## Available courses

**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

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

**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**

- Teacher: Kanika Tiwari
- Teacher: Dhiraj Upadhyaya

Industry 4.0

- Module 1
- Industry 4.0 – Introduction
- What is Industry 4.0
- Industrial Revolutions and Future
- The digital transformation of industry and the fourth industrial revolution
- Principles of “Smart Factory”
- Industry 4.0 Key Principles
- Automation
- Industry 4.0 and the Industrial Internet
- Industry 4.0 strategy & implementation
- Industry 4.0 challenges & risks
- Leadership & technology in Industry 4.0
- Module 2
- Data Driven Decision Making
- Data the new oil
- Predictive Modeling
- Data Mining
- Supervised
- Unsupervised
- Data mining from Unstructured data – Text Analytics
- Principles of Deep Learning
- Forecasting – Principles and methods
- Disruptions in big data, analytic and business-intelligence capabilities
- Module 3
- Internet The New Master – IOT (Internet Of Things Industry 4.0)
- Key IoT technologies
- Augmented-reality systems
- Advanced robotics and 3-D printing
- Autonomous activities
- Lean Manufacturing
- Implementation Viewpoint
- Architectural Topology
- The Three-Tier Topology
- Connectivity
- Key System Characteristics
- Communication protocols
- IoT, IIoT (Industrial Internet of Things)

- Teacher: Dhiraj Upadhyaya

Data Handling and Statistical Analysis

Analytical Methods in Biotechnology/ Biophysical Techniques

- Teacher: Avadhesh Kumar Singh RMLAU
- Teacher: Brijendra Kumar Kashyap RMLAU
- Teacher: Dr. Shivali Singh RMLAU
- Teacher: Manisha Yadav