Data Science Contents 편집하기


=== 1. Understanding Data Science ===

* What is Data Science?
* Impact on Business
* Key Technologies in Data Science

=== 2. Data Preparation and Preprocessing ===

* Data Collection
* Handling [[Missing Data|'''Missing Data''']] and '''[[Outlier (Data Science)|Outlier]]'''s
* Normalization and Standardization

=== 3. Exploratory Data Analysis (EDA) ===

* Goals of Data Analysis
* Basic Statistical Analysis
* Importance of Data Visualization

=== 4. Supervised Learning ===

* 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 ===

* Introduction to Unsupervised Learning
* Clustering: K-Means, Hierarchical
* Dimensionality Reduction: PCA
* Association Rule Learning

=== 6. Causality in Data Science ===

* Understanding Causality vs. Correlation
* Methods for Identifying Causality (e.g., A/B Testing, Randomized Controlled Trials)
* Applications in Business Decision Making

=== 7. Recommender Systems ===

* Types of Recommender Systems (Collaborative Filtering, Content-Based)
* Building a Simple Recommender System
* Challenges and Applications in Business

=== 8. Model Evaluation and Selection ===

* Importance of Model Evaluation
* Cross-Validation
* Evaluation Metrics (Accuracy, Precision, Recall)
* Overfitting vs. Underfitting

=== 9. Model Tuning ===

* Hyperparameter Tuning
* Grid Search vs. Random Search
* Ensemble Methods (Bagging, Boosting)

=== 10. Data Leakage ===

* What is Data Leakage?
* Identifying and Preventing Leakage
* Impact on Model Performance

=== 11. Data Science in Business ===

* Data-Driven Decision Making
* Predictive Analytics: Churn, Demand Forecasting
* Managing Data Science Projects

=== 12. Conclusion ===

* Integrating Data Science in Business
* Future Trends in Data Science
* Sustainable Growth Through Data Science