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UNIT I: Skill sets required for a computer science researcher
UNIT IV: Important Resources for Machine Learning
UNIT V: Things to do for a Research Scholar
UNIT VI: How to Choose a Research Topic
UNIT VII: Gold Mine for Researcher
UNIT VIII: Research in Computational {Biology, Chemistry, MD, Modelling & Simulation, more}
UNIT I: Skill sets required for a computer science researcher
 Python, R, Matlab/Octave.
 Optimization Techniques: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evaluation (DE), Ant Colony Optimization (ACO), Artificial Bee Colony Optimization (ABC), etc.
 Graph Plotting: Python (Matplotlib), R(ggplot), Matlab, Excel.
 Latex for Scientific Writing.
 Strong Technical Writing.
UNIT II: Learn Python
 Python download
Python 2.7 → https://www.python.org/downloads/
Anaconda → https://www.continuum.io/downloads
Spyder → https://www.python.org/downloads/
 Learn Basics of Python in 2 hr [Important]
It contains 17 basic programs in python that help to understand the syntax, loops, conditional checking, data structures, file handling in python.
http://bit.ly/RanaPython
 Byte of Python
Very good and easy tutorial to learn advances in python.
http://python.swaroopch.com/
 Scipy, NumPy, Matplotlib [Important]
Specialised libraries for python for various operations such as interpolation, optimization, linear algebra, signal processing, Fourier transformation, etc
http://www.scipy.org → Go to Documentation
http://www.numpy.org
http://matplotlib.org → for Plotting; Go to Gallery and Examples
Basics Plotting using Python
http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf
https://plot.ly/python/
 Scientific Programming, Analysis and Visualization with Python [Important]
Part I, Part II
[Book] Learning SciPy for Numerical and Scientific Computing Second Edition Click Here to download.
[Book] A primer on scientific programming with Python Click Here to download.
 Machine Learning using Python [Important]
http://scikitlearn.org
 Python Code for Optimization [Important]
Click Here to Download.
 PCA and LDA using Python
Principal Component Analysis and Least Discernment Analysis using Python
http://www.analyticsvidhya.com/blog/2016/03/practicalguideprincipalcomponentanalysispython/ http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
http://sebastianraschka.com/Articles/2014_python_lda.html
 Graph Theory using Python
Algorithm and Problems
Graph Tools → http://graphtool.skewed.de → Go to Documentation
iGraph → http://igraph.org/python/
NetworkX → https://networkx.github.io → Documentation + Examples + Tutorial
 Python Packages for research [Most Important]
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
https://pypi.org
UNIT III: Learn R
 R and RStudio
R → https://cran.rstudio.com/bin/windows/base/
RStudio → https://www.rstudio.com/products/rstudio/download/
 Books
Books on R, Python, Machine Leaning, Big Data Analytics
Go to → http://bit.ly/MachineLearningBooks 01  The Machine Learning  Starter Kit
 02  Data Mining with Rattle and R
 03  Elements of Statistical Learning data mining, inference and prediction
 04  An Introduction to Statistical Learning with Applications in R
 05  Applied Predictive Modeling
 07  R for Everyone Advanced Analytics and Graphics
 08  Reproducible Research with R and RStudio
 Explore "Others Books on Machine Learning"
 Explore "BooksMathsLinear LagebraProbalility"
 Explore "BooksSoftcomputing"
 Sample Dataset
Data set for Machine Learning practical
http://bit.ly/SampleDataSet
 Rattle Videos
Videos on Rattle, R Studio, Create R Package
http://bit.ly/MachineLearningUsingRattle
 Machine Learning Models Code in R
Machine Learning Models using R Coding + Hands on R programming.
http://bit.ly/MachineLearningCodeInR
 R Code for Numerai
Click Here to download
 R Packages for research [Most Important]
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
R Packages for Research
UNIT IV: Important Resources for Machine Learning
 Machine Learning MOOCs on Coursera.org [Most Important]
Recommeded courses from University of Washington.
Machine Learning @ Coursera
 Videos on Big Data [Most Important]
Learn Big Data Analytics using Top YouTube Videos, TED Talks & other resources
http://bit.ly/BigDataAnalyticsVideos
 The Talking Machines
Discussion on latest topics on Machine learning people from Academics / Industry.
www.thetalkingmachines.com
 Kaggle [Important]
Machine Learning Competitions. Helpful in selecting research topics.
