployments - often across multiple projects simultaneously. Traditional project management tools assume work flows in a straight line from "to do" to "done." Data science doesn't work that way.

t0ggles is the project management tool that gives data science teams the flexibility to track experiments, manage datasets, and coordinate ML workflows without forcing a rigid process. Use custom properties to log model metrics, dataset versions, and experiment parameters. Track experiment lineage with task dependencies. Manage multiple research streams on one board with multi-project support. Start on the free plan, and when you outgrow it every feature is $5/user/month.

#The Challenge: Why Data Scientists Need Different Tools

Data science workflows break the assumptions baked into most project management software:

Experiments are the work. In software engineering, you know what you're building before you build it. In data science, the experiment itself determines the next step. A model that underperforms doesn't mean the task "failed" - it means you learned something. Tools that only track binary done/not-done miss the nuance.

Parallel exploration is normal. Data scientists routinely explore multiple approaches simultaneously - different feature sets, model architectures, preprocessing strategies. You need to track these parallel threads, compare results, and converge on the best approach. Linear task lists can't represent this branching workflow.

Context is everything. Six months from now, someone will ask why you chose Random Forest over XGBoost for this project. The answer is buried in a Jupyter notebook, a Slack message, and your memory. Without structured documentation of decisions, rationale, and results, institutional knowledge evaporates.

Stakeholders speak a different language. Business stakeholders want to know "when will the model be ready?" while you're still figuring out if the data quality is sufficient. Bridging the gap between experimental research and business timelines requires project management that can represent both perspectives.

#How t0ggles Helps Data Science Teams

#Custom Properties: Track Experiment Metadata

Custom properties turn every task into a structured experiment record:

• Dataset (select): Which dataset this experiment uses
• Model Type (select): Random Forest, XGBoost, Neural Net, Linear Regression, etc.
• Accuracy (number): Primary evaluation metric
• F1 Score (number): Secondary metric
• Training Time (text): How long the model took to train
• Status (select): Exploring, Training, Evaluating, Production, Archived
• Notebook Link (text): URL to the Jupyter notebook

Sort experiments by accuracy to find your best-performing models. Filter by dataset to see all experiments on a specific data source. The board becomes an experiment tracker that's visual, filterable, and accessible to the whole team - not locked in one person's notebook environment.

#Task Dependencies: Model Experiment Lineage

Task dependencies capture the relationships between experiments:

• Data cleaning must finish before feature engineering starts
• Feature engineering v2 depends on the results of baseline model evaluation
• Hyperparameter tuning depends on the best model architecture being selected
• A/B test setup depends on the production model being deployed

The Gantt view shows the experiment pipeline as a timeline with dependency arrows. When a data quality issue delays the cleaning phase, you immediately see the downstream impact on model training and deployment schedules.

#Multi-Project Boards: Organize Research Streams

Data science teams typically work on multiple projects: a recommendation engine, a churn prediction model, an NLP classifier, and ad-hoc analysis requests. t0ggles multi-project boards keep everything organized:

• Recommendation Engine project - collaborative filtering experiments, A/B tests
• Churn Prediction project - feature engineering, model comparison, deployment
• NLP Classifier project - data labeling, fine-tuning, evaluation
• Ad-Hoc Analysis project - stakeholder requests, one-off investigations

Focus Mode zooms into one research stream. The combined view shows your team's full workload. Color-coded projects make it instantly clear which research stream each task belongs to.

#Notes: Document Decisions and Findings

Notes in t0ggles are perfect for data science documentation:

• Experiment logs: Summarize findings, include key metrics, note what worked and what didn't
• Architecture decisions: Document why you chose this approach over alternatives
• Data dictionaries: Describe datasets, features, and transformations
• Meeting notes: Capture stakeholder requirements and feedback

The rich text editor supports code blocks for SQL queries, Python snippets, and configuration examples. Link notes to related experiment tasks so the context is always connected. Organize in folders by project or topic.

#Milestones: Mark Research Phases

Milestones mark significant checkpoints in your data science projects:

• Data Collection Complete - all datasets acquired and validated
• Baseline Model Established - first model with benchmark metrics
• Model Selection Final - best architecture chosen for production
• Production Deployment - model serving live predictions
• A/B Test Complete - statistical significance reached

Milestones give stakeholders the high-level progress view they need while you manage the detailed experiment work underneath. The milestone progress tracking shows percentage complete based on associated tasks.

#AI Task Creation: Capture Research Ideas Fast

Data science generates ideas constantly - during EDA, while reading papers, during team discussions. AI task creation captures them without breaking your flow:

"Need to try BERT embeddings instead of TF-IDF for the classifier, also want to experiment with data augmentation for the imbalanced classes, and we should set up monitoring for model drift in production."

Three structured tasks created instantly, assigned to the right project, with appropriate priorities. Your ideas are captured and organized, ready for your next planning session.

