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Data Science Methodology in Action using ikigailabs

سرفصل های دوره

Gain hands-on experience in building a Data Driven AI engagement using ikigailabs


1. Introduction
  • 1. Why This Course
  • 2.1 S1-L2-Course-Overview.pdf
  • 2. Course Overview
  • 3. Learning Objectives.html
  • 4.1 S1-L3-Course Outline.pdf
  • 4. Course Outlne
  • 5.1 L1-S4-DSM.pdf
  • 5. Data Science Methodology
  • 6. ASUM-DM Project Management Method components.html
  • 7.1 Instructors Bio.pdf
  • 7. Instructors Bio

  • 2. Set-up Sandbox
  • 1.1 S2-L1-Setup-Sandbox.pdf
  • 1. Sand-box set-up overview
  • 2. What is the requirement to share an Ikigai project with others.html
  • 3.1 S2-L2-Product-Features.pdf
  • 3. Product Features
  • 4. Which of the features apply to IkigaiLabs.html
  • 5.1 S2-L3-Product-Demonstration.pdf
  • 5. Product Demonstration
  • 6. What are various functionalities available with Ikigai flows.html

  • 3. Step 1 - Define Project
  • 1.1 S3-L1-Step-1-Define-Project-Concepts.pdf
  • 1. Define Project - Concepts
  • 2. Learn use case selection criteria.html
  • 3.1 S3-L2-Define-Project-Example.pdf
  • 3. Define Project - Example
  • 4. Learn use case components.html

  • 4. Step 2 - Describe Data
  • 1.1 S4-L1-Describe-Data-Concepts.pdf
  • 1. Describe Data - Concepts
  • 2. Learn big data characteristics.html
  • 3.1 S4-L2-Step-2-Describe-Data-Tasks-Deliverables.pdf
  • 3. Describe Data - Templates
  • 4.1 Health Equity DataSet ikigailabs.zip
  • 4.2 S4-L3-Task 1. Load Data Sources.pdf
  • 4. Step 2 - Task 1 - Load Data Sources
  • 5. Limit on number of files in a folder.html
  • 6.1 S4-L4-Task 2 Classify Datasets.pdf
  • 6. Step 2 - Task 2 -Classify Data Sets
  • 7.1 S4-L5-Task 3 Describe Datasets.pdf
  • 7. Step 2 - Task 3 - Describe Data Sets
  • 8.1 S4-L5-Task 3 Describe Datasets.pdf
  • 8. Step 2 - Task 4 - Verify Data Quality
  • 9. Data Quality.html

  • 5. Step 3- Prepare Data
  • 1.1 ✅S5-L1-Prepare-Data-Reduction.pdf
  • 1. Prepare Data - Reduction
  • 2. Data Reduction.html
  • 3.1 ✅S5-L2-Prepare-Data-Feature-Engineering.pdf
  • 3. Prepare Data - Feature Engineering
  • 4. Health Equity Feature.html
  • 5.1 ✅S5-L3-Prepare-Data-Synthesis.pdf
  • 5. Prepare Data - Synthesis
  • 6. Example of Data Synthesis.html
  • 7.1 ✅S5-L4-Ikigai Data Preparation Capabilities.pdf
  • 7. Data Preparation toolkits from Ikigailabs
  • 8. data preparation facets available in Ikigai.html
  • 9.1 ✅S5-L5-Task1 Select.pdf
  • 9. Step 3 - Task 1 - Select
  • 10. How to utilize Select facet.html
  • 11.1 ✅S5-L6-Task2-Reformat.pdf
  • 11. Step 3 - Task 2 - Reformat
  • 12. Use of Convert function.html
  • 13.1 ✅S5-L7-Task3- Filter.pdf
  • 13. Step 3 - Task 3 - Filter
  • 14. Use of Filter facet.html
  • 15.1 ✅S5-L8-Task4 Merge.pdf
  • 15. Step 3 - Task 4 - Merge
  • 16. Use of Inner join facet.html
  • 17.1 ✅S5-L9-Task5-Group.pdf
  • 17. Step 3 - Task 5 - Group
  • 18. Use of Summary facet.html
  • 19.1 ✅S5-L10-Task6 Feature Engineering.pdf
  • 19. Step 3 - Task 6 - Feature Engineering
  • 20. Use of Multi Input Formula facet in Ikigai Labs.html
  • 21.1 ✅S5-L11- Summary of Prepare Data Section.pdf
  • 21.2 Final ABTl.csv
  • 21. Prepare Data - Summary
  • 22. Number of rows in ABT.html

  • 6. Step 4 - Develop Model
  • 1.1 S6-L2-DA-Simple-Statistics.pdf
  • 1. Descriptive Analytics - Simple Statstics
  • 2.1 S6-L3-DA-Bar-Chart.pdf
  • 2. Descriptive Analytics - Bar Chart
  • 3. Bar Chart.html
  • 4.1 S6-L4-DA-Pie-Chart.pdf
  • 4. Descriptive Analytics - Pie Chart
  • 5. Pie chart.html
  • 6.1 S6-L5-DA-Map-Charts.pdf
  • 6. Descriptive Analytics - Map generation
  • 7. Map chart.html
  • 8. Predictive Analytics - Clustering Set-up
  • 9. Clustering facet.html
  • 10.1 S6-L7-Clustering-Results.pdf
  • 10. Predictive Analytics - Clustering Example
  • 11. Visualize clustering results on a Map.html
  • 12.1 S6-L8-Predictive-Analytics-Regression-setup.pdf
  • 12. Predictive Analytics - Regression Set-up
  • 13. Predict facet.html
  • 14.1 S8-L9-Regression-Examples.pdf
  • 14. Predictive Analytics - Regression Example
  • 15. Model Type field in the Predict facet.html
  • 16.1 S6-L10-Prescriptive-Set-up.pdf
  • 16. Prescriptive Analytics - Examples
  • 17. Confounder for scenario analysis.html
  • 18.1 S6-L11-Prescriptive-Algorithm.pdf
  • 18.2 S6-L11-Prescriptive-Algorithm.pdf
  • 18. Prescriptive Analytics - Algorithm
  • 19. Concept of causation.html

  • 7. Step 5 - Evaluate Model
  • 1.1 Clustering results - evaluation.csv
  • 1.2 S7-L1-Evaluate-Model-Clustering.pdf
  • 1. Evaluate Model-Clustering
  • 2. Clustering evalutaion.html
  • 3.1 Regression Results - Evaluation.csv
  • 3.2 S7-L2-Evaluate-Model-Prediction..pdf
  • 3. Evaluate Model - Prediction
  • 4. Evaluating a Prediction model.html

  • 8. Step 6 - Deploy Model
  • 1.1 S8-L1-Deploy Model.pdf
  • 1. Step 6- Deploy Model
  • 2. API Key.html

  • 9. Step 7 - Optimize Model
  • 1.1 S9-L1-Optimize-Model.pdf
  • 1. Step 7 - Optimize Model
  • 2. How to optimize a prediction model.html

  • 10. Summary
  • 1.1 PPT-S10-L1-SummaryNS.pdf
  • 1. Summary and Next Steps
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