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.kaggle.com
 R bloggers [Important]
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.rbloggers.com
 KDNuggests
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.kdnuggets.com
 Analytics Vidhya
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.analyticsvidhya.com
 Machine Learning Competitions (Crowd Analytics) [Most Important]
Helpful in selecting research topics. www.kaggle.com
 www.chalearn.org
 www.mlwave.com
 www.tunedit.org
 www.codalab.org
 www.gesture.chalearn.org
 www.innocentive.com
 www.dreamchallenges.org
 www.crowdanalytix.com
 www.datahack.analyticsvidhya.com
 www.numer.ai
 www.genomeinterpretation.org
 http://grandchallenges.org/
 www.kaggle.com
 Machine Learning Models in R [Important]
http://bit.ly/MachineLearningModelsInR
 Data Set for Machine Learning [Most Important]
http://bit.ly/DataForMachineLearning
 Machine Learning Mastery
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.machinelearningmastery.com
 Join Mailing Groups [very imp]
Helpful in selecting research topics, recent news and updates
 http://bit.ly/MachineLearningBlogAndResource
 http://feedburner.google.com/fb/a/mailverify?uri=analyticsvidhya
 http://www.innocentive.com/blog
 https://www.crowdanalytix.com/blog
 http://www.kdnuggets.com/news/subscribe.html
 http://www.rbloggers.com/blogslist/
 http://bit.ly/MachineLearningBlogAndResource
 Mathematics for Machine Learning [Most Important]
 Explore "BooksMathsLinear LagebraProbalility"
 Self Evaluation in maths for machine learning.
http://bit.ly/MathsForMachineLearning1
 How to improve maths skills?
http://bit.ly/MathsForMachineLearning2
 Skills Required for Machine Learning jobs.
http://bit.ly/MathsForMachineLearning3
 Introduction to linear algebra with R
http://bit.ly/MathsForMachineLearning4
 Workshop on R
http://bit.ly/MachineLearningWorkshop1
 Explore "BooksMathsLinear LagebraProbalility"
UNIT V: Things to do for a Research Scholar
 Maintain a notebook for daily work plan.
 To improve Technical Writing: One Page Writing of Abstract + Conclusion
 Learning by doing.
 Learn Python/NumPy/Scipy/Matplotlib.
 Learn Rattle/R/Weka.
 Learn Matlab and Octave (Alternative to Matlab)
 Learn Latex.
 BiWeekly Presentation.
 Join mailing list e.g.:
Indeed.com, RBloggers.com, Kaggle.com, Bioclues.org, kdnuggets.com
 Learn Optimization Techniques.
 Learn Softcomputing Techniques.
 Learn Genetic Algorithm
 Learn PSO, ABC, ACO, DE.
 Learn MultiObjective Optimization (NSGA II)
 To learn FAST
 Learn from slides.
 Learn from Youtube / Videos.
 Learn code sharing (Githhub, Shiny, etc)
 Githhub: https://github.com
 Shiny: http://shiny.rstudio.com
UNIT VI: How to Choose a Research Topic
 Explore the Competitions and choose a topic
 For Optimization Problems

Encyclopedia of Optimization
This is a very good book for optimization problems. Explore 'Subject Index' section is very useful.

Encyclopedia of Optimization
 For Machine Learning Problems
 Machine Learning Salon
This is very good resource for machine learning. It contain information about research groups, blogs, article, people, problem domain and many more.
 Data Tau
Great resource for machine learning in the form on Blogs & Forums. It may help to choose research topic.
 Machine Learning Salon
 For Computer Networks
Those who are interested in Computer Networks (Security, Modelling, Analysis, Simulation, and many more). Kindly explore the following books.  Explore the R Packages of your interest
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
R Packages for Research
 Explore the Python Packages of your interest
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
https://pypi.python.org/pypi
UNIT VII: Gold Mine for Researcher
 For Research Papers
 Go to www.scihub.bz
or www.scihub.io
Search using DOI.
Example (Search for): 10.1016/j.bbapap.2014.07.010
 Go to booksc.org
Search using title.
Example (Search for): Quality assessment of modeled protein structure
 Go to www.scihub.bz
or www.scihub.io
 For Books
 For Thesis
 pqdtopen.proquest.com
 search.proquest.com
username: nisd1
passwd: research
 pqdtopen.proquest.com
UNIT VIII: Research in Computational {Biology, Chemistry, MD, Modelling & Simulation, more}
 Computational Chemistry Tools and Softwares
 Molecular Dynamics Softwares
Most Important
"If you give 100 hour per week, you can complete your PhD in three years"  Prof. Bhim Singh, IIT Delhi.
Finally
 "Mathematics is the queen of all the sciences"  Anonymous
 Whenever you have time, solve/explore maths problems, solve/explore graph problems, do maths using R/Python/Matlab/Octave, explore competitions, explore dataset.
 "Great minds discuss ideas, Average minds discuss events, Small minds discuss people"
 "Success is a journey...not a Destination!!!!"