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UNIT I: Skill sets required for a computer science researcher

UNIT II: Learn Python

UNIT III: Learn R

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

  1. Python, R, Matlab/Octave.
  2. Optimization Techniques: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evaluation (DE), Ant Colony Optimization (ACO), Artificial Bee Colony Optimization (ABC), etc.
  3. Graph Plotting: Python (Matplotlib), R(ggplot), Matlab, Excel.
  4. Latex for Scientific Writing.
  5. Strong Technical Writing.
 

UNIT II: Learn Python

  1. Download & Install Python
    Anaconda (Highly Recommended) → https://www.continuum.io/downloads
    Default Editor → Spyder
    To Start IDLE → Open "Anaconda Prompt" and write "idle"
     
  2. Learn Basics of Python in 2 hr    [Important]
    It contains 16 basic programs in python that help to understand the syntax, loops, conditional checking, data structures, file handling in python.
    http://bit.ly/Rana-Python
     
  3. Byte of Python
    Very good and easy tutorial to learn advances in python.
    http://python.swaroopch.com/
     
  4. 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/
     
  5. 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.
     
  6. Machine Learning using Python    [Important]
    http://scikit-learn.org
     
  7. Python Code for Optimization    [Important]
    Click Here to Download.
     
  8. PCA and LDA using Python
    Principal Component Analysis and Least Discernment Analysis using Python
    http://www.analyticsvidhya.com/blog/2016/03/practical-guide-principal-component-analysis-python/ http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
    http://sebastianraschka.com/Articles/2014_python_lda.html
     
  9. Graph Theory using Python
    Algorithm and Problems
    Graph Tools → http://graph-tool.skewed.de → Go to Documentation
    iGraph → http://igraph.org/python/
    NetworkX → https://networkx.github.io → Documentation + Examples + Tutorial
     
  10. 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

  1. R and R-Studio
    R → https://cran.rstudio.com/bin/windows/base/
    RStudio → https://www.rstudio.com/products/rstudio/download/
     
  2. 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 "Books-Maths-Linear Lagebra-Probalility"
    • Explore "Books-Softcomputing"

     
  3. Sample Dataset
    Data set for Machine Learning practical
    http://bit.ly/SampleDataSet
     
  4. Machine Learning Models Code in R
    Machine Learning Models using R Coding + Hands on R programming.
    http://bit.ly/MachineLearningCodeInR
     
  5. Machine Learning Models in R    [Important]
    http://bit.ly/MachineLearningModelsInR
     
  6. Rattle Videos
    Videos on Rattle, R Studio, Create R Package
    http://bit.ly/MachineLearningUsingRattle
     
  7. R Code for Numerai
    Click Here to download
     
  8. 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

  1. Machine Learning MOOCs on Coursera.org    [Most Important]
    Recommeded courses from University of Washington.
    Machine Learning @ Coursera
     
  2. Videos on Big Data    [Most Important]
    Learn Big Data Analytics using Top YouTube Videos, TED Talks & other resources
    http://bit.ly/BigDataAnalyticsVideos
     
  3. The Talking Machines
    Discussion on latest topics on Machine learning people from Academics / Industry.
    www.thetalkingmachines.com
     
  4. 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
     
  5. R bloggers    [Important]
    Join Mailing groups for recent article/ research on machine learning, R, and many more.
    www.r-bloggers.com
     
  6. KDNuggests
    Join Mailing groups for recent article/ research on machine learning, R, and many more.
    www.kdnuggets.com
     
  7. Analytics Vidhya
    Join Mailing groups for recent article/ research on machine learning, R, and many more.
    www.analyticsvidhya.com
     
  8. Machine Learning Competitions (Crowd Analytics)    [Most Important]
    Helpful in selecting research topics.
     
  9. Data Set for Machine Learning    [Most Important]
    http://bit.ly/DataForMachineLearning
     
  10. Machine Learning Mastery
    Join Mailing groups for recent article/ research on machine learning, R, and many more.
    www.machinelearningmastery.com
     
  11. Join Mailing Groups [very imp]
    Helpful in selecting research topics, recent news and updates
     
     
  12. Mathematics for Machine Learning    [Most Important]
 

UNIT V: Things to do for a Research Scholar

  1. Maintain a notebook for daily work plan.
     
  2. To improve Technical Writing: One Page Writing of Abstract + Conclusion
     
  3. Learning by doing.
     
  4. Learn Python/NumPy/Scipy/Matplotlib.
     
  5. Learn Rattle/R/Weka.
     
  6. Learn Matlab and Octave (Alternative to Matlab)
     
  7. Learn Latex.
     
  8. Bi-Weekly Presentation.
     
  9. Join mailing list e.g.:
    Indeed.com, R-Bloggers.com, Kaggle.com, Bioclues.org, kdnuggets.com
     
  10. Learn Optimization Techniques.
    • Learn Softcomputing Techniques.
    • Learn Genetic Algorithm
    • Learn PSO, ABC, ACO, DE.
    • Learn Multi-Objective Optimization (NSGA II)
  11. To learn FAST
    • Learn from slides.
    • Learn from Youtube / Videos.

     
  12. Learn code sharing (Githhub, Shiny, etc)
    • Githhub: https://github.com
    • Shiny: http://shiny.rstudio.com
 

UNIT VI: How to Choose a Research Topic

  1. Explore the Competitions and choose a topic
  2. For Optimization Problems
  3. 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.

       
  4. For Computer Networks
    Those who are interested in Computer Networks (Security, Modelling, Analysis, Simulation, and many more). Kindly explore the following books.
  5. 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
     
  6. 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

  1. For Research Papers
    • Go to www.sci-hub.tw
      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
       
  2. For Books
  3. For Thesis
 

UNIT VIII: Research in Computational {Biology, Chemistry, MD, Modelling & Simulation, more}

  1. Computational Chemistry Tools and Softwares
  2. 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
  1. "Mathematics is the queen of all the sciences" - Anonymous
     
  2. Whenever you have time, solve/explore maths problems, solve/explore graph problems, do maths using R/Python/Matlab/Octave, explore competitions, explore dataset.
     
  3. "Great minds discuss ideas, Average minds discuss events, Small minds discuss people"
     
  4. "Success is a journey...not a Destination!!!!"