Bayesian for Researching & Data Science

The use of P values for conventional significance testing has proven to be misused and misunderstood. Bayesian methods are emerging as the primary alternative to the conventional frequentist approach to statistical inference. Bayes' theorem is a model for learning and making decisions based on empirical data. Bayesian approaches have many empirical implications and are used widely for machine learning. 

M&S Research Hub provides full and comprehensive structured training for researchers, programmers, and data scientists at all proficiency levels to acquire detailed knowledge and become fully capable of using these models in their research and programming projects.

Learning by "Researching" TM

Training Calendar

March  / - Normal Group (7-10 participants)                                           

May / - Normal Group (7-10 participants)                         4 seats left                    

July / - Normal Group (7-10 participants)                         7 seats left                    

October  / - Normal Group (7-10 participants)              7 seats left                         




It was excellent training for me. I think Dr. Arhsian is the best in his field. He is a well-qualified person. I will recommend this institute for all my colleagues if they need it

Ferhat Citak 

Source - Home Page


A good platform to learn data analysis.

Mahdi Hassan

Source - Facebook Page


After finishing the first module, I decided to to do all modules because the first one has opened my mind, because here in Zambia there is no university or college where they offer as full program they way you offer it, it only comes as a course to those who are doing economics.


Source - Home Page

Live Training


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Video 1: M&S Research Hub | Bayesian for Research& Data Science using Python | Part of Session 1| Probabilities  (13 Minutes)

Video 2: M&S Research Hub | Bayesian for Research& Data Science using Python | Bayesian inference using Python  (11 Minutes)

Bayesian Training Program

A fully-fledged intensive training on the fundamentals of the Bayesian approach for econometric modeling and data analysis using Python programming package.

Duration: Approx. 18 Hours

Training Mode: Normal group (7-10 trainees), small group (2-5 trainees), and one-to-one

Platform: online (Zoom)

Extra benefits:

  • Certified certificate and 10% permanent discount on all events, workshops, and webinars.
  • Trainees become eligible to submit their papers at MSR working paper series (SSRN and RePEc indexed).

Training content is systematic and structured as follows:

1 - Introduction and Setup

2- Probability Theory

3- Model Inference

4- Probalistical programming

5- Bayesian A/B-testing

6- Hierarchical Models

7- Simple Linear Regression

8- Hierarchical Linear Regression

9- Logistic Regression

10- Bayesian Neural Network


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