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Business Analytics

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Course type: Online Instructor led Course

Platform: Skype

Course ID: 43878

Course type: Online Instructor led Course

Platform: Skype

Students Interested 0 (Seats Left 0)

No Reviews Yet

About the Course

Course Covers :



1. Basic Statistics


- Data Types


-Central tendency and CLT


-Point Estimate / Interval Estimates


- Hypotheis Testing


- Basic Probability


- Bayes Therom


-Model Development Cycle



  • Data Preparation/Cleansing

  • Model Design and Development

  • Model Validation

  • Model Implementation

  • Model Documentation



2. Linear Regression


-Assumptions


-Theory (OLS)


-Modeling Demo (Using R or Python)


- Case Study


 


3. Logistic Regression


-Assumptions


-Theory (MLE)


-Modeling Demo (Using R or Python)


- Case Study



4. Decision trees


-Theory 


-Modeling Demo (Using R or Python)


- Case Study



5. K means


-Theory 


-Modeling Demo (Using R or Python)


- Case Study



6. K Nearest Neighbours


-Theory 


-Modeling Demo (Using R or Python)


- Case Study



7.Ensemble Methods ( Random Forest, Boosting & Bagging)


-Theory 


-Modeling Demo (Using R or Python)


- Case Study



Case studies will be done in either R or Python depending on the requirement.

Training will be conducted using Skype or other online platform.

Course content can be customized depending on the requirement. Domain Specific training can be conducted like

Banking (Scoring Models/Default models) 

Marketing (MBA/Market mix models/Rules Induction)

Natural Language Processing/ Text Mining

Neural Networks/ Deep learning.


Data sets and codes will be shared with all participants. Will be assisted in online materials, Books and CV preparation. In case if needed dummy projects will be provided for practice purposes. Doubts will be resolved as needed.

Date and Time

Not decided yet.

About the Trainer

Avg Rating

0 Reviews

0 Students

1 Courses

Venkat

Post Graduation from Indian Statistical Institute

Provides classes for Advanced Statistics, Predictive modeling, Machine learning, Deep learning.

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