[Spring, 2017] CNU Data Science Class

Development tools : Python3, RStudio, Jupyter Notebook

Contents

Week1

Summary of Python & R for Data Science

1. Introduction to Python for Data Science
2. Introduction to R

Week2

Tashu Data Visualization with Python

1. Top 10 station
2. Top 10 trace
3. Number of stations per district
4. Number of usages per district
5. Day of a week transaction
6. Usage per hour
7. Day-hour usage pattern

Week3

Tashu Data Visualization with R

1. Top 10 station bar graph
2. Station usage scatter plot in ggmap
3. Top 20 trace scatter plot
4. Top 20 trace chord diagram

Week4

Fitbit Data with Python & R

1. Two weeks step per day with python
2. Four weeks step, sleep, heart rate(Fat burn) heatmap
3. One week step per 15min heatmap
4. Four week step and heartrate(fat burn) treemap

Week5

2016 Sokulee Contest Participant's Fitbit Data Analysis with Python

1. Top 10 steps during contest
2. Top 10 number of taking 9 A.M class student during contest

Week6

Predicting Rate of movies using MovieLens data with Python

1. Get nearest user using euclidean distance
2. Predicting rate of movies
3. Calculate accuray with RMSE, MAE

Week7

Visualizing word cloud with Python & R

1. Crawling data with Python
2. Visualizing word cloud with R

Week8

Hadoop Setting on Virtual machine

Configuration about xml file

1. core-site.xml
2. mapred-site.xml
3. hdfs-site.xml
4. yarn-site.xml

Week9

Hadoop wordcount

1. Default
2. Filtering patterns
3. Case sensitive

Week10

Hive Setting and Tashu data analysis

1. Hive setting
2. Year / Month / Day / Hour usage count
3. Top 10 rent station
4. Top 10 trace

Summary

1. Crawling and Wordcloud
2. Fitbit data analysis using Spark
3. Movie lens data analysis using Spark