Basic Data Science in R/Python


1. Data-Science-1-Air Quality & Data-Science-1-Facebook Metrics 

This module contains the basic data science approaches to deal with the data.
It contains the steps for-
  -Creating subsets of dataframe
  -Merging datasets
  -Sorting the dataframe based on a column
  -Transpose of a dataframe
  -Melting the dataframe (wide to long)
  -Casting of dataframe (long to wide)


2. Data-Science-2-Breast-Cancer

This module contains the further data science steps listed below-
  -Data cleaning(Remove NA, ?, Negative values)
  -Error Correcting(Outlier detection and removal)
  -Data transformation
  -Build data models using regression and naive bayes classifier

 
3. Text-mining-R-1 & Text-mining-R-2

This module contains the text mining approcahes with R.
It contains the following implementation-
  -Text mining operations
  -Calculating the tf count(text frequency count)
  -Generating a word cloud


4. Visualizations-in-Python-1 & Visualizations-in-Python-2
This module explores the data visualization option in Python with matplotlib and seaborn
Following visualizations are implemented-
  -Histograms
  -Dot Plots
  -Bar Plots
  -Line Charts
  -Pie Charts
  -Box Plots
  -Scatter Plots
  -Point Plot

Note: All Python notebooks are created on Python3 environment. 
    : It is better to install anaconda for Python3 and run these codes.