LMS with LA + GBL + PM

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

• 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

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)