Baby steps for Machine learning

1. You can’t be a master without basics. Also you need to have the core skills in those domains.
   • To use machine learning first know how to program. The Google Class for Python
   • Learn and Understand statistics, especially Bayesian probability, which is essential for many machine learning algorithms. Blog for free books on data science / statistics.
   • Also learn Scipy, numpy, pandas from youtube
   
   
2. After basics, it’s time to take some free courses from best universities
   • Stanford’s Machine Learning Course, this is the famous course by Andrew Ng
   • Harvard’s Data Science Course, this is End-to-end data science course. Get practice with the entire data science workflow from data collection to analysis
   • Read An Introduction to Statistical Learning and Elements of Statistical Learning
   
   
3. Practice the entire machine learning workflow: Data collection, cleaning, and preprocessing. Model building, tuning, and evaluation using real data sets. Pick 5-10 datasets from the UCI Machine Learning Repository. For example, you can pick 3 datasets each for regression, classification, and clustering.
4. Now go and participate in machine learning competitions. 

Infographicto become A Data Scientist

A good infographic clearly depicts simple steps to become A Data Scientist



via.:blog.datacamp.com

Crowded : Big Data Landscape 2016

Big Data Landscape 2016

source: Big Data Landscape 2016

Tools for Social Network Analysis

Here are the list of open-source tools for analyzing social networks

Gephi (http://gephi.org/). Visualization and basic network metrics.
NetLogo (modeling network dynamics)
iGraph (for programming)

Pajek (http://pajek.imfm.si/doku.php). Very extensive functionality via drop-down menus. Open-Source. Works only on windows.

NodeXL (http://nodexl.codeplex.com/). SNA integrated into Excel. Windows-only. Free. In beta.

NetworkX (http://networkx.lanl.gov/). Extensive functionality. Open Source. Scales to large networks by taking advantage of existing C, Fortran libs.

SNA Package for R (http://cran.r-project.org/web/packages/sna/index.html). Extensive, statistics-heavy functionality

Social Network Image Animator (http://www.stanford.edu/group/sonia/)

Books: 
Exploratory Social Network Analysis with Pajek (Structural Analysis in the Social Sciences)
Social Network Analysis: History, Theory and Methodology
Understanding Social Networks: Theories, Concepts, and Findings