Udemy - Data Science Project Planning

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 1.4 GB
  • Uploaded By tutsnode
  • Downloads 177
  • Last checked 3 weeks ago
  • Date uploaded 5 years ago
  • Seeders 6
  • Leechers 2

Infohash : 52A16CADDE8AE080DE65FABE7FF366803C772DE8




Description

Success of any project depends highly on how well it has been planned. Data science projects are no exception.

Large number of data science projects in industrial settings fail to meet the expectations due to lack of proper planning at their inception stage.

This course will provide a overview of core planning activities that are critical to the success of any data science project.

We will discuss the concepts underlying – Business Problem Definition; Data Science Problem Definition; Situation Assessment; Scheduling Tasks and Deliveries.

The concepts learned will help the students in:

A) Framing the business problem

B) Getting buy-in from the stakeholders

C) Identifying appropriate data science solution that can solve the business problem

D) Defining success criteria and metrics to evaluate the key project deliverables viz; models, data flow pipeline and documentation.

E) Assessing the prevailing situation impacting the project. For e.g. availability of data and resources; risks; estimated costs and perceived benefits.

F) Preparing delivery schedules that enable early and continuously incremental valuable actionable insights to the customers

G) Understanding the desired team attributes and communication needs
Who this course is for:

Managers or Leads who are going to plan their first data science project in a real life business environment
Members of a data science team who want to build awareness about crucial planning activities required for making their project successful
Senior Executives requiring a bird’s eye view of activities involved in planning a data science project

Requirements

Willingness to look beyond the technical aspects and learn about the crucial planning activities involved in a data science project.
Familiarity with high school level mathematics

Last Updated 11/2020

Files:

