Data Science & Machine Learning Learning Path

Route: /learning-paths/data-science-machine-learning/

Status: Working content draft for review
Purpose: Content architecture, copy direction, SEO starting points, and page-level placeholders
Source basis: WINNOW learning catalogue and Debojyoti Biswas profile documents supplied for the project
Important: Claims, dates, pricing, batch schedules, testimonials, placement outcomes, institutional clients, and certifications must not be published unless verified and approved.

Page purpose

Guide a goal-oriented visitor toward the most relevant WINNOW programme or sequence.

SEO starting point

Tentative content hierarchy

  1. Hero
  2. Who this path is for
  3. Recommended sequence
  4. Capability progression
  5. Where to enter based on experience
  6. Relevant programmes
  7. Instructor credibility
  8. CTA

Draft content

Hero

Data Science & Machine Learning Learning Path

A structured way to choose the relevant WINNOW programme based on what you want to be able to do, rather than on course names alone.

This path is intended for learners who want to move into practical data science and machine learning with enough programming and data-handling foundation to understand what they are doing.

1. Groundwork
Build Python reasoning, data structures, NumPy, Pandas, cleaning and transformation.

2. PredictWorks
Move through preprocessing, feature engineering, regression, classification, ensembles, model evaluation, tuning, explainability, deployment and monitoring.

Outcomes to communicate

The progression should develop the ability to:

[CONFIRM: mathematics/statistics prerequisite for PredictWorks]

Entry-point guidance

[CONTENT REQUIRED: simple “Start here if…” rules based on instructor-approved prerequisites]

Use cards linking to the specific course pages, not duplicated full curricula.

CTA

Need help choosing an entry point?

CTA: Discuss Course Fit