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:

https://www.udemy.com/datascience-knime/?couponCode=KNIME_DATASCIENCE

  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
      2. Binning and Naive Bayes practicum (click to download the workflow)
      3. Decision Tree
      4. Decision Tree Practicum (. Click here to download the workflow  )
      5. K-Nearest Neighborhood
      6. KNN Practicum (click to download the workflow)
      7. Distance Metrics of KNN
      8. Distance Metrics Practicum (click here to download the knime workflow)
      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
      1. Introduction to Clustering Concept
      2. K-Means
      3. Optimum K Value in k-Means
      4. K-Means Practicum
      5. Grid Search for optimum k value in k-means
      6. Hierarchical Clustering (Divisive and Agglomerative Approaches)
      7. HC Practicum
      8. DBSCAN
      9. DBSCAN Practicum
    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
    5. Gradient Boosted Tree Regression
    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
    1. Loading different train and test datasets
    2. Data Preprocessing Practicum(Click to download knime file)
    3. Regression Practicum  (Simple Linear, Multiple Linear Regression, Correlation Matrix, p-Value and Feature Elimination (backward Elimination, Forward Selection) )Knime Files: File 1, File 2
    4. Comparing the Regression Models : Decision Tree Regression, Random Forest Regression, Linear Regression, Polynomial Regression (Click to Download the Knime File)
    5. Evaluation of Regression Models (R2 and adjusted R2 ), Introduction to Classification problems and Logistic Regression
    6. Time Series Analysis and Classification algorithms : Decision Tree, Random Forest
    7. Imbalanced Datasets
    8. Customer Segmentation and Python ( Click to Download Knime File , Click to Download Dataset)
    9. Comparison of Clustering Algorithms: K-Means, K-Medoids ve Hierarchical Clustering (HC) and finding optimum Number of clusters with WCSS for K-Means ( cluster distance functions min, Max, group average, center, ward’s method) : Click to download knime file
      • Steps for above workflow
      • 1. Load Iris dataset
        1.1. Filter the class column
        2. For K-Means and K-Medoids
        2.1. Find the best cluster number
        2.2. Cluster with K-means and K-medoids
        3. Find the best K value for Hierarchical Clustering(HC)
        4. Cluster with HC
        5. Cluster with K=3
        6. Compare your results with K=3
        7. Report the best clustering
      • Evaluations:HC :  24 Error
        KMeans : 17 error
        KMedoids : 16 error
    10. Text Mining.

Jobs

Current open positions in OptiWisdom

OptiWisdom Project Manager Job Description (Active)

We have an exciting opportunity at our Antalya based global company. We have internet based operations in US, Europe and Turkey.

Location and Application

Position is open for only Antalya Technopolis. Please apply to the job only if you currently stay in Antalya. You can apply the job by sending a CV and cover letter to optiwisdom@optiwisdom.com

Job Definition

  • Work closely with Sales team and/or Advisory consultancy team business owners to establish a clear charter for client engagements sold
  • Lead the definition of project scope for more complex multi solution engagements and key customer deliverables
  • Develop project plans and schedule to set achievable expectations with all stakeholders, both internal and external
  • Manage the daily implementation of contracted solutions, often many solutions at once
  • Perform ongoing account delivery functions for selected customers
  • Partner with functional leads to assemble key resources and establish roles and responsibilities to ensure program goals and objectives are met
  • Bring in internal Subject Matter Experts as necessary to address client inquiries, while remaining main point of contact for the program
  • Clearly communicate and keep track of project milestones and status for all levels of the organization
  • Manage and track risk to project and program deliverables throughout the lifecycle of each engagement. Escalate to key stakeholders early enough for course correction
  • Change agent to drive the adoption of JIRA to support process improvements leveraging SCRUM practices
  • Track to resolution, software defects and known issues, with product and engineering teams in order to keep customer abreast of how these items may impact their implementation
  • Champion for process improvement and how best to instill project management best practices within the organization, above and beyond top tier customer engagements
  • Become the client champion for projects supported by the project manager to lead internal teams to deliver on time for the client

Operations:

  • Program budget tracking and reporting to management
  • Data gathering,
  • Administer change management
  • Manages risk management
  • Meeting cadence management,
  • Publish minutes, action items
  • Orchestration of the SDLC
  • Strong communication with development team
  • Scrum Mastering
  • Safe / Agile JiRA administration

