# Social Network Analysis – Visualization and Applications

Introduction to Gephi;  installing and exploring

Modularity and desired number of communities

Filtering Networks

Communication neworks

Predicting epidemic using social networks

SNA and Fraud Detection

Using R in SNA (In this particular example it is network analysis applied in documents), using package iGraph

http://www.r-bloggers.com/an-example-of-social-network-analysis-with-r-using-package-igraph/

The free ebook with all codes are available here: http://www.rdatamining.com/books/rdm/code

You want weekly class notes, here is Univ. of Maine lecture notes.

http://www.umasocialmedia.com/socialnetworks/course-lectures-fall-2015/

Stanford University 9 lectures on SNA

http://sna.stanford.edu/lab.php?l=1

http://sna.stanford.edu/lab.php?l=2

http://sna.stanford.edu/lab.php?l=3

http://sna.stanford.edu/lab.php?l=4

http://sna.stanford.edu/lab.php?l=5

http://sna.stanford.edu/lab.php?l=6

http://sna.stanford.edu/lab.php?l=7

http://sna.stanford.edu/lab.php?l=8

http://sna.stanford.edu/lab.php?l=9

# Some Top Techniques and Tools for a Data Scientist

1. How to find the optimum parameter values for a curve fitting problem. Stochastic Gradient Descent Method for Finding Local Optimum.

This is a commonly used numerical optimization technique. Some times, this is also called batch gradient descent algorithm.

2. How to avoid over-fitting problem? Use Regularization.

3. Sparse Matrix Based Prediction – GLMNET in R

4. Naive Bayes Modeling when there are many many conditioning variables

5. Out of Necessity, Real Time Application of Naive Bayes Application – Spam Deduction

6. Fisher LDA and Bayesian Classification

7.Lagrange Multipliers – A Simple Intro

8. Density Based Spacial Clutering Algorithm With Noise – DBSCAN

9. Meanshift Clustering with Scikit-learn and Python