Save time, empower your teams and effectively upgrade your processes with access to this practical Data Science Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Data Science related project.

Download the Toolkit and in Three Steps you will be guided from idea to implementation results.

 

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The Toolkit contains the following practical and powerful enablers with new and updated Data Science specific requirements:

STEP 1: Get your bearings

Start with…

  • The latest quick edition of the Data Science Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.

Organized in a data driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…

  • Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation

Then find your goals…

STEP 2: Set concrete goals, tasks, dates and numbers you can track

Featuring 639 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Science improvements can be made.

Examples; 10 of the 639 standard requirements:

  1. Academia v s industry which environment is better suited for acquiring skills in data science e g data cleaning mining analysis visualization?

  2. What would happen to the business if the analytics/data science/data mining function disappered overnight ?

  3. How would you define data science and data scientists and distinguish it from older related terms?

  4. What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

  5. Is it sensible to pursue higher education in purely business analysis and data science?

  6. What tools do you find the most useful for data mining data analysis I e data science?

  7. How should data science be measured, managed, planned?

  8. Where should data science sit in the business?

  9. How to compute results with a predefined privacy budget?

Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:

  • The workbook is the latest in-depth complete edition of the Data Science book in PDF containing 639 requirements, which criteria correspond to the criteria in…

Your Data Science self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:

  • The Self-Assessment Excel Dashboard; with the Data Science Self-Assessment and Scorecard you will develop a clear picture of which Data Science areas need attention, which requirements you should focus on and who will be responsible for them:

    • Shows your organization instant insight in areas for improvement: Auto generates reports, radar chart for maturity assessment, insights per process and participant and bespoke, ready to use, RACI Matrix
    • Gives you a professional Dashboard to guide and perform a thorough Data Science Self-Assessment
    • Is secure: Ensures offline data protection of your Self-Assessment results
    • Dynamically prioritized projects-ready RACI Matrix shows your organization exactly what to do next:

 

STEP 3: Implement, Track, follow up and revise strategy

The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage Data Science projects with the 62 implementation resources:

  • 62 step-by-step Data Science Project Management Form Templates covering over 6000 Data Science project requirements and success criteria:

Examples; 10 of the check box criteria:

  1. Quality Management Plan: What changes can you make that will result in improvement?
  2. Procurement Management Plan: Have all involved Data Science project stakeholders and work groups committed to the Data Science project?
  3. Stakeholder Analysis Matrix: What are the key services, contractual arrangements, or other relationships between stakeholder groups?
  4. Executing Process Group: Is the Data Science project making progress in helping to achieve the set results?
  5. Assumption and Constraint Log: How are new requirements or changes to requirements identified?
  6. Team Operating Agreement: Have you established procedures that team members can follow to work effectively together, such as a team operating agreement?
  7. Assumption and Constraint Log: Are best practices and metrics employed to identify issues, progress, performance, etc.?
  8. Human Resource Management Plan: Is the manpower level sufficient to meet the future business requirements?
  9. Planning Process Group: Just how important is your work to the overall success of the Data Science project?
  10. Stakeholder Analysis Matrix: Who is directly responsible for decisions on issues important to the Data Science project?

 
Step-by-step and complete Data Science Project Management Forms and Templates including check box criteria and templates.

1.0 Initiating Process Group:

  • 1.1 Data Science project Charter
  • 1.2 Stakeholder Register
  • 1.3 Stakeholder Analysis Matrix

2.0 Planning Process Group:

  • 2.1 Data Science project Management Plan
  • 2.2 Scope Management Plan
  • 2.3 Requirements Management Plan
  • 2.4 Requirements Documentation
  • 2.5 Requirements Traceability Matrix
  • 2.6 Data Science project Scope Statement
  • 2.7 Assumption and Constraint Log
  • 2.8 Work Breakdown Structure
  • 2.9 WBS Dictionary
  • 2.10 Schedule Management Plan
  • 2.11 Activity List
  • 2.12 Activity Attributes
  • 2.13 Milestone List
  • 2.14 Network Diagram
  • 2.15 Activity Resource Requirements
  • 2.16 Resource Breakdown Structure
  • 2.17 Activity Duration Estimates
  • 2.18 Duration Estimating Worksheet
  • 2.19 Data Science project Schedule
  • 2.20 Cost Management Plan
  • 2.21 Activity Cost Estimates
  • 2.22 Cost Estimating Worksheet
  • 2.23 Cost Baseline
  • 2.24 Quality Management Plan
  • 2.25 Quality Metrics
  • 2.26 Process Improvement Plan
  • 2.27 Responsibility Assignment Matrix
  • 2.28 Roles and Responsibilities
  • 2.29 Human Resource Management Plan
  • 2.30 Communications Management Plan
  • 2.31 Risk Management Plan
  • 2.32 Risk Register
  • 2.33 Probability and Impact Assessment
  • 2.34 Probability and Impact Matrix
  • 2.35 Risk Data Sheet
  • 2.36 Procurement Management Plan
  • 2.37 Source Selection Criteria
  • 2.38 Stakeholder Management Plan
  • 2.39 Change Management Plan

3.0 Executing Process Group:

  • 3.1 Team Member Status Report
  • 3.2 Change Request
  • 3.3 Change Log
  • 3.4 Decision Log
  • 3.5 Quality Audit
  • 3.6 Team Directory
  • 3.7 Team Operating Agreement
  • 3.8 Team Performance Assessment
  • 3.9 Team Member Performance Assessment
  • 3.10 Issue Log

4.0 Monitoring and Controlling Process Group:

  • 4.1 Data Science project Performance Report
  • 4.2 Variance Analysis
  • 4.3 Earned Value Status
  • 4.4 Risk Audit
  • 4.5 Contractor Status Report
  • 4.6 Formal Acceptance

5.0 Closing Process Group:

  • 5.1 Procurement Audit
  • 5.2 Contract Close-Out
  • 5.3 Data Science project or Phase Close-Out
  • 5.4 Lessons Learned

 

Results

With this Three Step process you will have all the tools you need for any Data Science project with this in-depth Data Science Toolkit.

In using the Toolkit you will be better able to:

  • Diagnose Data Science projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices
  • Implement evidence-based best practice strategies aligned with overall goals
  • Integrate recent advances in Data Science and put process design strategies into practice according to best practice guidelines

Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, ‘What are we really trying to accomplish here? And is there a different way to look at it?’

This Toolkit empowers people to do just that – whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc… – they are the people who rule the future. They are the person who asks the right questions to make Data Science investments work better.

This Data Science All-Inclusive Toolkit enables You to be that person:

 

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Includes lifetime updates

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

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