LMS with LA + GBL + PM

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R Programming

CSE437 : R Programming

Module-1

Introduction about objective, SLO, Course delivery methods, class discipline, Discussion about class test, assignment etc. Introduction to R Programming.
R-Data Types , Types of variables and using Variables
R-Objects, Attributes, Lists, Vectors:Vecorized Operations , Power of Vectorized operations.
R-Matrices: Buliding matrix, Matrix Operations and subsetting
R-Factors, R-Data Frames:Building data Frame, Basic Operations with data Frame,
Filtering Data Frame , Merging data Frame, Using the []and $ sign
Importing and Exploring Data
Handling Missing Values, Names Attributes
Library and Packages in R and their Installation
Write commands in R to demonstrate the use of different data types and Variables in R
To store data in R using Vector and Perform various operations using vectors
To store data in R using Matrix and Perform various operations using Matrices
To store data in R using DataFrame and Perform various operations using DataFrame

Module-2

R: Operators and Expressions
R Control Structure: Various Constructs of If-Else Statement
R Control Structure: While Loop and For Loop
R: Using The Console, Dates and Times
Functions in R and Scoping Rules of Functions
Write a Program to Demonstrate Operators and Expressions
Write a Program to Demonstrate various constructs of If-ElseStatement
Write a Program to DemonstrateWhile loop and For Loop
Write a Program to Demonstrate Functions in R and its scoping Rule

Module-3

Object Oriented Programming in R: Creating and Understanding Objects and Classes In R
Object Oriented Programming in R: Creating and Understanding objects and Classes in R
Generating Random Numbers , Random sampling and Using Sample() Function
Simulating a Linear Model to learn how to simulate data in R
Understanding Profilers and using them to collect detailed information on R Functions
Understanding Profilers and using them for the optimization of Programs
The Str () Function In R
Write a Program to Demonstrate Classes and Objects in R
Write a Program to Demonstrate Generating Random Numbers
Write a Program to simulate Linear Model
Write a Program to Demonstrate the application of Profiler
Write a Program to Demonstrate the usage of Str() Function
Open ended Practical program, Assignment

Module-4

Data Preprocessing in R :Steps in Data Preprocessing , importing and exploring the data
How to Deal with Missing Values
How to Deal with Categorical Values
Splitting the Data into Training and Testing Data
Feature Scaling in R
Introduction and Visualization with Matplot(), qplot()
Advanced Visualization with ggplot()
Grammar of Graphics and Factors in R
Histograms and Density Charts in R
Statistical Transformation in ggplot()
Write a Program to Demonstrate data Preprocessing In R
Write a Program to Demonstrate Visualization with Matplot() and qplot()
Write a Program to Demonstrate Visualization with ggplot()

Module-5

Working with Data : Analyzing data
probability distributions in R
Statistical tests: t-test, ANOVA
Chi-Square Test and Linear Regression in R
Case Studies: Multiple Linear Regression
Introduction to Classification: KNN Classifier
Association Rule Mining in R
Introduction to Clustering: K-Means Clustering
Write a Program to Demonstrate Probability Distributions in R
Write a Program to Demonstrate develop hypothesis and Perform Statistical Analysis
Write a Program to Demonstrate K-means Clusterig
Write a Program to Demonstrate KNN Classifier
Write a Program to Demonstrate Association rule Mining In R

Others

Open Ended Evaluation
Evaluation
Evaluation
Evaluation

Teacher: Anchal Garg

How to use Moodle LMS

This course is for Faculties who would act as Teachers in LMS Course. This will content for creating, managing and monitoring activities which their participants will use.

You will learn how to configure Moodle activities to reduce repetitive administration tasks such as checking whether work has been completed by all the learners, ensuring that the required documents have been received, or meeting audit reporting requirements. The challenge is to use computers to do the repetitive tasks that eat up your time and leave the creative rewarding aspects of teaching to you. You don't have to automate everything, so you are not expected to know how to use every activity in Moodle, rather you will start with what you find comfortable and effective in your context. Once you are convinced that this has really saved your time and that the process has been enjoyable, then you will be challenged to add another activity to your repertoire. In fact, you only have to master three activities to get to the end of this book! You really don't have to learn it all before you jump in and try. You wouldn't expect this from your learners and we don't expect this from you. Trust your instincts! You will know this works when you feel the change in the vibe of the room, or in the after-hours voluntary activities, or the increase in peer-mentoring, and more joy for both you and your learners.

The Moodle activities you create will automatically create data on learner participation and competence to assist you in identifying struggling learners and plan appropriate intervention/ scaffolding. Activities for advanced learners can be revealed according to the criteria you set. The in-built reports available in Moodle LMS not only help you to get to know your learners faster, but also create evidence for formative assessment.

Teacher: Dhiraj Upadhyaya

Analytics using Python Programming

Teacher: Dhiraj Upadhyaya

Analytics using R Programming

Introductory Topics to Analytics

Understanding the need for Analytics in Specific Domain; CRISP Modeling
Introduction to Data Management
Properties & Types of Data, Measurement Scale, Basic Statistics on Data
Types of Analytics - Business, Marketing, Finance, etc

Basics of R Programming

Need for R, Features of R, Download, Setup, Installation ; R & R Studio; Configuration. eg. Learning to setup R and share Code
Data Structures in R
Creating and Understanding Basic Data Structures in R - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in R programming
Data Manipulation & Summarisation in R
Understanding how data can be summarised in different ways to do Descriptive Analysis which describes features of data
Import Export Data - CSV, Table, Excel, Google Sheet, Other Formats

Module 2 : Analytical Modeling

Understand what is Modeling and how it can be used in Various Domain
Statistical Tests -P Value, Z Value, Hypothesis, Null Hypothesis and Alternative Hypothesis, F Test, ANOVA Introduction)
Linear Regression (Using R) Start of Machine Learning, Develop a Prediction Model for predicting a financial values based on one or more than 1 Independent Variable; Understand the assumptions and measures of goodness of Model, Understand its prediction ability
Visualisation using Graphs. Creating Graph in R and understanding which graph to be used when
Missing Value and Outlier Analysis
Understanding how missing values & outliers are handled in data summarisation & modeling
Logistic Regression . Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables eg. Predicting Cases for Fraud, Default on Payment etc

Module 3

Clustering. Grouping Customers based on characteristics so that they can be target for sale increase
Decision Trees. When to use CART & CHAID to create decision tree based on categorical variables.
Ensemble (Bagging & Boosting)
Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest, How group thinking impacts the decisions (from business point of view)

Module 4

Association Rule Analysis. Understand how Association Rules can be created using Market Basket Analysis. Finding Interesting association between items purchased by Customers and building strately to sell more
Twitter Analysis. Configure Twitter Account & Application; Setup for downloading tweets and analyse them for positive and negative sentiments related to Financial News/ Articles

Code and Data Sharing through Git Hub, Git Configuration, Monthly Brushup Sessions