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.