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Udemy - Data Science Project Planning

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Udemy - Data Science Project Planning (Size: 1.38 GB)
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  55 511.23 KB
  TutsNode.com.txt 63 B
  [TGx]Downloaded from torrentgalaxy.to .txt 585 B
  [TutsNode.com] - Data Science Project Planning
  1. Introduction
  1. Course Preview.mp4 68.7 MB
  1. Course Preview.srt 3.92 KB
  2. Welcome.mp4 20.59 MB
  2. Welcome.srt 1.34 KB
  3. Context.mp4 22.85 MB
  3. Context.srt 4.13 KB
  4. Data Science Project - Challenges.mp4 9.07 MB
  4. Data Science Project - Challenges.srt 2.31 KB
  5. Data Science Project Planning - An Overview.mp4 12.04 MB
  5. Data Science Project Planning - An Overview.srt 2.92 KB
  6. Introduction.html 140 B
  2. Business Problem Definition
  1. Introduction.mp4 15.99 MB
  1. Introduction.srt 1.04 KB
  2. Business Problem Definition - An Overview.mp4 14.49 MB
  2. Business Problem Definition - An Overview.srt 4.63 KB
  3. Understanding the Business Problem.mp4 20.05 MB
  3. Understanding the Business Problem.srt 6.55 KB
  4. Stakeholder Analysis - I.mp4 22.31 MB
  4. Stakeholder Analysis - I.srt 6.61 KB
  4.1 Questions for Stakeholders – A Cheat Sheet.pdf 95.67 KB
  5. Stakeholder Analysis - II.mp4 35.3 MB
  5. Stakeholder Analysis - II.srt 10.42 KB
  6. Review of Previous Work.mp4 9 MB
  6. Review of Previous Work.srt 2.65 KB
  7. Framing the Business Problem.mp4 30.2 MB
  7. Framing the Business Problem.srt 8.95 KB
  7.1 Business Problem Statement - Examples.pdf 108.22 KB
  8. Review Questions.html 144 B
  3. Data Science Problem Formulation
  1. Introduction.mp4 33.32 MB
  1. Introduction.srt 2.07 KB
  10. Summary of Data Science Problem Types.mp4 10.51 MB
  10. Summary of Data Science Problem Types.srt 2.43 KB
  10.1 Data Science Problem Types - A Visual Recap.pdf 243.6 KB
  10.2 Data Science Problem Types – Summary.pdf 107.35 KB
  11. Setting Project Goals.mp4 19.47 MB
  11. Setting Project Goals.srt 5.88 KB
  12. Specifying Project Success Criteria – An Overview.mp4 26.18 MB
  12. Specifying Project Success Criteria – An Overview.srt 7.25 KB
  13. Review Questions.html 144 B
  14. Evaluation Metrics for Classification Models.mp4 40.72 MB
  14. Evaluation Metrics for Classification Models.srt 10.48 KB
  14.1 Calculation of Accuracy, Precision, Recall & F1 Score.pdf 286.53 KB
  15. Evaluation Metrics for Anomaly Detection Models.mp4 49.55 MB
  15. Evaluation Metrics for Anomaly Detection Models.srt 12.23 KB
  15.1 Accuracy Paradox.pdf 224.12 KB
  16. Evaluation Metrics for Regression Models.mp4 21.89 MB
  16. Evaluation Metrics for Regression Models.srt 4.96 KB
  16.1 Calculation of RMSE and R-Squared.pdf 416.63 KB
  17. Evaluation Metrics for Clustering Models - I Internal Evaluation.mp4 49.38 MB
  17. Evaluation Metrics for Clustering Models - I Internal Evaluation.srt 12.3 KB
  17.1 Data Transformation - Encoding & Dummy Variables.pdf 212.69 KB
  17.2 Silhouette Coefficient Calculation.pdf 527.37 KB
  17.3 Dunn Index Calculation.pdf 273.88 KB
  18. Evaluation Metrics for Clustering Models - II External Evaluation.mp4 14.88 MB
  18. Evaluation Metrics for Clustering Models - II External Evaluation.srt 4.51 KB
  18.1 Calculation of Rand Index and Jaccard Index.pdf 244.3 KB
  19. Evaluation Metrics for Association Models.mp4 18.28 MB
  19. Evaluation Metrics for Association Models.srt 5.46 KB
  19.1 Calculation of Support, Calculation and Lift.pdf 260.57 KB
  2. Data Science Project Lifecyle - An Overview of CRISP-DM.mp4 40.1 MB
  2. Data Science Project Lifecyle - An Overview of CRISP-DM.srt 9.92 KB
  20. Evaluation Metrics for Recommendation Models - I.mp4 40.32 MB
  20. Evaluation Metrics for Recommendation Models - I.srt 10.68 KB
