Save time, empower your teams and effectively upgrade your processes with access to this practical Data Science Teams Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Data Science Teams related project.
Download the Toolkit and in Three Steps you will be guided from idea to implementation results.
The Toolkit contains the following practical and powerful enablers with new and updated Data Science Teams specific requirements:
STEP 1: Get your bearings
- The latest quick edition of the Data Science Teams 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 999 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 Teams improvements can be made.
Examples; 10 of the 999 standard requirements:
- How do you accelerate the induction of supply chain management concepts proven for decades in capital investment and industrial engineering into data science and data management?
- Do teams think that using an improved / more consistent process would improve performance and if so, what methodologies might a team use to manage data science projects?
- What are the main program characteristics contributing to high ratings for quality that can be identified and replicated in future program design and development?
- How do other organizations embed data science across enterprise so that it can deliver the next level of organizational performance and return on investment?
- What are the current trends in data science that can develop robust measures to create a safer, more efficient, and higher-performing workplace?
- How does marketing analytics combine with research methods, statistics, and data science and how much of marketing analytics is marketing?
- What advice do you have for people who have both a social science and a computer science background and who want to go into data science?
- How can data across participants be used to increase statistical power by performing transfer learning across different individuals?
- What are the most valuable tools and the most valuable skills that someone should have if he/she wants to work in data science?
- Do you have the ability and desire to look at the massive amounts of data available and use it to predict or classify behavior?
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 Teams book in PDF containing 999 requirements, which criteria correspond to the criteria in...
Your Data Science Teams 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 Teams Self-Assessment and Scorecard you will develop a clear picture of which Data Science Teams 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 Teams 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 Teams projects with the 62 implementation resources:
- 62 step-by-step Data Science Teams Project Management Form Templates covering over 1500 Data Science Teams project requirements and success criteria:
Examples; 10 of the check box criteria:
- Communications Management Plan: How is this initiative related to other portfolios, programs, or Data Science Teams projects?
- Probability and Impact Assessment: Assuming that you have identified a number of risks in the Data Science Teams project, how would you prioritize them?
- Monitoring and Controlling Process Group: Is there sufficient time allotted between the general system design and the detailed system design phases?
- Responsibility Assignment Matrix: Evaluate the performance of operating organizations?
- Risk Audit: Does the customer have a solid idea of what is required?
- Human Resource Management Plan: Are the Data Science Teams project team members located locally to the users/stakeholders?
- Activity Duration Estimates: Does a procedure exist to ensure the Data Science Teams project work is completed in the appropriate sequence and on time?
- Quality Metrics: How do you know if everyone is trying to improve the right things?
- Change Management Plan: Is there an adequate supply of people for the new roles?
- Probability and Impact Assessment: Is the number of people on the Data Science Teams project team adequate to do the job?
Step-by-step and complete Data Science Teams Project Management Forms and Templates including check box criteria and templates.
1.0 Initiating Process Group:
- 1.1 Data Science Teams project Charter
- 1.2 Stakeholder Register
- 1.3 Stakeholder Analysis Matrix
2.0 Planning Process Group:
- 2.1 Data Science Teams 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 Teams 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 Teams 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 Teams 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 Teams project or Phase Close-Out
- 5.4 Lessons Learned
With this Three Step process you will have all the tools you need for any Data Science Teams project with this in-depth Data Science Teams Toolkit.
In using the Toolkit you will be better able to:
- Diagnose Data Science Teams 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 Teams 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 Teams investments work better.
This Data Science Teams All-Inclusive Toolkit enables You to be that person.
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.