Data Analytics

Data analytic is the generalize term for qualitative and quantitative techniques and processes used to enhance productivity and profit. The target is to understand and analyze the pattern and behavior of data with the extraction of data. The techniques also vary according to organizational requirements. It is estimated that by 2018 there will nearly 1.2 million open data jobs , it is clear that understanding and communicating with data will be critical for impact. The skills needed to be a Business Analyst, Data Analyst, or Data Scientist are all about to be in real demand,and here with Skillthon techies will get a head start!

Prerequisites:-

Can access computer system and make use of internet to perform search over Google.

Need to Prepare?

Knowledge of Matrix will be appreciated.Prior programming experience is not required.

Tools Expected:-

Windows OS based PC, Smart phone with Internet, Notebook and Pen

Tools Provided (for the session):-

R Development tool

Concepts:-

R Programing, Data sets, Data examining Linear and Logistics Regression

Summary:-

This workshop is all about examining a data set to reach out a conclusions about the information it contain with the aid of specialized system and software. It’s the way of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

Project:-

Patanjali product analysis, Snapdeal analysis

 

Commitment:-

2 Days (7 hours each including 1-hour lunch break)

Agenda:-

Day 1

Session 1-  (03:30 hrs)

Overview

  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation

Introduction

  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Saving your work

Installing Packages

  • Finding resources
  • Installing resources

Data Structures, Variables

  • Variables and assignment
  • Data types
  • Indexing, subsetting
  • Viewing data and summaries
  • Naming conventions
  • Objects

 

Session 2- (02:30 hrs)

Getting Data into the R Environment

  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC

Control Flow

  • Truth testing
  • Branching
  • Looping
  • Vectorized calculations

Functions in Depth

  • Parameters
  • Return values
  • Variable scope
  • Exception handling
Session Recap
 
Day 2
Session 1- (03:30 hrs)

Handling Dates in R

  • Date and date-time classes in R
  • Formatting dates for modeling

Descriptive Statistics

  • Continuous data
  • Categorical data

Inferential Statistics

  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test
  • Distribution testing
  • Power testing

Group By Calculations

  • Split apply combine strategy

Base Graphics

  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, Titles, Axes
  • Exporting graphics to different formats

 

 

Session 2- (02:30 hrs)

Advanced R Graphics: GGPlot2

  • Understanding the grammar of graphics
  • Quick plot function
  • Building graphics by pieces

Linear Regression

  • Linear models
  • Regression plots
  • Confounding / Interaction in regression
  • Scoring new data from models (prediction) 
Session Recap
 
Zonal Round of SkillThon
  • Competition
  • Certificate distribution and acknowledgement
 

Charges:

INR 1200 (GST exclusive) Per Participation