#Board Forms: Structured Intake for Analysis Requests

Ad-hoc requests are the tax on every data science team. "Can you pull churn by segment?" arrives by DM, by email, and in the hallway, each one missing half the context you need. Board forms replace that with one link you hand to the business teams.

Build the form once: requesting team, the question you want answered, the decision it feeds, a deadline, and a file upload for the spreadsheet they already made. Choice options are real board entities, so the requesting team sets the project and urgency sets priority - the request arrives on your Ad-Hoc Analysis project already scoped and routed. Anything the form doesn't map to a field lands in a structured block in the task description, so nothing is lost.

Keep the form members-only for internal use, or use verified email so anyone in the company can submit without an account. The approval queue lets you triage before requests hit the board, and the two-way email thread on each submission is where you ask the clarifying question that used to cost three days of back-and-forth.

#Data Science Workflows In t0ggles

#End-to-End ML Project

Set up a project with statuses: Backlog, Exploring, In Progress, Review, Done. Create tasks for each phase of the ML lifecycle:

Data Phase: Data collection, cleaning, EDA, feature engineering. Each task has custom properties for dataset name and version. Dependencies ensure cleaning happens before feature engineering.

Modeling Phase: Baseline model, experiment iterations, hyperparameter tuning. Custom properties track model type, metrics, and training parameters. Multiple experiment tasks run in parallel, each linked to the feature engineering task they depend on.

Deployment Phase: Model packaging, API setup, monitoring, A/B testing. Dependencies chain these sequentially. Milestones mark "Model in Staging" and "Model in Production."

The Gantt view shows the full project timeline. Stakeholders see milestone progress. The team sees detailed experiment status.

#Kaggle Competition or Research Sprint

Create a time-boxed project with aggressive deadlines. Dump all approach ideas as tasks. Use tags to categorize: Feature Engineering, Model Architecture, Ensemble, Post-Processing.

Team members self-assign tasks and work in parallel. Each experiment task gets updated with results as they come in. Sort by accuracy score to see the leaderboard of approaches. The Calendar view shows what needs to happen before the deadline.

#Stakeholder Request Management

Business teams submit analysis requests through a board form, so every request shows up with the same fields answered. Create an "Ad-Hoc Analysis" project with statuses: Requested, Scoping, In Progress, Review, Delivered. Each request becomes a task with custom properties for requesting team, urgency, and estimated effort.

Use filter presets to create a "My Queue" view showing only your assigned analysis tasks sorted by due date. Stakeholders can track progress on the board without pinging you for updates.

#What Data Science Teams Need vs What t0ggles Delivers

What You Need	How t0ggles Delivers
Experiment tracking with metrics	Custom properties for accuracy, F1, model type, dataset version
Experiment lineage and dependencies	Task dependencies showing experiment chains in Gantt view
Multi-project research organization	Projects per research stream with Focus Mode
Research documentation	Notes with code blocks, linked to experiment tasks
Stakeholder progress visibility	Milestones with percentage tracking and public boards
Ad-hoc request intake	Board forms turn stakeholder requests into scoped, routed tasks
Fast idea capture	AI task creation from natural language
Parallel experiment management	Multiple tasks with shared dependencies and comparison views
Team workload tracking	Reports showing task distribution and completion rates

#Why Choose t0ggles for Data Science

vs Jira: Jira was designed for sprint-based software development, not experimental research. Its rigid workflows don't accommodate the branching, iterative nature of data science. t0ggles adapts to how you actually work.

vs Jupyter/notebook-only tracking: Notebooks are great for code but terrible for project management. You can't see team workload, set dependencies, or share progress with stakeholders from a notebook. t0ggles complements your notebook environment with structured project coordination.

vs Trello: Trello is too simple for data science - no custom properties for tracking metrics, no dependencies for modeling experiment order, no Gantt view for timeline management.

vs dedicated MLOps platforms: MLflow and Weights & Biases track model experiments, but they don't manage the broader project - stakeholder requests, team coordination, documentation, timelines. t0ggles handles the project management layer that sits above your MLOps tooling.

#Simple, Affordable Pricing

Start on the free plan - no card, no trial clock. It is enough to run your first experiment board and see whether t0ggles fits the way your team works, and you can stay on it as long as you like. See what's included.

When you outgrow it, one plan unlocks everything.

$5 per user per month (billed annually) includes:

• Unlimited boards, projects, tasks, notes, and files
• Custom properties for experiment metadata
• Task dependencies with Gantt view
• Milestones for research phase tracking
• AI task creation for fast idea capture
• Notes with code blocks for documentation
• Board forms for stakeholder request intake
• Reports with CSV/PDF export
• All integrations (GitHub, Zapier, Google Calendar)

One paid plan. No per-seat surprises.

Free forever plan - no card required. Want the paid features while you evaluate? Every first subscription starts with a 14-day free trial.

#Get Started Today

Data science projects deserve project management that understands experimentation, parallel exploration, and iterative discovery. t0ggles gives you the structured tracking, flexible organization, and team visibility that data science workflows demand.

Start for free and bring clarity to your data science projects.