Data Science Learning Path
Master the complete journey from data collection to model deployment. Learn essential concepts, techniques, and tools used by data scientists worldwide.
Essential Tools & Libraries
Python
Most popular language for data science
NumPy
Numerical computing and array operations
Pandas
Data manipulation and analysis
Scikit-learn
Machine learning algorithms
TensorFlow/Keras
Deep learning framework
PyTorch
Deep learning with dynamic graphs
Core Topics
Learn how to gather, clean, and prepare data for analysis. Understanding data quality is crucial for building reliable models.
Subtopics:
- Web scraping and APIs
- Data cleaning and validation
- Handling missing values
- Feature engineering
- Data normalization
Discover patterns, relationships, and insights from your data using statistical summaries and visualizations.
Subtopics:
- Descriptive statistics
- Data visualization techniques
- Correlation analysis
- Distribution analysis
- Outlier detection
Master the mathematical foundations needed for data analysis, hypothesis testing, and probability theory.
Subtopics:
- Probability distributions
- Hypothesis testing
- Confidence intervals
- Regression analysis
- ANOVA and T-tests
Understand supervised and unsupervised learning approaches to build predictive and analytical models.
Subtopics:
- Supervised learning basics
- Linear and logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
- Naive Bayes classifier
Discover hidden patterns in data without labeled outcomes using clustering and dimensionality reduction techniques.
Subtopics:
- K-Means clustering
- Hierarchical clustering
- Principal Component Analysis (PCA)
- t-SNE visualization
- DBSCAN clustering
Explore neural networks and deep learning architectures for complex pattern recognition and prediction tasks.
Subtopics:
- Neural network basics
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- LSTMs and GRUs
- Transfer learning
Learn techniques to assess model performance, prevent overfitting, and ensure your models generalize well.
Subtopics:
- Cross-validation strategies
- Performance metrics
- Confusion matrix and ROC curves
- Hyperparameter tuning
- Learning curves
Master techniques for analyzing and forecasting data that changes over time, such as stock prices or weather patterns.
Subtopics:
- Trend and seasonality
- ARIMA models
- Exponential smoothing
- Forecasting techniques
- Prophet and statsmodels
Create compelling visual representations of data to communicate insights and findings effectively.
Subtopics:
- Matplotlib and Seaborn
- Plotly interactive plots
- Dashboard creation
- Color theory in visualization
- Creating infographics
Learn to work with large-scale datasets using distributed computing frameworks and cloud technologies.
Subtopics:
- Apache Spark basics
- Hadoop ecosystem
- Cloud platforms (AWS, GCP, Azure)
- Data warehousing
- MapReduce concepts
Getting Started with Data Science
1. Build Strong Foundations
Start with Python programming, mathematics (linear algebra, calculus), and statistics. These are the building blocks for everything else.
2. Learn Data Manipulation
Master Pandas and NumPy to efficiently work with datasets. Data preparation is often 80% of the work in real projects.
3. Explore & Visualize
Practice EDA to understand your data. Use Matplotlib, Seaborn, and Plotly to create meaningful visualizations.
4. Build ML Models
Start with simple models like linear regression, then progress to more complex algorithms. Always validate and evaluate properly.
5. Work on Real Projects
Apply your knowledge to real-world datasets from Kaggle or other sources. Portfolio projects are crucial for your career.
Learning Resources
• Coursera - Machine Learning Specialization
• Udacity - Data Science Nanodegree
• DataCamp - Interactive data science courses
• Fast.ai - Practical deep learning
• Kaggle - Competitions and datasets
• Stack Overflow - Q&A for technical help
• GitHub - Share and explore projects
• Reddit r/datascience - Community discussions