  20.1 Calculation of MAE and RMSE.pdf 342.74 KB
  20.2 Mean Average Precision (MAP) Calculation.pdf 187.16 KB
  21. Evaluation Metrics for Recommendation - II.mp4 28.86 MB
  21. Evaluation Metrics for Recommendation - II.srt 8.79 KB
  21.1 Intra-List Similarity Calculation.pdf 240.26 KB
  22. Review Questions.html 144 B
  23. Model Deployment Criteria and Metrics.mp4 12.59 MB
  23. Model Deployment Criteria and Metrics.srt 4.82 KB
  24. Model Monitoring Metrics.mp4 26.85 MB
  24. Model Monitoring Metrics.srt 7.81 KB
  24.1 Population Stability Index Calculation.pdf 199.51 KB
  25. Data Flow Pipeline Metrics.mp4 24.6 MB
  25. Data Flow Pipeline Metrics.srt 9.04 KB
  26. Documentation Criteria.mp4 57.34 MB
  26. Documentation Criteria.srt 17.9 KB
  27. Review Questions.html 144 B
  3. Data Science Problem Formulation – An Overview.mp4 8 MB
  3. Data Science Problem Formulation – An Overview.srt 2.99 KB
  4. Data Science Problem Type - Classification.mp4 42.46 MB
  4. Data Science Problem Type - Classification.srt 9.75 KB
  5. Data Science Problem Type - Regression.mp4 19.53 MB
  5. Data Science Problem Type - Regression.srt 4.88 KB
  6. Data Science Problem Type - Clustering.mp4 23.8 MB
  6. Data Science Problem Type - Clustering.srt 5.45 KB
  7. Data Science Problem Type - Anomaly Detection.mp4 13.07 MB
  7. Data Science Problem Type - Anomaly Detection.srt 3.1 KB
  8. Data Science Problem Type - Association.mp4 14.39 MB
  8. Data Science Problem Type - Association.srt 3.01 KB
  9. Data Science Problem Type - Recommendation.mp4 16.43 MB
  9. Data Science Problem Type - Recommendation.srt 3.74 KB
  4. Situation Assessment
  1. Introduction.mp4 15.03 MB
  1. Introduction.srt 960 B
  10. Review Questions.html 144 B
  2. Situation Assessment - An Overview.mp4 11.71 MB
  2. Situation Assessment - An Overview.srt 4.17 KB
  3. Team Composition.mp4 25.75 MB
  3. Team Composition.srt 7.48 KB
  4. Resource Assessment.mp4 29.13 MB
  4. Resource Assessment.srt 10.14 KB
  5. Project Requirements, Assumptions & Constraints.mp4 20.83 MB
  5. Project Requirements, Assumptions & Constraints.srt 7.51 KB
  6. Review Questions.html 144 B
  7. Risk Assessment.mp4 40.7 MB
  7. Risk Assessment.srt 14.86 KB
  8. Terminology.mp4 12.02 MB
  8. Terminology.srt 2.69 KB
  9. Costs and Benefits.mp4 12.81 MB
  9. Costs and Benefits.srt 4.2 KB
  5. Project Scheduling
  1. Introduction.mp4 19.13 MB
  1. Introduction.srt 1.11 KB
  2. Scheduling - I.mp4 39.58 MB
  2. Scheduling - I.srt 9.23 KB
  2.1 Data Science Project Lifecycle - Phases, Activities & Deliverables.pdf 144.15 KB
  3. Scheduling - II.mp4 47.09 MB
  3. Scheduling - II.srt 17.49 KB
  4. Review Questions.html 144 B
  6. Emerging Methods
  1. Introduction.mp4 13.93 MB
  1. Introduction.srt 809 B
  2. Emerging Methods for Executing Data Science Projects.mp4 13.18 MB
  2. Emerging Methods for Executing Data Science Projects.srt 5.29 KB
  3. Microsoft Team Data Science Process (TDSP).mp4 36.87 MB
  3. Microsoft Team Data Science Process (TDSP).srt 10.08 KB
  4. Agile Data Science 2.0.mp4 50.83 MB
  4. Agile Data Science 2.0.srt 14.47 KB
  5. Review Questions.html 144 B
  7. Conclusion
  1. Introduction.mp4 9.97 MB
  1. Introduction.srt 574 B
  2. Recap of Key Points.mp4 27.39 MB
  2. Recap of Key Points.srt 9.33 KB
  3. Project Plan Review - Checkpoints.mp4 12.29 MB
  3. Project Plan Review - Checkpoints.srt 3.72 KB
  3.1 Project Plan Review Checkpoints.pdf 122.93 KB
  4. Preparing a Project Plan.html 144 B
  5. Closing Remarks.mp4 13.9 MB
  5. Closing Remarks.srt 4.36 KB
  6. Congratulations and Thanks.mp4 13.72 MB
  6. Congratulations and Thanks.srt 864 B

Description



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

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