Data Science

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Data Science is a branch of formal science that encompasses the entire set of techniques for collecting, processing, storing, analyzing, and presenting data. This category covers introductory-level topics in basic statistics and visualization, typically taught in the first or second year of undergraduate studies.

Introduction to Statistics

• Definition of Statistics
• Parameters and Statistics
  • What are Hyperparameters?
• What is Degrees of Freedom in Statistics?

Data

• Definition and Origin of Data
  • Scales of Measurement
  • What is Dimensionality Reduction?
• Qualitative and Quantitative Variables
  • Frequency of Qualitative Data
  • Class Intervals of Quantitative Data
• Independent and Dependent Variables
  • Design Matrix $X$

Central Tendency and Dispersion

• Mean $\overline{x}$
• Median $m$
• Mode
• Variance $s^{2}$
• Z-score and Standardization
• Normalization
• Percentiles and Outliers

Evaluation

• Definition of Classification and Regression Problems
• Cross-validation

Regression Problems

• Fitted Values, Predicted Values, Residuals, and Errors
• 🔒(25/01/13) Mean Absolute Percentage Error MAPE
• 🔒(25/01/17) Mean Arctangent Absolute Percentage Error MAAPE

Classification Problems

• Confusion Matrix, Sensitivity, and Specificity
  • Situations Where Accuracy is Overestimated
• ROC Curve
  • How to Find the Optimal Cutoff Using the ROC Curve
  • How to Compare Models Using the AUC of the ROC Curve
• $F_{1}$ Score
  • Precision
  • Recall

Visualization

• Bar Charts for Qualitative Data
• Histograms for Quantitative Data
• Line Graphs for Time Series Data
• Dot Plots for Multivariate Data
• What is a Heatmap?
• What is a Dendrogram?
• What is a Box Plot?

References

• Mendenhall. (2012). Introduction to Probability and Statistics (13th Edition)
• 경북대학교 통계학과. (2008). 엑셀을 이용한 통계학