Desired Skills/Qualifications/System Experience requirements: (Nice to have Qualifications)

  • Capex/Opex/Deprecation account practices
  • Microsoft Office Suite
  • Agile experiences in large enterprise environment
  • Excellent written and verbal communication skills in both English and Turkish
  • Working independently
  • PMP Certification
  • SAFE/Agile/Jira Certification or equivalent experience
  • Antalya BasedAbout OptiWisdom:An artificial intelligence, machine learning and data science company based in Antalya. We are actively operating in four major domains. 1) In education domain, we are actively training top companies in Turkey about the data science projects. 2) In consultancy, we have active consultancy agreements with sector leader companies and governmental organizations. 3) In R&D projects we are currently writing TUBITAK projects and we have active EU funded R&D projects. We also have an office in Technopolis Antalya. 4) We are a mid sized startup, developing software products for the AI and Data Science domain. We have a domain independent scoring engine, OptiScorer, segmentation tool OptiSegment and matching / recommender engine OptiLinker. We have just launched our first products AnalytiXR a few months ago and its currently getting connected to the top App Markets around the world.

DIVERSITY
Diversity, inclusion and teamwork are second nature to OptiWisdom; and these values permeate our entire business structure. OptiWisdom is committed to creating an environment where a wide spectrum of opinions and beliefs are actively sought, listened to and respected. Further, our aim is creating talented workforce from the many geographic areas and markets in which OptiWisdom operates worldwide, which represents a distinct competitive advantage. The rich and varied personal and professional backgrounds of our employees make OptiWisdom a dynamic and rewarding company at which to build a career. We invite you to join us.

EOE of Minorities/Females/Vets/Disability
OptiWisdom, Inc. considers all applicants for employment without regard to race, color, religion, sex, national origin, age, disability or sexual orientation, OptiWisdom, Inc., is a woman prioritized company with 73% woman employees.

 

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:

https://www.udemy.com/datascience-knime/?couponCode=KNIME_DATASCIENCE

  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
      2. Binning and Naive Bayes practicum (click to download the workflow)
      3. Decision Tree
      4. Decision Tree Practicum (. Click here to download the workflow  )
      5. K-Nearest Neighborhood
      6. KNN Practicum (click to download the workflow)
      7. Distance Metrics of KNN
      8. Distance Metrics Practicum (click here to download the knime workflow)
      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
      1. Introduction to Clustering Concept
      2. K-Means
      3. Optimum K Value in k-Means
      4. K-Means Practicum
      5. Grid Search for optimum k value in k-means
      6. Hierarchical Clustering (Divisive and Agglomerative Approaches)
      7. HC Practicum
      8. DBSCAN
      9. DBSCAN Practicum
    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
    5. Gradient Boosted Tree Regression
    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
    1. Loading different train and test datasets
    2. Data Preprocessing Practicum(Click to download knime file)
    3. Regression Practicum  (Simple Linear, Multiple Linear Regression, Correlation Matrix, p-Value and Feature Elimination (backward Elimination, Forward Selection) )Knime Files: File 1, File 2
    4. Comparing the Regression Models : Decision Tree Regression, Random Forest Regression, Linear Regression, Polynomial Regression (Click to Download the Knime File)
    5. Evaluation of Regression Models (R2 and adjusted R2 ), Introduction to Classification problems and Logistic Regression
    6. Time Series Analysis and Classification algorithms : Decision Tree, Random Forest
    7. Imbalanced Datasets
    8. Customer Segmentation and Python ( Click to Download Knime File , Click to Download Dataset)
    9. Comparison of Clustering Algorithms: K-Means, K-Medoids ve Hierarchical Clustering (HC) and finding optimum Number of clusters with WCSS for K-Means ( cluster distance functions min, Max, group average, center, ward’s method) : Click to download knime file
      • Steps for above workflow
      • 1. Load Iris dataset
        1.1. Filter the class column
        2. For K-Means and K-Medoids
        2.1. Find the best cluster number
        2.2. Cluster with K-means and K-medoids
        3. Find the best K value for Hierarchical Clustering(HC)
        4. Cluster with HC
        5. Cluster with K=3
        6. Compare your results with K=3
        7. Report the best clustering
      • Evaluations:HC :  24 Error
        KMeans : 17 error
        KMedoids : 16 error
    10. Text Mining.