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data
data
 
 
examples
examples
 
 
lab_9+10
lab_9+10
 
 
old files that were put into g20 format
old files that were put into g20 format
 
 
other old files
other old files
 
 
pdfs
pdfs
 
 
plots
plots
 
 
.gitignore
.gitignore
 
 
Functions_for_Graphics_DT.ipynb
Functions_for_Graphics_DT.ipynb
 
 
Functions_for_Graphics_GB.ipynb
Functions_for_Graphics_GB.ipynb
 
 
Functions_for_Graphics_RF.ipynb
Functions_for_Graphics_RF.ipynb
 
 
H - (other) Feature Selection Percentile.ipynb
H - (other) Feature Selection Percentile.ipynb
 
 
H - (other) Feature Selection Variance Threshold.ipynb
H - (other) Feature Selection Variance Threshold.ipynb
 
 
H - (other) Feature Selection chi2.ipynb
H - (other) Feature Selection chi2.ipynb
 
 
H - Clustering & Scaling.ipynb
H - Clustering & Scaling.ipynb
 
 
H - PCA.ipynb
H - PCA.ipynb
 
 
H - Pattern Mining - FPGrowth test.ipynb
H - Pattern Mining - FPGrowth test.ipynb
 
 
H - Pattern Mining - JB.ipynb
H - Pattern Mining - JB.ipynb
 
 
H S - Overfitting and analysis.ipynb
H S - Overfitting and analysis.ipynb
 
 
H0 - Data Profiling.ipynb
H0 - Data Profiling.ipynb
 
 
H1 - Data Balancing.ipynb
H1 - Data Balancing.ipynb
 
 
H2 - Scaling.ipynb
H2 - Scaling.ipynb
 
 
H3 - Feature Generation and manual selection.ipynb
H3 - Feature Generation and manual selection.ipynb
 
 
H4 - Feature Selection K Best.ipynb
H4 - Feature Selection K Best.ipynb
 
 
H5 - Outliers.ipynb
H5 - Outliers.ipynb
 
 
Heart.bak
Heart.bak
 
 
Heart.dat
Heart.dat
 
 
Heart.dir
Heart.dir
 
 
README.md
README.md
 
 
T - (other) Feature Selection Percentile.ipynb
T - (other) Feature Selection Percentile.ipynb
 
 
T - (other) Feature Selection Variance Threshold.ipynb
T - (other) Feature Selection Variance Threshold.ipynb
 
 
T - (other) Feature Selection chi2.ipynb
T - (other) Feature Selection chi2.ipynb
 
 
T - Clustering.ipynb
T - Clustering.ipynb
 
 
T - PCA.ipynb
T - PCA.ipynb
 
 
T - Pattern Mining - JB.ipynb
T - Pattern Mining - JB.ipynb
 
 
T - Pattern Mining.ipynb
T - Pattern Mining.ipynb
 
 
T S - Overfitting and analysis.ipynb
T S - Overfitting and analysis.ipynb
 
 
T0 - Data Profiling.ipynb
T0 - Data Profiling.ipynb
 
 
T1 - Data Balancing.ipynb
T1 - Data Balancing.ipynb
 
 
T2 - Feature Selection K Best.ipynb
T2 - Feature Selection K Best.ipynb
 
 
T3 - Outliers.ipynb
T3 - Outliers.ipynb
 
 
Toxic.bak
Toxic.bak
 
 
Toxic.dat
Toxic.dat
 
 
Toxic.dir
Toxic.dir
 
 
config.py
config.py
 
 
ds_functions.py
ds_functions.py
 
 
dslabs.mplstyle
dslabs.mplstyle
 
 
g20_Clustering.py
g20_Clustering.py
 
 
g20_Data_Profiling.py
g20_Data_Profiling.py
 
 
g20_Decision_Trees.py
g20_Decision_Trees.py
 
 
g20_Gradient_Boosting.py
g20_Gradient_Boosting.py
 
 
g20_KNN.py
g20_KNN.py
 
 
g20_Naive_Bayes.py
g20_Naive_Bayes.py
 
 
g20_Pattern_Mining.py
g20_Pattern_Mining.py
 
 
g20_Random_Forests.py
g20_Random_Forests.py
 
 
g20_functions.py
g20_functions.py
 
 
ts_functions.py
ts_functions.py
 
 

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Data Science

Project data sets

Data profiling
  • Dimensionality
  • Distribution
  • Granularity
  • Sparsity
  • Correlation
Data preparation
  • Missing-values imputation
  • Anomaly/outlier detection and removal
  • Discretization and Dummification
  • Normalization
  • Balancing
Feature engineering
  • Feature selection
  • Feature extraction
  • Feature generation
Unsupervised learning
  • Pattern mining
  • Clustering
Supervised learning
  • Naïve Bayes
  • kNN
  • Decision tree
  • Random forest
  • Gradient boosting
  • Overfitting

Extra lab: Social network analysis (SNA)

Extra lab: Time-series Analysis and Forecasting

Data sets
  • Profiling
  • Transformation
  • Forecasting
  • Motif discovery

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Data Science course - 2020

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