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Course Name: Multivariate Data Analysis 
Participants: BSc Mathematics and Data Science students of Sorbonne University
Faculty Name : Dr. Tanujit Chakraborty
Timeline : January 17, 2022 to April 28, 2022   |  ​Total Teaching Hours : 90 Hours (45 Sessions)
Email: tanujit.chakraborty@sorbonne.ae​

Course Introduction:

Data driven decision making is the state-of-the-art of decision making today. As the data collected and stored are multidimensional, to extract knowledge out of it requires statistical analysis in the multivariate domain. The aim of this course is therefore to build confidence in the students in analyzing and interpreting multivariate data.

Course Objectives: ​

The course will help the students by:
​
(i) Providing guidelines to identify and describe real life problems so that relevant data can be collected,
(ii) Linking data generation process with statistical distributions, especially in the multivariate domain,
(iii) Linking the relationship among the variables (of a process or system) with multivariate statistical models,
(iv) Providing step by step procedure for estimating parameters of a model developed,
(v) Analyzing errors along with computing overall fit of the models,
(vi) Interpreting model results in real life problem solving and providing procedures for model validation.
(vii) Hands on experience on the usage of open source software like R and Python.

Evaluation Components:

 The evaluation components for the Multivariate Data Analytics (MDA) course will be as follows: 
1) Homework Assignments - 10% ;  2) Mid Term Test - 20% ;  3) Project Work - 20% ;  4) End Term Test - 50%.

Textbooks: ​

• Friedman J, Hastie T, Tibshirani R. (2009). The Elements of Statistical Learning. New York: Springer series in statistics. (Read the Free Online Copy from here (Second Edition): https://web.stanford.edu/~hastie/ElemStatLearn/  
• Rencher, A.C. and Christensen, W.F. (2012). Methods of Multivariate Analysis. 3rd Edition. An Introduction to Stochastic Modeling. Wiley. 
• Bishop, C.M. (2006). Pattern Recognition and Machine Learning. Information Science and Statistics Series. Springer, New York. 

Some Very Interesting Papers For Reading :

I would recommend all the participants to go through these research articles (mostly non-mathematical) along with the course. Please click on the paper name to view these outstanding and interesting paper: 
​
1. Statistics - What are the most important statistical ideas of the past 50 years? (2021)
2. Data Science - 50 Years of Data Science​ (2017)
3. Statistics Vs Data Science - The science of statistics versus data science: What is the future? (2021)
4. Statistics Vs Machine Learning - Prediction, Estimation, and Attribution (2020)
5. Future - The future of statistics and data science (2018)
Lecture Notes:

