End to End Data Science Practicum with Knime

About End to End Data Science Practicum with Knime

The course starts with a top down approach to data science projects. The first step is covering data science project management techniques and we follow

CRISP-DM

methodology with 6 steps below:

Business Understanding :

We cover the types of problems and business processes in real life

Data Understanding:

We cover the data types and data problems. We also try to visualize data to discover.

Data Preprocessing:

We cover the classical problems on data and also handling the problems like

noisy or dirty data and missing values.

Row or column

filtering, data integration with concatenation and joins

. We cover the data transformation such as

discretization, normalization, or pivoting

.

Machine Learning:

we cover the classification algorithms such as _Naive Bayes, Decision Trees, Logistic Regression or K-NN._ We also cover prediction / regression algorithms like linear regression, polynomial regression or decision tree regression. We also cover unsupervised learning problems like clustering and association rule learning with k-means or hierarchical clustering, and a priori algorithms. Finally we cover

ensemble techniques

in Knime.

Evaluation:

In the final step of data science, we study the metrics of success via Confusion Matrix, Precision, Recall, Sensitivity, Specificity for classification; purity , randindex for Clustering and rmse, rmae, mse, mae for Regression / Prediction problems with Knime.

BONUS CLASSES

We also have bonus classes for artificial neural network and deep learning on image processing problems. Warning: We are still building the course and it will take time to upload all the videos. Thanks for your understanding.