Data Science Contents
IT 위키
1. Understanding Data Science[편집 | 원본 편집]
- What is Data Science?
- Impact on Business
- Key Technologies in Data Science
2. Data Preparation and Preprocessing[편집 | 원본 편집]
- Data Collection
- Handling Missing Data and Outliers
- Normalization and Standardization
3. Exploratory Data Analysis (EDA)[편집 | 원본 편집]
- Goals of Data Analysis
- Basic Statistical Analysis
- Importance of Data Visualization
4. Supervised Learning[편집 | 원본 편집]
- Introduction to Supervised Learning
- Linear Regression
- Decision Trees
- K-Nearest Neighbors (KNN)
- Support Vector Machines (SVM)
- Naive Bayes Classifier
- Regression vs. Classification
5. Unsupervised Learning[편집 | 원본 편집]
- Introduction to Unsupervised Learning
- Clustering: K-Means, Hierarchical
- Dimensionality Reduction: PCA
- Association Rule Learning
6. Causality in Data Science[편집 | 원본 편집]
- Understanding Causality vs. Correlation
- Methods for Identifying Causality (e.g., A/B Testing, Randomized Controlled Trials)
- Applications in Business Decision Making
7. Recommender Systems[편집 | 원본 편집]
- Types of Recommender Systems (Collaborative Filtering, Content-Based)
- Building a Simple Recommender System
- Challenges and Applications in Business
8. Model Evaluation and Selection[편집 | 원본 편집]
- Importance of Model Evaluation
- Cross-Validation
- Evaluation Metrics (Accuracy, Precision, Recall)
- Overfitting vs. Underfitting
9. Model Tuning[편집 | 원본 편집]
- Hyperparameter Tuning
- Grid Search vs. Random Search
- Ensemble Methods (Bagging, Boosting)
10. Data Leakage[편집 | 원본 편집]
- What is Data Leakage?
- Identifying and Preventing Leakage
- Impact on Model Performance
11. Data Science in Business[편집 | 원본 편집]
- Data-Driven Decision Making
- Predictive Analytics: Churn, Demand Forecasting
- Managing Data Science Projects
12. Conclusion[편집 | 원본 편집]
- Integrating Data Science in Business
- Future Trends in Data Science
- Sustainable Growth Through Data Science
