Data Science Curriculum
A comprehensive 24-week journey to becoming a professional data scientist with Python, machine learning, and big data technologies.
Program Investment
What's Included
- 24 week intensive program
- 24/7 mentor-assisted learning
- 11+ comprehensive modules
- Real-world data science projects
- Blockchain-verified certification
Orientation & Environment Setup
Week 0
Goal: Set up all tools and introduce course flow.
Topics:
- Welcome & Data Science Roadmap
- Tools Installation (Python, Anaconda, JupyterLab, Git, VS Code)
- Datasets & Project Guidelines
Project:
Submit screenshot of working Python + Jupyter environment
Python Foundations for Data Science
Weeks 1-2
Goal: Learn Python essentials for analysis and modeling.
Topics:
- Data types, loops, functions, list comprehensions
- Error handling and debugging
- NumPy for numerical computing
- Pandas for data manipulation
Project:
Clean and transform a CSV dataset using Pandas
Data Visualization
Weeks 3-4
Goal: Create effective data visualizations.
Topics:
- Principles of data visualization
- Static visuals with Matplotlib and Seaborn
- Interactive visuals with Plotly
- Storytelling with charts
Project:
Build a dashboard from a dataset (Jupyter Notebook or Python script)
Statistics & Probability for Data Science
Weeks 5-6
Goal: Apply statistical reasoning to datasets.
Topics:
- Descriptive statistics and probability basics
- Hypothesis testing and p-values
- Correlation and simple regression
- Probability distributions
Project:
Analyze dataset for significant differences between groups
SQL for Data Science
Week 7
Goal: Query databases for analysis.
Topics:
- SQL basics: SELECT, filtering, aggregations
- Advanced SQL: joins, subqueries
- Window functions for analytics
Project:
Use SQL to produce analytics reports from a sample database
Machine Learning Foundations
Weeks 8-9
Goal: Understand and implement ML models.
Topics:
- ML concepts and workflow
- Supervised learning: regression and classification
- Unsupervised learning: clustering and dimensionality reduction
- Model evaluation with scikit-learn
Project:
Train and evaluate ML models on a real dataset
Feature Engineering & Model Tuning
Weeks 10-11
Goal: Improve model performance.
Topics:
- Feature engineering techniques
- Encoding, scaling, handling missing values
- Hyperparameter tuning with GridSearchCV
- Cross-validation strategies
Project:
Improve model performance from Module 5 with feature engineering
Deep Learning Foundations
Weeks 12-14
Goal: Build neural networks for predictive tasks.
Topics:
- Neural network basics
- Building models with TensorFlow/Keras
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
Project:
Build a deep learning model for image or text classification
Data Science for the Real World
Weeks 15-17
Goal: Apply DS in production and real-world contexts.
Topics:
- From notebook to API
- Model deployment with Flask/FastAPI
- Model monitoring and maintenance
- Ethical considerations in AI
Project:
Deploy a simple ML API on a cloud platform
Big Data & Cloud for Data Science
Weeks 18-19
Goal: Work with large datasets in the cloud.
Topics:
- Big data concepts and challenges
- PySpark for distributed processing
- Google BigQuery for analytics
- AWS S3 and cloud storage
Project:
Run a big data analysis on cloud-hosted dataset
Capstone Data Science Project
Weeks 20-24
Goal: Build and present a complete data science project.
Requirements:
- End-to-end data science pipeline
- Data collection, cleaning, and analysis
- Machine learning model development
- Model deployment and API creation
- Comprehensive documentation and presentation
Project:
Submit final project with dataset, code, documentation, and presentation video
Ready to start your journey as a Data Scientist?
Apply to Skymirror Academy