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Welcome to The Researchers' Guide (YouTube Channel) blog posts

Hello, I am Rahul Raoniar (PhD Student at IIT Guwahati, India) and welcome to Rahul_CODIFY !

"If you have knowledge, let others light their candles in it." - Margaret Fuller

This is a Python and R data Science Repository for Learning, Contributing and Improving Data Science Literacy

The future Video blogs will include the following:

  1. Blog posts [For readers]:

  4. Codes and instructions for

    • Loading data into R and Python
      • using base Python and R packages
    • Data manipulaton
      • Using Base R
      • dplyr
      • forcats
      • data.table
      • Pandas
      • dfply
    • Data tidying
      • tidyr package
      • broom package
      • Pandas
    • Static Visualization
      • Base R
      • ggplot2
      • Matplotlib
      • Seaborn
      • plotnine
    • Interactive Visualization
      • ggvis
      • rbokeh
      • plotly
      • TrelliscopeJS (Big Data)
    • Modelling
      • Supervised
        • Linear + Linear mixed effect models
        • Logit models (binary, multinominal classification and ordered) & Mixed effect models
        • Survival Analysis [non-parametric, semi-parametric and full parametric models]
        • Tree based models (classification and regression)
        • naive bayes classifier (Probabilistic models)
        • k-nearest neighbour (classification)
        • Ensemble learners (Boot strap aggregation, random forest, Boosting, Extreme gradient boosting)
        • Support Vector Machines
        • Neural Networks using Keras and Tensor Flow
          • shalow Neural Network (nntool, neuralnet packages)
          • Deep Neural Network (h2o, Keras, MXNet packages etc.)
        • Auto ML (h2o package)
      • Unsupervised
        • Clustering
          • K-means
          • Hirarchical
          • Model based
          • Density Based
        • Association Analysis and Sequence Mining
        • Dimension Reduction
          • Principal Component Analysis
          • Multidimensional Scaling
          • Singular Value Decomposition
          • Non-linear dimension reduction (ISOMAP and Locally Linear Embeding)
    • Model Evaluation
      • Contigency Table
      • Cross Validation
      • Performance metrices (Metrics package)
      • ROCR Curve
      • F-measure
      • Hyperparameter tuning using
        • caret
        • mlr
        • H2O
        • Scikit-learn
        • Pycaret
      • Interpretation of ML models using lime (Local Interpretable Model-Agnostic Explanations)
      • Interpretation of ML models using SHAP (Shapley Additive Explanations)

  5. Datasets

  6. Python and R codes in the form of scripts & markdown documents

  7. Interactive dashboard using Tableau

  8. Web based application using Streamlit