Data Science Internship

About Course

A structured virtual internship covering 6 weeks of
curriculum — from Python basics to deploying production
ML models. Your access duration depends on your chosen
tier (15 days to 6 months).

Includes:
– 24 animated concept lessons
– 24 deep-dive theory sessions
– 24 IDE-style practice labs
– Weekly live mentor sessions (Core tier and above)
– 2 industry projects + 1 real-world capstone
– Career module — resume, GitHub portfolio, interview prep

Outcome: You will leave with GitHub-ready projects,
an ATS-friendly resume, a working ML pipeline you built
yourself, and a verified internship certificate.

Mentor: Synkoc Industry Expert
Expertise: Data Science · Python · ML · DL · NLP ·
GenAI · AWS/GCP/Azure · MLOps

Write Python code to load, clean and analyse real datasets
Build and evaluate ML models — Linear Regression, Logistic Regression, Decision Trees, Random Forests
Perform Exploratory Data Analysis (EDA) on real Indian business datasets
Apply feature engineering and hyperparameter tuning to improve model accuracy
Implement K-Means clustering and PCA for unsupervised learning
Build and train your first neural network end-to-end
Complete a Customer Churn Prediction project from scratch
Write an ATS-friendly Data Science resume
Build a professional GitHub portfolio
Answer ML interview questions confidently

Course Content

Week 1 — Python & Data Wrangling
Build your foundation: Python, NumPy, pandas, and data cleaning. By end of this week you can read any messy CSV and turn it into clean, analyzable data.

Lesson 1 — Python Basics for Data Science (Animated)
Lesson 1 — Python Basics for Data Science (Theory)
Lesson 1 — Python Basics for Data Science (Lab)
Lesson 2 — NumPy for Numerical Computing (Animated)
Lesson 2 — NumPy for Numerical Computing (Theory)
Lesson 2 — NumPy for Numerical Computing (Lab)
Lesson 3 — pandas for Data Manipulation (Animated)
Lesson 3 — pandas for Data Manipulation (Theory)
Lesson 3 — pandas for Data Manipulation (Lab)
Lesson 4 — Data Cleaning & Preparation (Animated)
Lesson 4 — Data Cleaning & Preparation (Theory)
Lesson 4 — Data Cleaning & Preparation (Lab)

Week 2 — Statistics & Exploratory Data Analysis
Think like a scientist: descriptive stats, visualisations, hypothesis testing, and the EDA checklist that every senior data scientist runs on every new dataset.

Week 3 — Supervised ML I
Train your first ML models from scratch. The workflow, train/test split, Linear Regression, Logistic Regression, and Decision Trees with real Indian datasets.

Week 4 — Supervised ML II
Make your models production-grade: Random Forests, feature engineering, regularisation, and hyperparameter tuning. From 75% accuracy to 88%.

Week 5 — Unsupervised & Deep Learning
Expand the toolkit: customer segmentation with K-Means, dimensionality reduction with PCA, and your first neural network for end-to-end ML pipelines.

Week 6 — Capstone Project
Build a complete Customer Churn ML system end-to-end: EDA, baseline, model, evaluation, deployment, and explainability. Your portfolio centrepiece.

Bonus — Career Awareness
Turn the internship into job offers: ATS-friendly resume, polished GitHub portfolio, and ML interview prep covering bias/variance, metrics, and STAR answers.

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

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About Course

What Will You Learn?

Course Content

Week 1 — Python & Data Wrangling Build your foundation: Python, NumPy, pandas, and data cleaning. By end of this week you can read any messy CSV and turn it into clean, analyzable data.

Lesson 1 — Python Basics for Data Science (Animated)

Lesson 1 — Python Basics for Data Science (Theory)

Lesson 1 — Python Basics for Data Science (Lab)

Lesson 2 — NumPy for Numerical Computing (Animated)

Lesson 2 — NumPy for Numerical Computing (Theory)

Lesson 2 — NumPy for Numerical Computing (Lab)

Lesson 3 — pandas for Data Manipulation (Animated)

Lesson 3 — pandas for Data Manipulation (Theory)

Lesson 3 — pandas for Data Manipulation (Lab)

Lesson 4 — Data Cleaning & Preparation (Animated)

Lesson 4 — Data Cleaning & Preparation (Theory)

Lesson 4 — Data Cleaning & Preparation (Lab)

Week 2 — Statistics & Exploratory Data Analysis Think like a scientist: descriptive stats, visualisations, hypothesis testing, and the EDA checklist that every senior data scientist runs on every new dataset.

