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DSCE Business Analytics Conclave Workshop

19th, 20th & 21st April, 2pm - 5pm

If you run into issues while installing the softwares required for the workshop, please reach out to us @ [email protected] or 7259970037.

Introduction

Materials for the workshop. Please install all the required softwares and download the data sets and scripts from this repository before the workshop. The final version of the materials will be available by 6 PM Wednesday (18/04/2018). Please ensure that you download them before coming to the workshop.

Software Prerequisites

R

R can be installed from the R project website. Please install the latest version 3.4.4.

Download and install Rtools from here.

RStudio

RStudio IDE can be installed from the RStudio website. Install the free desktop version.

R Packages

Below are a list of R packages required for the workshop:

First Day

  • readr
  • readxl
  • haven
  • dplyr
  • ggplot2
  • magrittr
  • descriptr
  • skimr

Second Day

  • inferr
  • olsrr
  • blorr
  • rmarkdown
  • knitr
  • devtools
  • forcats
  • caret

Once you download the workshop materials, run the script (package_download.R) in the R folder to download the above packages as shown in the below video:

To test if a package has been correctly installed or not, type library(packagename) in the RStudio console. For example, to test if readr package has been installed, type library(readr) in the RStudio console. If the package has not been installed correctly, you will get the following error message:

Error in library(readr) : there is no package called ‘readr’

There will be no error message if the package has been correctly installed.

Anaconda

Download and install the Anaconda distribution from their website. We will use Jupyter Notebook for the python session.

After installing the Anaconda distribution for Python, open the Command Prompt (type cmd) or Windows Powershell (type powershell) and type jupyter notebook.

R Session

  • Quick tour of R & RStudio
  • Importing data into R
  • Data Wrangling
  • Data Visualization
  • Exploratory Data Analysis
  • Linear Regression
  • Logistic Regression

Python Session

  • Data Wrangling with Pandas
  • Data Visualization
    • matplotlib
    • bokeh
    • plotly
  • Modeling with statsmodels

Useful Resources

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