Eduzan / Data Science
Data Science Tutorial Roadmap
Worked examples are fully visible. Check-yourself items are study aids you can reveal one at a time.
What is Data Science?
Data Science is an interdisciplinary field that combines statistics, computer science, and domain knowledge to extract meaningful insights from data and support data-driven decision-making.
Significance and Applications
- Enables informed business decisions
- Drives innovation using data and AI
- Used across industries such as healthcare, finance, marketing, and technology
Data Science vs Traditional Data Analysis
- Data science focuses on large-scale, complex data
- Uses machine learning and automation
- Traditional analysis relies on structured data and descriptive methods
Data Science Process
- Data collection
- Data cleaning and preparation
- Exploration and analysis
- Modeling and evaluation
- Deployment and monitoring
Types of Data
- Structured data
- Semi-structured data
- Unstructured data
Data Collection Methods
- Surveys and questionnaires
- Web scraping
- APIs and data streams
Data Sources
- Relational databases
- NoSQL databases
- Public and open datasets
Ethical Considerations
- Responsible data usage
- Consent and transparency
Importance of Data Cleaning
- Improves data quality
- Ensures reliable analysis and modeling
Handling Data Issues
- Missing values
- Outliers and inconsistencies
Data Transformation
- Normalization and standardization
- Encoding categorical variables
Feature Engineering
- Creating meaningful features
- Feature selection techniques
Descriptive Statistics
- Mean, median, mode
- Variance and standard deviation
Data Visualization
- Histograms
- Box plots
- Scatter plots
Pattern Identification
- Trends
- Correlations and anomalies
Tools for EDA
- Python: Pandas, Matplotlib, Seaborn
- R: ggplot2, dplyr
Probability and Distributions
- Normal distribution
- Binomial and Poisson distributions
Hypothesis Testing
- Null and alternative hypotheses
- p-values and confidence intervals
Correlation and Regression
- Linear regression
- Multiple regression
Statistical Significance
- Interpreting results
- Avoiding false conclusions
Types of Machine Learning
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Key Algorithms
- Linear and logistic regression
- Decision trees
- Support Vector Machines (SVM)
Model Evaluation
- Train-test split
- Cross-validation
- Metrics: accuracy, precision, recall
Ensemble Methods
- Random forests
- Boosting algorithms (AdaBoost, Gradient Boosting)
Neural Networks and Deep Learning
- Artificial neural networks
- Convolutional and recurrent neural networks
Specialized Domains
- Natural Language Processing (NLP)
- Time series analysis
Model Selection and Optimization
- Hyperparameter tuning
- Model comparison
Deployment Techniques
- REST APIs
- Batch vs real-time inference
Monitoring and Maintenance
- Model drift detection
- Performance monitoring
Tools
- Docker
- Kubernetes
- Cloud platforms (AWS, GCP, Azure)
Characteristics of Big Data
- Volume
- Velocity
- Variety
Processing Frameworks
- Hadoop ecosystem
- Apache Spark
Storage Solutions
- NoSQL databases
- Data lakes
Ethical Considerations
- Responsible AI usage
- Transparency and accountability
Privacy Laws
- GDPR
- CCPA
Bias and Fairness
- Identifying algorithmic bias
- Fairness-aware modeling
Industry Applications
- Healthcare analytics
- Financial risk modeling
- Marketing and customer analytics
Real-World Projects
- Lessons learned
- Best practices
Emerging Technologies
- Artificial intelligence
- Automated machine learning (AutoML)
Job Market Evolution
- Data scientist roles
- AI and ML specialization
Continuous Learning
- Upskilling strategies
- Lifelong learning mindset
End of lesson.