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## Available courses

Anaconda, Python, Spyder, Jupyter, Git, Git Hub

Analytical Levels, CRISP Modeling, Predictive Analytics, Business Intelligence, Visualisation, Data Modeling, Analysis vs Analytics

Libraries, Data Structures, Numpy, Pandas, Matplotlib, Seaborn, Plotnine, sklearn, statsmodels etc

Linear Regression, Logistic Regression, Decision Trees, Random Forests, Clustering, Association Rule Analysis, Time Series Analysis, Text Mining, StoryTelling with Data

- Teacher: Tanvi Tiwari
- Teacher: Dhiraj Upadhyaya

- Analytics, Data Management, Python Programming, Data Structures, Data Manipulation and Summarisation, Missing Values and Outliers,
- Analytical Modeling, Statistical Tests,
- Visualisation
- Linear Regression
- Logistic Regression
- Clustering
- Decision Trees, Random Forests
- Association Rule Analysis

- 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: Dhiraj Upadhyaya

Objective : To enable senior management to understand the full life cycle of Industry 4.0 and make better decisions.

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