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Data Science Learning Path
Master the tools and techniques of data science, from exploratory data analysis to advanced machine learning. Develop the skills to extract meaningful insights from data and build predictive models with real-world applications.
Overview
Difficulty:
Intermediate
Duration:
12-16 weeks
Prerequisites:
Basic Python knowledge, understanding of statistics fundamentals
Data Science combines statistical analysis, programming, and domain expertise to extract knowledge and insights from data. In this learning path, you'll develop the technical skills to work with data at scale, build predictive models, and communicate data-driven insights effectively. This path is ideal for those looking to enter the field of data science or enhance their existing data analysis skills.
Learning Modules
Module 1: Foundations of Data Science
- Overview of the data science workflow
- Setting up your data science environment
- Python for data science: NumPy and Pandas
- Data cleaning and preprocessing techniques
- Exploratory data analysis fundamentals
- Project: Cleaning and analyzing a real-world dataset
Module 2: Statistical Analysis
- Descriptive statistics and distributions
- Inferential statistics and hypothesis testing
- Probability theory fundamentals
- Correlation analysis and causation concepts
- A/B testing methodology
- Project: Statistical analysis of business metrics
Module 3: Data Visualization
- Principles of effective data visualization
- Matplotlib and Seaborn for statistical graphics
- Interactive visualizations with Plotly
- Dashboard creation with Streamlit
- Storytelling with data
- Project: Building an interactive data dashboard
Module 4: Machine Learning Fundamentals
- Supervised vs. unsupervised learning
- Model training, validation, and evaluation
- Feature engineering and selection
- Regression models (linear, polynomial, regularized)
- Classification models (logistic regression, decision trees)
- Project: Predictive modeling competition
Module 5: Advanced Machine Learning
- Ensemble methods (Random Forests, Gradient Boosting)
- Support Vector Machines
- Clustering and dimensionality reduction
- Model interpretability techniques
- Hyperparameter tuning and optimization
- Project: Solving a complex ML problem
Module 6: Deep Learning Introduction
- Neural network fundamentals
- Introduction to TensorFlow and Keras
- Convolutional Neural Networks for image data
- Recurrent Neural Networks for sequence data
- Transfer learning techniques
- Project: Image classification or text analysis
Module 7: Data Science in Production
- Model deployment workflows
- API development for ML models
- Monitoring model performance
- ML pipelines and automation
- Ethics in data science and AI
- Project: Deploying a model to production
Module 8: Capstone Project
- End-to-end data science project
- Problem definition and scoping
- Data collection and preparation
- Model development and evaluation
- Solution presentation and documentation
- Building a data science portfolio
Featured Projects
Predictive Customer Analytics
Build a customer churn prediction system that identifies at-risk customers and recommends retention strategies. Apply classification algorithms, feature engineering, and model explainability techniques to provide actionable insights.
Recommendation Engine
Create a content recommendation system similar to those used by streaming platforms. Implement collaborative filtering, content-based filtering, and hybrid approaches to generate personalized recommendations.
Anomaly Detection System
Develop a system to detect unusual patterns in time-series data, such as fraud detection in financial transactions or identifying equipment failures from sensor data. Apply statistical methods and machine learning techniques for anomaly detection.
Additional Resources
Recommended Books
- "Python for Data Analysis" by Wes McKinney
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
- "Data Science from Scratch" by Joel Grus
- "Machine Learning Engineering" by Andriy Burkov
Online Documentation
Community Resources
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C:\>
./enroll.sh DATA_SCIENCE