Data Science Project Planning [TutsNode.com] - Data Science Project Planning 1. Introduction
  • 1. Course Preview.mp4 (68.7 MB)
  • 2. Welcome.srt (1.3 KB)
  • 4. Data Science Project - Challenges.srt (2.3 KB)
  • 5. Data Science Project Planning - An Overview.srt (2.9 KB)
  • 6. Introduction.html (0.1 KB)
  • 1. Course Preview.srt (3.9 KB)
  • 3. Context.srt (4.1 KB)
  • 3. Context.mp4 (22.8 MB)
  • 2. Welcome.mp4 (20.6 MB)
  • 5. Data Science Project Planning - An Overview.mp4 (12.0 MB)
  • 4. Data Science Project - Challenges.mp4 (9.1 MB)
3. Data Science Problem Formulation
  • 14.1 Calculation of Accuracy, Precision, Recall & F1 Score.pdf (286.5 KB)
  • 26. Documentation Criteria.srt (17.9 KB)
  • 16.1 Calculation of RMSE and R-Squared.pdf (416.6 KB)
  • 17. Evaluation Metrics for Clustering Models - I Internal Evaluation.srt (12.3 KB)
  • 15. Evaluation Metrics for Anomaly Detection Models.srt (12.2 KB)
  • 20. Evaluation Metrics for Recommendation Models - I.srt (10.7 KB)
  • 26. Documentation Criteria.mp4 (57.3 MB)
  • 14. Evaluation Metrics for Classification Models.srt (10.5 KB)
  • 24. Model Monitoring Metrics.srt (7.8 KB)
  • 2. Data Science Project Lifecyle - An Overview of CRISP-DM.srt (9.9 KB)
  • 1. Introduction.srt (2.1 KB)
  • 13. Review Questions.html (0.1 KB)
  • 15. Evaluation Metrics for Anomaly Detection Models.mp4 (49.6 MB)
  • 20.1 Calculation of MAE and RMSE.pdf (342.7 KB)
  • 11. Setting Project Goals.srt (5.9 KB)
  • 22. Review Questions.html (0.1 KB)
  • 27. Review Questions.html (0.1 KB)
  • 17. Evaluation Metrics for Clustering Models - I Internal Evaluation.mp4 (49.4 MB)
  • 10.2 Data Science Problem Types – Summary.pdf (107.4 KB)
  • 4. Data Science Problem Type - Classification.srt (9.7 KB)
  • 7. Data Science Problem Type - Anomaly Detection.srt (3.1 KB)
  • 10. Summary of Data Science Problem Types.srt (2.4 KB)
  • 17.3 Dunn Index Calculation.pdf (273.9 KB)
  • 25. Data Flow Pipeline Metrics.srt (9.0 KB)
  • 21. Evaluation Metrics for Recommendation - II.srt (8.8 KB)
  • 4. Data Science Problem Type - Classification.mp4 (42.5 MB)
  • 12. Specifying Project Success Criteria – An Overview.srt (7.2 KB)
  • 19. Evaluation Metrics for Association Models.srt (5.5 KB)
  • 6. Data Science Problem Type - Clustering.srt (5.4 KB)
  • 16. Evaluation Metrics for Regression Models.srt (5.0 KB)
  • 14. Evaluation Metrics for Classification Models.mp4 (40.7 MB)
  • 19.1 Calculation of Support, Calculation and Lift.pdf (260.6 KB)
  • 5. Data Science Problem Type - Regression.srt (4.9 KB)
  • 23. Model Deployment Criteria and Metrics.srt (4.8 KB)
  • 18. Evaluation Metrics for Clustering Models - II External Evaluation.srt (4.5 KB)
  • 10.1 Data Science Problem Types - A Visual Recap.pdf (243.6 KB)
  • 3. Data Science Problem Formulation – An Overview.srt (3.0 KB)
  • 9. Data Science Problem Type - Recommendation.srt (3.7 KB)
  • 8. Data Science Problem Type - Association.srt (3.0 KB)
  • 20. Evaluation Metrics for Recommendation Models - I.mp4 (40.3 MB)
  • 2. Data Science Project Lifecyle - An Overview of CRISP-DM.mp4 (40.1 MB)
  • 18.1 Calculation of Rand Index and Jaccard Index.pdf (244.3 KB)
  • 21.1 Intra-List Similarity Calculation.pdf (240.3 KB)
  • 20.2 Mean Average Precision (MAP) Calculation.pdf (187.2 KB)
  • 24.1 Population Stability Index Calculation.pdf (199.5 KB)
  • 1. Introduction.mp4 (33.3 MB)
  • 17.1 Data Transformation - Encoding & Dummy Variables.pdf (212.7 KB)
  • 15.1 Accuracy Paradox.pdf (224.1 KB)
  • 21. Evaluation Metrics for Recommendation - II.mp4 (28.9 MB)
  • 24. Model Monitoring Metrics.mp4 (26.8 MB)
  • 12. Specifying Project Success Criteria – An Overview.mp4 (26.2 MB)
  • 25. Data Flow Pipeline Metrics.mp4 (24.6 MB)
  • 6. Data Science Problem Type - Clustering.mp4 (23.8 MB)
  • 16. Evaluation Metrics for Regression Models.mp4 (21.9 MB)
