Web Development for Data Scientists

1. Introduction

A web application can make a model or analysis accessible to users, but it adds responsibilities beyond the notebook: request validation, authentication, error handling, and clear contracts between pages and services.

2. Core concepts

Flask maps routes to functions. Templates render server-side HTML; static files hold CSS and JavaScript; query parameters and forms carry user input. An API should validate input and return a stable response format without exposing stack traces or secrets.

3. Topics covered in this module

• HTML and CSS basics for data-science interfaces
• Flask routes, app setup, static/templates folders, and Jinja
• Forms, query parameters, flash messages, and template inheritance
• JSON APIs, validation, error responses, security, and deployment boundaries

4. Syntax and how to read it

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.get('/api/score')
def score():
    value = request.args.get('value', type=float)
    if value is None:
        return jsonify(error='value is required'), 400
    return jsonify(score=value * 2)

The route validates a typed query parameter and returns JSON with an HTTP status. Production apps also need appropriate authentication, rate limits, and error logging.

5. Practical workflow

Keep model loading and prediction logic separate from route handling. Validate form/API values, call a small service function, and render a useful response for success and failure.

6. Common mistakes

• Exposing a development server or debug mode to the public internet.
• Trusting client-provided values or returning raw exception traces.

7. Best practices

• Keep secrets in protected configuration and validate every request boundary.
• Separate prediction logic from the web route so both can be tested independently.

8. Practice task

An API receives a requested threshold as text. What should it do before using the threshold in a calculation?

9. One possible solution

Parse it to the expected numeric type, validate its allowed range, and return a clear client error for malformed or out-of-range values.

10. Summary

Expose analytical results through a small Flask application with templates, forms, and APIs. Keep secrets in protected configuration and validate every request boundary. Separate prediction logic from the web route so both can be tested independently.

11. What comes next

The next module, Large Language Models and Retrieval-Augmented Generation, builds on this foundation and applies it to a new part of the analytics workflow.