Lesson 1 — Descriptive Statistics (Animated)

Lesson 1 — Descriptive Statistics (Theory)

Lesson 1 — Descriptive Statistics (Lab)

Lesson 2 — Data Visualisation with matplotlib & seaborn (Animated)

Lesson 2 — Data Visualisation with matplotlib & seaborn (Theory)

Lesson 2 — Data Visualisation with matplotlib & seaborn (Lab)

Lesson 3 — Hypothesis Testing & A/B Tests (Animated)

Lesson 3 — Hypothesis Testing & A/B Tests (Theory)

Lesson 3 — Hypothesis Testing & A/B Tests (Lab)

Lesson 4 — EDA Walkthrough on Real Data (Animated)

Lesson 4 — EDA Walkthrough on Real Data (Theory)

Lesson 4 — EDA Walkthrough on Real Data (Lab)

Week 3 — Supervised ML I Train your first ML models from scratch. The workflow, train/test split, Linear Regression, Logistic Regression, and Decision Trees with real Indian datasets.

Lesson 1 — ML Workflow & Train/Test Split (Animated)

Lesson 1 — ML Workflow & Train/Test Split (Theory)

Lesson 1 — ML Workflow & Train/Test Split (Lab)

Lesson 2 — Linear Regression (Animated)

Lesson 2 — Linear Regression (Theory)

Lesson 2 — Linear Regression (Lab)

Lesson 3 — Logistic Regression (Animated)

Lesson 3 — Logistic Regression (Theory)

Lesson 3 — Logistic Regression (Lab)

Lesson 4 — Decision Trees (Animated)

Lesson 4 — Decision Trees (Theory)

Lesson 4 — Decision Trees (Lab)

Week 4 — Supervised ML II Make your models production-grade: Random Forests, feature engineering, regularisation, and hyperparameter tuning. From 75% accuracy to 88%.

Lesson 1 — Decision Trees Deep Dive (Animated)

Lesson 1 — Decision Trees Deep Dive (Theory)

Lesson 1 — Decision Trees Deep Dive (Lab)

Lesson 2 — Random Forests (Animated)

Lesson 2 — Random Forests (Theory)

Lesson 2 — Random Forests (Lab)

Lesson 3 — Feature Engineering (Animated)

Lesson 3 — Feature Engineering (Theory)

Lesson 3 — Feature Engineering (Lab)

Lesson 4 — Regularisation & Hyperparameter Tuning (Animated)

Lesson 4 — Regularisation & Hyperparameter Tuning (Theory)

Lesson 4 — Regularisation & Hyperparameter Tuning (Lab)

Week 5 — Unsupervised & Deep Learning Expand the toolkit: customer segmentation with K-Means, dimensionality reduction with PCA, and your first neural network for end-to-end ML pipelines.

Lesson 1 — K-Means Clustering (Animated)

Lesson 1 — K-Means Clustering (Theory)

Lesson 1 — K-Means Clustering (Lab)

Lesson 2 — PCA — Principal Component Analysis (Animated)

Lesson 2 — PCA — Principal Component Analysis (Theory)

Lesson 2 — PCA — Principal Component Analysis (Lab)

Lesson 3 — Introduction to Neural Networks (Animated)

Lesson 3 — Introduction to Neural Networks (Theory)

Lesson 3 — Introduction to Neural Networks (Lab)

Lesson 4 — End-to-End ML Pipeline (Animated)

Lesson 4 — End-to-End ML Pipeline (Theory)

Lesson 4 — End-to-End ML Pipeline (Lab)

Week 6 — Capstone Project Build a complete Customer Churn ML system end-to-end: EDA, baseline, model, evaluation, deployment, and explainability. Your portfolio centrepiece.

Lesson 1 — Customer Churn Capstone — End-to-End (Animated)

Lesson 1 — Customer Churn Capstone — End-to-End (Theory)

Lesson 1 — Customer Churn Capstone — End-to-End (Lab)

Bonus — Career Awareness Turn the internship into job offers: ATS-friendly resume, polished GitHub portfolio, and ML interview prep covering bias/variance, metrics, and STAR answers.

Lesson 1 — Data Science Resume Writing (Animated)

Lesson 1 — Data Science Resume Writing (Theory)

Lesson 1 — Data Science Resume Writing (Lab)

Lesson 2 — GitHub Portfolio Polish (Animated)

Lesson 2 — GitHub Portfolio Polish (Theory)

Lesson 2 — GitHub Portfolio Polish (Lab)