From Notebook to Dashboard
Sharing results people can use.
From Notebook to Dashboard is a free Data Science Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Data Science Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Beyond the Notebook
A notebook is great for you, but stakeholders will not run cells. To reach them you turn analysis into a shareable dashboard. 🚀
Why a Dashboard
A dashboard lets non-coders explore your results live, filtering and clicking instead of asking you to rerun the code each time.
Static or Interactive
Decide early: a static report is a fixed snapshot, while an interactive app lets users change inputs and see updates.
The Easiest Path
For Python users, Streamlit turns a plain script into a web app with almost no front-end code required.
import streamlit as st
st.title("Sales Dashboard")Show a Number
A headline metric gives instant context. Streamlit's metric widget displays a value and its change from a target side by side.
st.metric("Revenue", "$1.2M", "+8%")Drop In a Chart
Pass any matplotlib or plotly figure straight to the app and it renders inline, reusing the plots you already made.
st.pyplot(fig)Add a Filter
A single widget makes data explorable. A selectbox lets users pick a category and the whole page reacts to their choice.
region = st.selectbox("Region", df["region"].unique())Cache Slow Steps
Wrap heavy loads in a cache so the app stays fast: the data is read once and reused across every user interaction.
@st.cache_data
def load():
return pd.read_csv("sales.csv")Run It Locally
You launch the app from a terminal, and Streamlit serves it in your browser so you can test before sharing it widely.
streamlit run app.pyShare With the World
Push the script to a repo and deploy on a host like Streamlit Community Cloud to hand colleagues a simple URL.
Keep It Trustworthy
A live dashboard needs upkeep: refresh its data, note the last update, and document assumptions so users keep trusting it.
Quick Check
Why move an analysis from a notebook to a dashboard?
Recap
You turned a notebook into a shareable dashboard with Streamlit: metrics, charts, filters, caching, and deploy. That closes the data-to-decision loop. 🎉
Frequently asked questions
Is the “From Notebook to Dashboard” lesson free?
Yes — the full text of “From Notebook to Dashboard” is free to read here on the web, and the Data Science Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Data Science Academy course, upgrade to CoddyKit PRO.
What will I learn in “From Notebook to Dashboard”?
Sharing results people can use. You practise Data Science Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Data Science Academy?
No prior experience is required. Data Science Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “From Notebook to Dashboard” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Data Science Academy lesson?
Yes. Every Data Science Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Feature Importance and SHAP
- Partial Dependence Intuition
- Charts That Persuade Stakeholders
- From Notebook to Dashboard