This is a 13 weeks course offered at SUAD. All the data analysis were done using R software. Class notes, Slides, data and code are available below. ​
MDA_Course Syllabus and Preface.pdf
File Size: 420 kb
File Type: pdf
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Week 1 : Recap
​Topic: Introduction to Multivariate Data Analysis
MDA-Session-1_Introduction.pdf
File Size: 19357 kb
File Type: pdf
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Week 1 : Recap
​Topic: Descriptive Statistics
MDA-Session-2_Descriptive_Statistics.pdf
File Size: 8917 kb
File Type: pdf
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Data Sampling Tools (Optional - Recap).pdf
File Size: 436 kb
File Type: pdf
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Week 2 : Recap
​Topic: Probability Distributions and Sampling Distributions
Probability Theory (Optional - Recap).pdf
File Size: 1618 kb
File Type: pdf
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MDA-Session-3_Distribution_Theory.pdf
File Size: 5286 kb
File Type: pdf
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Understanding_Statistical_Tables.pdf
File Size: 9531 kb
File Type: pdf
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Week 2 : Recap
​Topic: Basics of R and RStudio
MDA-Session-4_RStudio.pdf
File Size: 4628 kb
File Type: pdf
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MDA-Session-4_Data_Code.zip
File Size: 201 kb
File Type: zip
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Week 3 : Recap
​Topic: Statistical Inference
MDA-Session-5_Statistical_Inference.pdf
File Size: 8359 kb
File Type: pdf
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Week 3 : Recap
​
​Topic: Analysis of Variance (ANOVA)
MDA-Session-6_ANOVA.pdf
File Size: 5720 kb
File Type: pdf
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MDA-Session-5-6_Data_Code.zip
File Size: 1 kb
File Type: zip
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Week 4 :
​Topic: Correlation Analysis and Simple Linear Regression
MDA-Session-7_Simple_Linear_Regression.pdf
File Size: 4508 kb
File Type: pdf
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MDA_Session_7_SLR.pdf
File Size: 6431 kb
File Type: pdf
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MDA-Session-7_Data_Code.zip
File Size: 2 kb
File Type: zip
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Week 5, 6 and 7 : 
​Topic: Multiple Linear Regression
MDA-Session-8_Multiple_Linear_Regression.pdf
File Size: 5484 kb
File Type: pdf
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MDA_Session_8_MLR.pdf
File Size: 4012 kb
File Type: pdf
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MDA_Session_8_Model Selection.pdf
File Size: 4846 kb
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MDA_Session_8_Multicollinearity.pdf
File Size: 3346 kb
File Type: pdf
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MDA_Session_8_Shrinkage_Methods.pdf
File Size: 3815 kb
File Type: pdf
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MDA_Session_8_Model_Adequacy_Checking.pdf
File Size: 8640 kb
File Type: pdf
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MDA-Session-8_Data_Code.zip
File Size: 4 kb
File Type: zip
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Week 8 and 9: 
​Topic: Nonlinear Regression, Logistic Regression, and GLM
MDA-Session-9_Nonlinear_and_Logistic_Regressions.pdf
File Size: 3961 kb
File Type: pdf
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MDA_Session_9_Nonlinear_Regression.pdf
File Size: 6033 kb
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MDA_Session_9_Generalized_Linear_Model.pdf
File Size: 5880 kb
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MDA_Session_9_Dummy_Variables_Logistic_Regression.pdf
File Size: 4552 kb
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MDA-Session-9_Data_Code.zip
File Size: 5 kb
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Week 9 : 
​Topic: Time Series Forecasting
MDA-Session-10_Time_Series_Forecasting.pdf
File Size: 4980 kb
File Type: pdf
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MDA-Session-10_Data_Code.zip
File Size: 3 kb
File Type: zip
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Week 10 and 11 : 
​Topic: Dimension Reduction Techniques
MDA-Session_11_Dimenionality_Reduction.pdf
File Size: 1675 kb
File Type: pdf
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MDA-Session-11_Dimensionality_Reduction_Using_R.pdf
File Size: 1471 kb
File Type: pdf
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MDA-Session-11_Data_Code.zip
File Size: 2 kb
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Week 12 : 
​Topic: Tutorial Problems and Solutions
MDA_Session_12_Tutorials.pdf
File Size: 8676 kb
File Type: pdf
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Week 13 : 
​Topic: Special Talk Sessions 
​           
SVD_Image_Processing.pdf
File Size: 5230 kb
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SVD_Applications_Data_Code.zip
File Size: 8 kb
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MDA_Biomedical_Data_Analysis_Stanford_Medicine.pdf
File Size: 9929 kb
File Type: pdf
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Fooled_by_Statistics.pdf
File Size: 11031 kb
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Contact Me

Dr. Tanujit Chakraborty 
Sorbonne University and Sorbonne Center for Artificial Intelligence.  
Centre for Data Sciences, IIIT Bangalore, India.
​​Email :  tanujitisi@gmail.com​ 

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Code Repository at GitHub
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​​© 2013 - 2023 All rights reserved to Tanujit Chakraborty.​​

  • HOME
  • BIOGRAPHY
  • RESEARCH
    • Research interests
    • PUBLICATIONS
  • TEACHING
    • DATA ANALYTICS (MBA)
    • ANALYSIS (UG LEVEL)
    • MULTIVARIATE DATA ANALYTICS (MDA)
  • TALKS
  • TUTORIALS
    • LECTURE NOTES
    • BOOKS I WROTE
    • VIDEO LETURES
  • STAT & ML LAB
    • STAT & ML LAB
    • APPLY HERE
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    • WORKSHOP ON ML
  • ETC.