Data Science
Roadmap
From Python Basics to Production-Ready ML Models — a structured path for aspiring data scientists.
Who Is This For?
- Aspiring data scientists
- Analysts looking to upskill
- Software engineers moving to ML
- STEM students & curious minds
Prerequisites
- Basic computer literacy
- High school algebra
- Python installed on your machine
- Curiosity & analytical mindset
What You’ll Learn
- Python for data manipulation
- Statistics & probability
- Build & evaluate ML models
- Deploy models to production
| # | Phase | Intensive | Balanced | Flexible |
|---|---|---|---|---|
| 1 | Python Programming Foundation | Week 1 | Weeks 1–3 | Weeks 1–5 |
| 2 | Statistics & Probability | Week 2 | Weeks 4–6 | Weeks 6–11 |
| 3 | Mathematics for ML | Weeks 3–4 | Weeks 7–10 | Weeks 12–19 |
| 4 | Data Analysis & Visualization | Week 5 | Weeks 11–13 | Weeks 20–25 |
| 5 | Machine Learning | Weeks 6–7 | Weeks 14–18 | Weeks 26–35 |
| 6 | Deep Learning & Specialization | Week 8 | Weeks 19–22 | Weeks 36–43 |
| 7 | Production & Career | Week 9 | Weeks 23–26 | Weeks 44–52 |
Python is the lingua franca of data science. Before diving into statistics and ML, you need to be comfortable writing Python code, working with data structures, and using the core libraries that power the data science ecosystem.
Python Fundamentals
- Variables & Data Types
- Control Flow (if/else, loops)
- Functions & Lambda
- List Comprehensions
- Error Handling
Data Structures
- Lists & Tuples
- Dictionaries & Sets
- Strings & String Methods
- Working with Files
- JSON Handling
Dev Environment
- Jupyter Notebooks
- VS Code Setup
- Virtual Environments
- pip & conda
- Git Basics
Python for Data
- NumPy Basics
- Pandas Introduction
- Reading CSV/Excel Files
- Basic Data Inspection
- Simple Plots with Matplotlib
🛠 Data File Processor
Build a CLI tool that reads CSV files, cleans data, and outputs summary statistics.
🛠 Personal Expense Analyzer
Analyze bank statement data, categorize spending, and visualize trends over time.
Complete both projects. Write Python scripts that read, process, and analyze data files without constantly Googling basic syntax.
Statistics is the backbone of data science. It’s how we make sense of data, quantify uncertainty, and make decisions with confidence. Skip this phase at your peril — without statistics, you’re just guessing.
Descriptive Statistics
- Mean, Median, Mode
- Variance & Std Deviation
- Percentiles & Quartiles
- Skewness & Kurtosis
Probability Fundamentals
- Basic Probability Rules
- Conditional Probability
- Bayes’ Theorem
- Expected Value
Distributions
- Normal Distribution
- Binomial & Bernoulli
- Poisson Distribution
- Central Limit Theorem
Inferential Statistics
- Confidence Intervals
- Hypothesis Testing
- p-values & Significance
- t-tests & Chi-square
🛠 A/B Test Analyzer
Determine if the difference in conversion rates is statistically significant and calculate confidence intervals.
🛠 Sales Statistical Report
Compute descriptive stats by category, identify correlations, and test hypotheses about seasonal effects.
Complete the A/B Test Analyzer. Look at any dataset and immediately compute statistics, identify distributions, and draw statistically-grounded conclusions.
This is where many learners struggle or skip entirely — and it’s why they later hit a ceiling. Understanding the math means knowing WHY algorithms work, WHEN they’ll fail, and HOW to debug them.
Linear Algebra
- Vectors & Operations
- Matrix Multiplication
- Linear Transformations
- Eigenvalues & Eigenvectors
- SVD & PCA
Calculus for Optimization
- Derivatives & Gradients
- Partial Derivatives
- Chain Rule (Backprop!)
- Gradient Descent
- Convexity & Local Minima
Probability for ML
- Maximum Likelihood Estimation
- Bayesian Inference
- Prior, Likelihood, Posterior
- Information Theory Basics
🛠 Linear Regression from Scratch
Build linear regression using only NumPy. Implement normal equation AND gradient descent. Visualize convergence.
🛠 PCA Visualization Tool
Implement PCA from scratch using eigendecomposition. Apply it to a high-dimensional dataset and visualize the components.
Implement linear regression from scratch. Explain WHY gradients point to the minimum, WHAT eigenvalues represent, and HOW probability connects to model training.
Now we combine Python, statistics, and math into practical data analysis — exploring datasets, cleaning messy data, engineering features, and creating visualizations that tell stories.
