ACU-OER

Utilizing Big Data to Improve Job Performance

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- Category : Engineering > Data Analysis & Statistics
- Keyword : Big Data
- Taught by : Taesan BAEK
- Created By : Yeungjin Cyber University
- Offered By : Yeungjin Cyber University
- Date Added : 2022.11.04
This course aims to compare and analyze the selection of data mining techniques by using Google Colaboratory.
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Number of lessons: 42

차시별 강의
No	Contents Type & Play	Subject	Description	URL
1.	Type	Subject Orientation	Description	Url URL
2.	Type	Subject [Week 1-1] Concept of Big Data	Description · Concept of Big Data · Characteristics of Big Data	Url URL
3.	Type	Subject [Week 1-2] Big Data Collection	Description · Step-by-step element description of big data utilization process · Big data collection process	Url URL
4.	Type	Subject [Week 1-3] Big Data Storage Management	Description · Data pre/post-processing · Data storage processing · Data security management	Url URL
5.	Type	Subject [Week 2-1] Big Data Analysis	Description · Establishing big data analysis plan · Establishing big data analysis environment · Big data visualization	Url URL
6.	Type	Subject [Week 2-2] Understanding Data Analysis Techniques I	Description · Data analysis technique according to the purpose of analysis · Statistical analysis technique	Url URL
7.	Type	Subject [Week 2-2] Understanding Data Analysis Techniques II	Description · Data mining techniques · Machine Learning and Text Mining	Url URL
8.	Type	Subject [Week 3-1] Understanding Exploratory Modeling	Description · Concepts and characteristics of exploratory modeling · Exploratory modeling techniques and application examples	Url URL
9.	Type	Subject [Week 3-2] Understanding Predictive Modeling	Description · Concept and characteristics of predictive modeling · Predictive modeling techniques and application examples	Url URL
10.	Type	Subject [Week 3-3] Difference between Exploratory Modeling and Predictive Modeling	Description · Difference between exploratory modeling and predictive modeling · Data analysis types according to problem-solving goals	Url URL
11.	Type	Subject [Week 4-1] Getting Started with Google Colab	Description · Understanding Google Colaboratory · Checking Google Colaboratory UI and features provided	Url URL
12.	Type	Subject [Week 4-2] Google Colab Basic Direction for Use	Description · Understanding the basic direction for use and terminology of Google Colaboratory	Url URL
13.	Type	Subject [Week 4-3] Practice for using Google Colab	Description · Creating and verifying Google Colab notebook · Installing package and checking visualization · Checking IRIS dataset	Url URL
14.	Type	Subject [Week 5-1] Understanding Clustering	Description · Concept and characteristics of clustering · Clustering algorithm	Url URL
15.	Type	Subject [Week 5-2] Examples of Feature Analysis using Clustering	Description · Prediction of learning achievement by using cluster analysis	Url URL
16.	Type	Subject [Week 5-3] IRIS Data Analysis Using Clustering	Description · Data correlation of IRIS dataset through scatter plot · Clustering of IRIS data using K-means technique	Url URL
17.	Type	Subject [Week 6-1] Understanding Correlation Analysis	Description · Correlation analysis concept and characteristics · Various determination criteria for correlation analysis	Url URL
18.	Type	Subject [Week 6-2] Exapmle of Analyzing Shopping Cart through Correlation Analysis	Description · Case example of correlation analysis in analyzing shopping carts · Case example of correlation analysis in analyzing domestic tourist destinations	Url URL
19.	Type	Subject [Week 6-3] IRIS Data Analysis through Correlation Analysis	Description · Data processing for correlation analysis · Correlation analysis by using apriori · Visualization of correlation analysis	Url URL
20.	Type	Subject [Week 7-1] Understanding Decision Trees	Description · Concept of decision tree · Process of creating a deision tree	Url URL
21.	Type	Subject [Week 7-2] Example of Market Analysis Using Decision Trees	Description · Market analysis cases using decision trees	Url URL
22.	Type	Subject [Week 7-3] Classifying & Analyzing IRIS Data Using Decision Trees	Description · Dataset configuration for decision tree · Varieties prediction according to variables using ctree · Classification model creation and visualization · Validation using test data	Url URL
23.	Type	Subject [Week 8] Midpoint Review	Description · Review major concepts presented in Weeks 1 to 7	Url URL
24.	Type	Subject [Week 9-1] Understanding Classification	Description · Classification Ensemble · Classification Subsampling	Url URL
25.	Type	Subject [Week 9-2] Examples of Classification	Description · Classification cases using random forest	Url URL
26.	Type	Subject [Week 9-3] Classifying MNIST Data Using Classification	Description · Dataset configuration for classification · Creation of classification model using random forest and error measurement · Confirmation of successful and unsuccessful cases of classification using classification	Url URL
27.	Type	Subject [Week 10-1] Understanding Neural Networks	Description · Concept of neural network · Perceptron · Activation Function and Backpropagation	Url URL
28.	Type	Subject [Week 10-2] Problem Solving Examples Using a Neural Network	Description · Cases of image synthesis using neural network	Url URL
29.	Type	Subject [Week 10-3] Learning and Predicting MNIST Data Using a Neural Network	Description · Checking data for neural network use · Configuring dataset for neural network training · Creating neural network model · Measuring the accuracy of each dataset for neural network model	Url URL
30.	Type	Subject [Week 11-1] Understanding Dimensionality Reduction	Description · Dimensionality reduction · Principal component analysis	Url URL
31.	Type	Subject [Week 11-2] Understanding Data Visualization	Description · Purpose and necessity of data visualization · Various data visualization methods	Url URL
32.	Type	Subject [Week 11-3] Data Visualization Practice	Description · Relational visualization using scatterplots and histograms · Comparative visualization using boxplots · Time visualization using bar graphs	Url URL
33.	Type	Subject [Week 12-1] Understanding Prediction Errors	Description · Prediction error · Loss function	Url URL
34.	Type	Subject [Week 12-2] Understanding Cross-validation	Description · Cross-validation · Cross-validation method	Url URL
35.	Type	Subject [Week 12-3] Accuracy Analysis Practice Using Prediction Error and Cross-validation	Description · Pre-processing for prediction error measurement and cross-validation · Prediction error measurement · Cross-validation and verification result confirmation	Url URL
36.	Type	Subject [Week 13-1] Understanding Model Evaluation	Description · Model evaluation	Url URL
37.	Type	Subject [Week 13-2] Hyperparameter and Model Selection	Description · Hyperparameter · Model evaluation method and model selection	Url URL
38.	Type	Subject [Week 13-3] Model Cross-validation Practice	Description · Pre-processing for model cross-validation · Model cross-validation · Selection based on model cross-validation results	Url URL
39.	Type	Subject [Week 14-1] Creating a Word Cloud Using the English Vocabulary	Description · Pre-processing for word cloud generation · Word cloud generation for web document words	Url URL
40.	Type	Subject [Week 14-2] Korean Text Mining Using KoNLP I	Description · Hangeul text mining library installation · Pre-processing for Korean text mining	Url URL
41.	Type	Subject [Week 14-3] Korean Text Mining Using KoNLP II	Description · Pre-processing for text mining of Korean web documents · Text refinement for text mining · Performing text mining on Korean web documents	Url URL
42.	Type	Subject [Week 15] Final Check	Description · Review major concepts presented in Weeks 9 to 14	Url URL