## END TO END DATA SCIENCE PRACTICUM WITH KNIME

You can find the course content for End to End Data Science Practicum with Knime in Udemy. You can also get registered to the course with below discount coupon:

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1. Introduction to Course
1. What is this course about?
2. How are we going to cover the content?
3. Which tool are we going to use?
4. What is unique about the course?
2. What is Data Science Project and our methodology
1. Project Management Techniques
2. KDD
3. CRISP-DM
3. Working environment and Knime
1. Installation of Knime
2. Versioning of Knime
3. Resources (Documentation, Forums and extra resources)
4. Welcome Screen and working environment of Knime
4. Welcome to Data Science
1. Understanding of a workflow
2. First end-to-end problem: Teaching to the machine
3. First end-to-end workflow in Knime
5. Understanding Problem
1. Types of analytics
2. Descriptive Analytics and some classical problems
3. Predictive Analytics
4. Prescriptive Analytics
6. Understanding Data
1. File Types
2. Coloring Data
3. Scatter Matrix
4. Visualization and Histograms
7. Data Preprocessing (Excel Files : cscon_gender ,  cscon_age)
1. Row Filtering
2. Rule Based Row Filtering
3. Column Filtering
4. Group by , Aggregate
5. Join and Concatenation
6. Missing Values and Imputation
7. Date and Time operations
8. Example 1
8. Feature Engineering
1. Encoding: One – To – Many
2. Rule Engine
3. Imbalanced Data: SubSampling, SMOTE
9. Models
1. Introduction to Machine Learning : Test and Train Datasets
2. Introduction to Machine Learning: Problem Types
3. Classification Problems (Excel Files : cscon_gender )
1. Naive Bayes and Bayes Theorem
3. Decision Tree
5. K-Nearest Neighborhood
7. Distance Metrics of KNN
• 9. Support Vector Machines
10. Kernel Trick and SVM Kernels
11. SVM Practicum
12. End to End Practicum for Classification
13. Extra: Logistic Regression
14. Extra: Logistic Regression Practicum
4. ARM Problems
1. ARM / ARL Concept
2. A priori algorithm and association rule extraction
3. ARM Practicum
5. Clustering Problems
6. Regression Problems
1. Linear Regression
2. Linear Regression Practicum
3. Evaluation of Prediction Modes
4. Practicum of Evaluation
5. Multiple Linear Regression
6. Multiple Linear Regression Practicum
7. Polynomial Regression
8. Polynomial Regression Practicum
9. Simple Regression Tree
10. Simple Regression Tree Practicum
• 11. Example 2: Stock market prediction
10. Knime as a tool : Some Advanced Operations
1. PMML File Types and saving the model
2. PMML Practicum with Knime
3. MetaNodes
4. Variables and Flow of a variable
5. Loops and optimizing the model parameters
11. Evaluation
1. Introduction to Evaluation
• 2. ZeroR Algorithm, Imbalanced Data Set and Baseline
3. k-fold Cross Validation
4. Confusion Matrix, Precision, Recall, Sensitivity, Specificity
5. Evaluation of clustering: purity , randindex
6. Evaluation of prediction: rmse, rmae, mse, mae
7. Evaluation Practicum with knime: Example 3
8. Evaluation of ARM
12. Reporting
1. Exporting Reports to Images (Data to Report)
13. Connecting Knime with other Languages
1. Java Snippet
2. R Snippet
3. Python Snippet
14. Meta Learners
1. Ensemble Techniques: Bagging, Boosting and Fusion
2. Random Forest ensemble learning technique for Classification
3. Random Forest ensemble learning technique for Regression
4. Random Forest Practicum
6. Gradient Boosted Tree Regression Practicum
7. Example 4
15. Deep Learning
1. Introduction to artificial neural networks
2. Linearly Separable Problems and beyond
3. DL4J Extension
16. Real Life Practicums
1. Resources about real life applications : Job Search, Forums, Competitions etc.
2. Predicting the customer will pay or not
3. Predicting the period of payments
4. Credit Limit
5. Customer Segmentation
17. Bonus