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]:

2. • https://medium.com/the-researchers-guide
3. • https://onezero.blog/

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