Available courses

Analytics using Python Programming

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


HOW TO USE LMS - Dhiraj Upadhyaya

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.


Introduction to Industry 4.0

To enable senior management to understand the full lifecycle of Industry 4.0 and make better decisions.

Course Structure

Module No

Module

Sub Module

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)

R Programming

Data Handling and Statistical Analysis