Transform your data into meaningful insights with customizable templates for experiments, analysis, and more. Understand your information and gain crucial insight into what's most important to your company.
1. Notion Growth Experiment Dashboard
Our template helps teams and companies efficiently prioritize, design, track, and analyze growth experiments so that they can focus on growing your business. Based on Sean Ellis' 4-step growth hacking process, it enables teams to score their growth ideas using the ICE prioritization framework. Instead of feeling overwhelmed, they can plan their growth with structure and confidence. Our 3-section experiment view also helps users easily go through each process step, from prioritization to design, and analysis.
2. Data Science Project ToDos
The template is for anyone developing a Data Science project and wants an organised set of TODOs to get work done effectively.
3. Benchmarking Template
This template helps teams and individuals to implement benchmarking in an interactive way. It includes metrics related to finance, marketing, e-commerce, and more that help to compare and analyze performance in different areas.
4. Machine Learning Engineering Wiki
The Notion ML Engineering Wiki template is a comprehensive and adaptable resource for helping build machine learning applications. This template, which includes checklists, expert guidelines, curated resources, and best practices, helps ML engineers simplify their workflow and get optimal outcomes. It covers vital topics such as pipeline creation, problem definition, presentation creation, AI community engagement, and access to a knowledge hub of research papers, repositories, libraries, and visualization methods. Whether you're a beginner or a seasoned ML practitioner, this template is your go-to tool for efficient and successful ML engineering.
5. Growth Experiments Guide
Growth is a new part of the tech industry, without a huge amount of resources online to help you get started.
As I personally struggled with this when growing my career in growth, I thought I’d build this handy template to help the growth practitioners out there get started.
Inside you’ll find the most common growth experiments I’ve run, as well as some tools to help you get started.
Having taken 4 SaaS startups to market using this growth workspace I hope it’ll help you on your growth journey.
Inside you'll find:
A roadmap of the most common experiments I've run
A breakdown of all experiments across the customer journey and pirate metrics
A list of the most commonly used tools to help you x10 your growth strategy
6. Data Scientist & Analyst Roadmap (Beginner)
Our template provides a structured roadmap for individuals, teams, or companies venturing into data science and analytics. It helps them by offering curated study materials, cheat sheets for quick reference, and a dedicated page to capture project ideas. This streamlined approach saves time, enhances learning efficiency, and fosters creativity, enabling users to make significant strides in their data-driven endeavours.
7. Data Science Use Case Canvas
Data Science Use Case Canvas is an essential tool for the success of any data science project. Ideally the canvas should be filled during a brain-storming session involving the data scientists, data engineers, data analysts, developers and project stake-holders. The canvas gives a clear understanding of the project context, use case objectives, the data available, the constraints and challenges that should be taken into consideration, the metrics of evaluation, the error acceptance criteria, and the well defined deliverables.
8. Data science hub
Easily manage all of your data science projects, resources, code snippets and more within a single place. Capture interesting articles, tutorials and videos that you come across so that you can easily retrieve them later.
Develop your skills by keeping track of your online training progress, notes and certificates. Retrieve code snippets from your own database rather than trying to remember where you saw a particular piece of code.
You can also maintain an up-to-date resume to showcase to potential employers.
9. Data Science Projects
These tasks include creating: a requirements.txt file, a Dockerfile, a Makefile, a .gitignore file and a starting point for the state-of-the-art documentation tasks.
Furthermore, the automation establishes a connection between the project and pre-written code snippets. For instance, commonly used files like Makefile, Dockerfile, requirements.txt, and .gitignore are preconfigured as code snippets, simplifying project setup.
In addition to project and task management, this template incorporates various valuable resources to enhance productivity and streamline workflows (you will build those ressource from a project to another): Cheat Sheet Database, Papers Database, Resource Database, Course Databases, Languages Database and Shortcuts Database.
10. Start your Data Science Project
Discover the value of your data science project.
Start by simple steps to discover which data analysis suits your dataset, and which machine learning model fits your needs.
