Build Your Data Science Portfolio in 4 Sessions with Mital Kinderkhedia AMBCS

Build Your Data Science Portfolio in 4 Sessions | 1:1 Mentorship

Ready to turn your Python and machine learning knowledge into a portfolio employers can explore?

In this 4-session, hands-on mentoring programme, I'll guide you from project idea to a polished GitHub portfolio project. Learn to work with real-world datasets, perform EDA, build and evaluate a machine learning model, and create professional project documentation.

Perfect for aspiring data scientists, graduates and career changers who want practical experience and a portfolio they can confidently showcase.

4 × 60-minute sessions

Stop just learning data science. Start demonstrating what you can do.

SESSION 01 · DISCOVER & DESIGN

From Idea to Data Science Project Blueprint

Goal: Choose a meaningful project and establish a professional development plan.

1. Understand what makes a data science portfolio project industry-aligned.
2. Identify a project idea aligned with the learner's target role.
3. Find and evaluate real-world datasets.
4. Define the business problem, objectives and success metrics.
5. Set up Python, Jupyter Notebook, Git and GitHub.
6. Create the project repository and folder structure.

Practical deliverable: A GitHub repository, project brief, dataset and project roadmap.

SESSION 02 · EXPLORE & ANALYSE

Real-World Data Analysis & Storytelling

Goal: Transform raw data into meaningful insights.

1. Load, inspect and clean data using Pandas and NumPy.
2. Handle missing values, duplicates and inconsistent data.
3. Conduct exploratory data analysis (EDA).
4. Identify trends, patterns, relationships and outliers.
5. Create clear, professional visualisations using Matplotlib or Seaborn.
6. Translate findings into meaningful insights.

Practical deliverable: A clean dataset, documented EDA notebook and insight-driven visualisations.

SESSION 03 · BUILD & VALIDATE

Machine Learning & Model Evaluation

Goal: Build a credible, reproducible machine learning workflow.

1. Select a suitable baseline model for the problem.
2. Prepare features and split data correctly.
3. Build a Scikit-learn pipeline.
4. Train and evaluate a model using appropriate metrics.
5. Understand overfitting, data leakage and model limitations.
6. Compare results against a baseline and explain the findings.
7. Discuss how the model could be used in a real-world setting.

Practical deliverable: A reproducible ML notebook, evaluation results and documented model limitations.

SESSION 04 · POLISH & PUBLISH

GitHub Portfolio, Documentation & Interview Readiness

Goal: Turn the project into a polished, recruiter-friendly portfolio piece.

1. Refactor and organise notebooks and project files.
2. Write a professional README with project objectives, methods and results.
3. Add visualisations, key findings and setup instructions.
4. Apply Git and GitHub best practices.
5. Review reproducibility, repository presentation and project quality.
6. Practise explaining the project as an interview case study.
7. Plan the next portfolio project based on the learner's target role.

Practical deliverable: A polished, published GitHub project with documentation and a project walkthrough.

Important: How the Mentoring Programme Works

This is a hands-on mentoring programme, not a done-for-you portfolio service. You will be responsible for building your own data science project, writing and running your code, analysing the data and completing the assigned tasks.

My role is to guide, teach and support you throughout the process. I will provide expert direction, practical explanations, feedback and project templates where appropriate to help you structure your work and develop industry-aligned skills.

The aim is for you to understand what you're building, why you're building it and how to confidently present it to potential employers. By the end of the four sessions, you'll have developed a portfolio project through your own effort, with my guidance and support along the way.

You do the building. I provide the guidance, tools and feedback to help you succeed.