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Practical Data Science Using R



Workshop Start Date: 19-10-2019

Workshop End Date: 24-11-2019

Venue: Alpine (P.G.) college of education (Online Session)

Registration Ends: 10-10-2019

Fee: Rs.999

Duration: 30 hours Weekend Program


Pre-requisite:

This session is for the Alpine (P.G.) college of education

Software: R programming (R Studio). The team will assist in all the installations.

 

Total Duration: 30+ hours

Course Schedule: The second weekend of October and November 2019 

3 hours on Saturday and Sunday each (total 5 weekends)

 

Additional Q&A session based on demands

 

Mode: Live Online session

Projects: One real-time project based on R, It Will be mentored by faculty.

 

Certification: Post-workshop completion and project submission, Candidates will be awarded a certificate

 

R Programming

Data structure

Vector: numeric, character, logical

Factor, Matrix, Data frame, List

Hands-on practice

 

Operation

Mathematical, Statistical, Logical

Relational, Conditional, ifelse

String Operation, Date operation, etc…

Hands-on practice

 

Exploratory Data Analysis

External data import & export

Data Summary, Transformation

A subset, Rename, Reshape, Sort, Merge, Append

Tabulation, Aggregation,

Handling missing values, etc…

Hands-on practice

 

Data Munging with ‘dplyr’

All kind of data manipulation with ‘dplyr’

Hands-on practice

 

Loop & User Defined Function

Control flow: if-else-if

for loop, while loop, next, break

User-defined function to build an algorithm

Hands-on practice

 

R Graphics & Statistics

Data Visualisation

Generating graphs/plots in R

Graphical parameters

Line, Bar, Pie, Histogram, Density plots

Saving/Exporting R plots

dev.off()

Hands-on practice

 

Advanced graphics with ‘ggplot2’

Advanced and fancy charting

Colour and theme manipulation

Hands-on practice

 

Statistical Science (Descriptive)

Central tendency: Mean, Median

Dispersion: variance, std.deviation

Quartiles, IQR, covariance, correlation

Different Distribution Analysis

Box-plot, Outlier treatment

Hands-on practice

 

Machine Learning & Modelling

Supervised Learning Models

Hands-on case study for the below algorithm

 

Multivariate Linear Regression

Univariate analysis & Variable Selection

Model Assumptions & Diagnostic Checks

 

Logistic Regression for Classification

Logit function, odds ratio,

Model Estimation, Confusion Matrix

Accuracy, Sensitivity, Specificity

ROC curve, AUC, Gini-Coefficient

 

Unsupervised Learning Models

Cluster Analysis

Anomaly Detection, Image Classification