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Data Analysis

This is a synopsis on my findings and activities on data analysis and data science.

• Here you can see some of my presentations.

Toolsets

R-Project

e.g. tidyverse, caret, MLmetrics

Python

Methods - Maths and Statistics

Methods and statistics that can be used for statistical learning and machine learning.

Empirical Data Analysis

• Statistical Classification

• Text Mining

Machine Learning

• Decision Trees

• Analysing Coherences

• Linear Regression

• Non parametric regression

Technology and Programming

• Programming with R

• Programming with Python

• The Julia Language

• Databases

Parallelisation

Parallelisation can be necessary due to four reasons:

• not enough memory size
• to much cpu time is needed
• I/O needs to much time
• transfer to network needs to much time

Simple Memory Profiling in R

Getting Data

• Twitter

• Wikipedia

• Openstreetmap