Pandas Mastery
- DataFrames & Series
- Groupby & Aggregation
- Merging & Joining
- Pivot Tables
- Time Series
Data Cleaning
- Handling Missing Values
- Outlier Detection
- String Cleaning & Regex
- Deduplication
- Data Validation
EDA
- Univariate Analysis
- Bivariate Analysis
- Correlation Analysis
- Distribution Analysis
- Documenting Findings
Visualization
- Matplotlib Deep Dive
- Seaborn for Stats
- Plotly for Interactive
- Choosing the Right Chart
- Best Practices
🛠 End-to-End EDA
Comprehensive EDA on Titanic or Housing dataset. Clean, analyze, visualize, and document every insight.
🛠 Interactive Dashboard
Build an interactive Plotly Dash or Streamlit dashboard with filters and dynamic visualizations.
Complete a full EDA project with a written report. Take any messy dataset and, within hours, have it cleaned, analyzed, and visualized with key insights documented.
This is what you’ve been building toward. With your foundation in Python, statistics, and math, you’ll now understand ML algorithms deeply — not just how to call them, but why they work and how to evaluate them properly.
ML Fundamentals
- Supervised vs Unsupervised
- Train/Val/Test Split
- Bias-Variance Tradeoff
- Cross-Validation
- Feature Scaling
Regression
- Linear & Polynomial
- Ridge & Lasso
- Decision Trees
- Random Forests
- XGBoost
Classification
- Logistic Regression
- Decision Trees & RF
- Support Vector Machines
- K-Nearest Neighbors
- Naive Bayes
Unsupervised
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- t-SNE & UMAP
- Anomaly Detection
Model Evaluation
- MSE, MAE, R²
- Precision, Recall, F1
- ROC-AUC Curves
- Hyperparameter Tuning
- Model Selection
🛠 House Price Prediction
Complete ML pipeline. Compare algorithms, feature engineer, tune hyperparameters, and write an interpretation report.
🛠 Customer Churn
Predict churning customers. Handle class imbalance, interpret feature importance, give business recommendations.
🛠 Customer Segmentation
Use clustering to segment customers by behavior. Profile each segment and visualize results.
Complete all three projects with documented notebooks. Frame a business problem as an ML task, build and evaluate models, and communicate results to non-technical stakeholders.
Deep learning has revolutionized AI. While not every problem needs neural networks, understanding them opens doors to computer vision, NLP, and cutting-edge applications. Choose a specialization based on your interests.
Neural Net Fundamentals
- Perceptrons & Activations
- Forward & Backpropagation
- Loss Functions & Optimizers
- Dropout, BatchNorm
- TensorFlow/PyTorch Basics
🖼 Computer Vision Track
- Convolutional Neural Nets
- Image Classification
- Object Detection
- Transfer Learning (ResNet)
- Data Augmentation
💬 NLP Track
- Text Preprocessing
- Word Embeddings
- RNNs & LSTMs
- Transformers & Attention
- BERT & GPT (using)
📈 Time Series Track
- Time Series Decomposition
- ARIMA Models
- Prophet
- LSTMs for Forecasting
- Evaluation Metrics
🛠 Image Classifier
Custom image classifier using pretrained ResNet/VGG. Fine-tune on your dataset and deploy as a web app.
🛠 Sentiment Analysis
NLP pipeline classifying sentiment in product reviews. Compare traditional ML with transformer-based approaches.
Complete one specialization track project. Understand when deep learning adds value and implement using modern frameworks and pretrained models.
The final step: taking your models from notebooks to production and yourself from learner to professional. Deploy models, learn MLOps fundamentals, and build the portfolio that gets you hired.
Model Deployment
- Flask/FastAPI for ML APIs
- Docker Basics
- Cloud (AWS/GCP/Azure)
- Streamlit for ML Apps
- Model Serialization
MLOps Fundamentals
- ML Pipelines
- Experiment Tracking (MLflow)
- Model Versioning
- Monitoring & Drift Detection
- CI/CD for ML & DVC
Portfolio & Career
- GitHub Best Practices
- Project Documentation
- Technical Blogging
- LinkedIn Optimization
- Interview Preparation
🛠 End-to-End ML App
Deploy a previous project as a full web app with data pipeline, model training, API, and frontend. Document everything.
🛠 Portfolio Website
Showcase your best 3–5 projects each with problem statement, methodology, results, and lessons learned.
Deploy at least one ML application publicly and have a portfolio with 3–5 documented projects demonstrating end-to-end ML capabilities.
Don’t skip the math — it separates good data scientists from great ones.
Build projects with real, messy data. Kaggle is good but not enough.
Learn to communicate findings to non-technical audiences.
Statistics before machine learning — always, no exceptions.
Understand algorithms deeply before reaching for AutoML.
Join communities: Kaggle, Reddit r/datascience, LinkedIn DS groups.
Practice explaining your projects — interviews are about communication.
Stay curious and keep learning — the field evolves rapidly.
Set up your Python environment (Anaconda recommended for beginners)
Choose your learning pace: Intensive, Balanced, or Flexible
Start with Phase 1 — don’t skip ahead, the foundation matters
Join a community for accountability, help, and motivation
Begin building your GitHub profile from day one
Commit to consistency over intensity — steady wins the race
If You want to Learn DM : Contact@pharmabharat.com