데이터과학기초 강의예제코드

경북대학교 IT대학 컴퓨터학부 데이터과학기초(ITEC419-001, 2018~) 강의예제코드

Requirements

• Python 3.x
• Library: pandas(0.23.0), matplotlib(2.2.2), seaborn(0.8.1), sci-kit learn(0.19.1)

Table of Contents

Week 01

• Explorer Iris Dataset

Week 02 Pre-processing

• Standardization or mean removal or variance scaling (Z-score)
• Min-Max Normalization
• Normalized vector or Unit vector
• Binarization
• One-hot-encoder
• Missing value imputation
• Image Pre-processing
• Read CSV file in order to make data feame(Pandas) AND Change column name

Week 03 Visualization

• Line Chart
• Pie Chart
• Bar Chart
• Histogram
• Representation Values
• Box Plot
• Violin Plot
• Scatter Plot
• Scatter Plot Matrix
• Heatmap

Week 04 Linear Algebra

• Vector Operation
• Vector Norm & Inner Product
• Matrix Operation
• Various Square Matrix
• Matrix Translation & Eigen Analysis

Week 05 Statistics

• Interval Estimation

Week 06 Feature Extraction

• PCA (Principal Component Analysis)
• LDA (Linear Discriminant Analysis)

Week 07 Bayesian Classifier

• Bayesian Classifer for Gaussian Distribution

Week 08 K-Nearest Neighbor Classifier

• K-Nearest Neighbor Classifier using hand-made version and scikit-learn library

Week 09 Clustering

• K-means clustering (hand-made version)

Week 10 Regression

• Simple Linear Regression
• Simple Linear Regression using scikit-learn library

Week 11 Neural Network

• Multi Layer Perceptron

Week 12 Deep Learning

• MNIST Example using Keras with TensorFlow

  ※ Requirements: Keras(2.2.2)

Homework

• Feature Extraction and Classification for COIL20 Dataset