6-Month Data Scientist Course Roadmap

Transform aspiring data professionals into corporate-ready data scientists with this hands-on, industry-fueled roadmap. Each stage builds technical depth, business acumen, and production-readiness—culminating in 12 full-fledged capstone projects designed for real-world application.

Month 1: Foundational Skills in Python and Data

Core Topics

• Python programming for data analysis (NumPy, pandas)
• Analytical thinking, code best practices
• Data wrangling: cleaning, merging, transforming
• Exploratory Data Analysis (EDA) with matplotlib & seaborn
• Introduction to SQL for structured data
• Mini-Projects: Data audit/visualization report on real-world dataset & SQL challenge retrieving business KPIs.

Month 2: Applied Statistics & Advanced Analytics

Core Topics

• Descriptive & inferential statistics for business
• Hypothesis testing, statistical inference
• Data distributions, central tendency, variance
• Probability and correlation (Pearson, Spearman)
• Mini-Projects: A/B testing experiment analysis & Customer segmentation with clustering and stats.

Month 3: Machine Learning Foundations

Core Topics

• ML workflow: data split, model selection, fitting
• Supervised learning: regression & classification (scikit-learn)
• Unsupervised learning: clustering, PCA, anomaly detection
• Feature engineering & selection
• Evaluation metrics (accuracy, precision, recall, ROC/AUC)
• Mini-Projects: Housing price predictor, Fraud detection model, & Dimensionality reduction on customer data.

Month 4: Advanced ML, Deep Learning & Big Data

Core Topics

• Ensemble methods: Random Forest, XGBoost, LightGBM
• Neural networks with TensorFlow or PyTorch (basics)
• Time series forecasting (ARIMA, Prophet)
• NLP essentials: text vectorization, tf-idf, sentiment analysis
• Handling large datasets (Dask, Spark basics)
• Mini-Projects: Build a sentiment classifier, Stock price forecasting, & Customer churn prediction.

Month 5: Machine Learning Operations & Data Science in Production

Core Topics

• Building reproducible pipelines (scikit-learn, MLflow)
• Model validation and cross-validation
• Version control (Git basics) for DS, collaborative workflows
• Model deployment (Flask/FastAPI), serving with Docker
• Performance monitoring, concept/data drift
• Data visualization and dashboarding (Plotly, Power BI/Tableau basics)
• Mini-Projects: Deploy a predictive model as an API, Automated model retraining workflow, & an Interactive data dashboard.

Month 6: Corporate-Grade Capstone Projects (12 Full-Scale Builds)

Wrap up with a sprint of end-to-end projects tackling real business needs—portfolio-ready evidence for job interviews.

Capstone Projects Lineup

• Customer Lifetime Value Prediction
• Product Recommendation Engine
• End-to-End A/B Testing Platform
• Credit Risk Scoring Model
• Time Series Forecasting Platform
• Social Media Insights & Sentiment Analysis
• Real-Time Fraud Detection System
• Churn Prediction for SaaS/Telecom
• Interactive Executive Dashboard
• Image Classification/Recognition Engine
• Business Data Lake Integration
• Explainable AI Reporting Suite

Learning Experience

• Every lesson: hands-on code and immediate mini-projects
• Team collaboration: peer reviews, pair programming, code demos
• Frequent business case studies for context
• Portfolio: Each capstone is documented, demoed, and ready for recruiters

By graduation, students are:

• Experts in essential and advanced data science techniques
• Fluent in production workflows and business problem-solving
• Portfolio-ready with practical, interview-winning projects