  • 5. Data Science Problem Type - Regression.mp4 (19.5 MB)
  • 11. Setting Project Goals.mp4 (19.5 MB)
  • 19. Evaluation Metrics for Association Models.mp4 (18.3 MB)
  • 9. Data Science Problem Type - Recommendation.mp4 (16.4 MB)
  • 18. Evaluation Metrics for Clustering Models - II External Evaluation.mp4 (14.9 MB)
  • 8. Data Science Problem Type - Association.mp4 (14.4 MB)
  • 7. Data Science Problem Type - Anomaly Detection.mp4 (13.1 MB)
  • 23. Model Deployment Criteria and Metrics.mp4 (12.6 MB)
  • 10. Summary of Data Science Problem Types.mp4 (10.5 MB)
  • 3. Data Science Problem Formulation – An Overview.mp4 (8.0 MB)
  • 17.2 Silhouette Coefficient Calculation.pdf (527.4 KB)
5. Project Scheduling
  • 3. Scheduling - II.srt (17.5 KB)
  • 2.1 Data Science Project Lifecycle - Phases, Activities & Deliverables.pdf (144.1 KB)
  • 3. Scheduling - II.mp4 (47.1 MB)
  • 2. Scheduling - I.srt (9.2 KB)
  • 1. Introduction.srt (1.1 KB)
  • 4. Review Questions.html (0.1 KB)
  • 2. Scheduling - I.mp4 (39.6 MB)
  • 1. Introduction.mp4 (19.1 MB)
4. Situation Assessment
  • 7. Risk Assessment.srt (14.9 KB)
  • 4. Resource Assessment.srt (10.1 KB)
  • 5. Project Requirements, Assumptions & Constraints.srt (7.5 KB)
  • 6. Review Questions.html (0.1 KB)
  • 10. Review Questions.html (0.1 KB)
  • 3. Team Composition.srt (7.5 KB)
  • 9. Costs and Benefits.srt (4.2 KB)
  • 7. Risk Assessment.mp4 (40.7 MB)
  • 2. Situation Assessment - An Overview.srt (4.2 KB)
  • 8. Terminology.srt (2.7 KB)
  • 1. Introduction.srt (0.9 KB)
  • 4. Resource Assessment.mp4 (29.1 MB)
  • 3. Team Composition.mp4 (25.7 MB)
  • 5. Project Requirements, Assumptions & Constraints.mp4 (20.8 MB)
  • 1. Introduction.mp4 (15.0 MB)
  • 9. Costs and Benefits.mp4 (12.8 MB)
  • 8. Terminology.mp4 (12.0 MB)
  • 2. Situation Assessment - An Overview.mp4 (11.7 MB)
6. Emerging Methods
  • 4. Agile Data Science 2.0.srt (14.5 KB)
  • 4. Agile Data Science 2.0.mp4 (50.8 MB)
  • 3. Microsoft Team Data Science Process (TDSP).srt (10.1 KB)
  • 2. Emerging Methods for Executing Data Science Projects.srt (5.3 KB)
  • 1. Introduction.srt (0.8 KB)
  • 5. Review Questions.html (0.1 KB)
  • 3. Microsoft Team Data Science Process (TDSP).mp4 (36.9 MB)
  • 1. Introduction.mp4 (13.9 MB)
  • 2. Emerging Methods for Executing Data Science Projects.mp4 (13.2 MB)
2. Business Problem Definition
  • 1. Introduction.srt (1.0 KB)
  • 4. Stakeholder Analysis - I.srt (6.6 KB)
  • 8. Review Questions.html (0.1 KB)
  • 5. Stakeholder Analysis - II.srt (10.4 KB)
  • 7.1 Business Problem Statement - Examples.pdf (108.2 KB)
  • 4.1 Questions for Stakeholders – A Cheat Sheet.pdf (95.7 KB)
  • 7. Framing the Business Problem.srt (9.0 KB)
  • 3. Understanding the Business Problem.srt (6.6 KB)
  • 2. Business Problem Definition - An Overview.srt (4.6 KB)
  • 6. Review of Previous Work.srt (2.7 KB)
  • 5. Stakeholder Analysis - II.mp4 (35.3 MB)
  • 7. Framing the Business Problem.mp4 (30.2 MB)
  • 4. Stakeholder Analysis - I.mp4 (22.3 MB)
  • 3. Understanding the Business Problem.mp4 (20.1 MB)
  • 1. Introduction.mp4 (16.0 MB)
  • 2. Business Problem Definition - An Overview.mp4 (14.5 MB)
  • 6. Review of Previous Work.mp4 (9.0 MB)
7. Conclusion
  • 3.1 Project Plan Review Checkpoints.pdf (122.9 KB)
  • 2. Recap of Key Points.srt (9.3 KB)
  • 5. Closing Remarks.srt (4.4 KB)
  • 3. Project Plan Review - Checkpoints.srt (3.7 KB)
  • 6. Congratulations and Thanks.srt (0.8 KB)
  • 1. Introduction.srt (0.6 KB)
  • 4. Preparing a Project Plan.html (0.1 KB)
  • 2. Recap of Key Points.mp4 (27.4 MB)
  • 5. Closing Remarks.mp4 (13.9 MB)
  • 6. Congratulations and Thanks.mp4 (13.7 MB)
  • 3. Project Plan Review - Checkpoints.mp4 (12.3 MB)
  • 1. Introduction.mp4 (10.0 MB)
  • TutsNode.com.txt (0.1 KB)
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