Identify and document security vulnerabilities and weaknesses in the environment as unauthorized access potential, non compliance with defined standards, etc.

More Uses of the Data Science Toolkit:

• Develop and apply application architecture methodologies, standards and leading practices.
  
  
• Secure that your organization stays up to date on new technologies and methods across data science and data visualizations.
  
  
• Arrange that your organization creates and updates standard operating procedures and reports out to management on efficiency gains.
  
  
• Confirm your organization updates security plans resulting from application changes or hardware, software, or network modifications.
  
  
• Guide: work closely with infrastructure team members, project managers, vendors, leadership, system owners, and operations/support staff.
  
  
• Develop and/or maintain expertise in identifying security risks in the hardware, software, and systems used by your organization.
  
  
• Assure your organization demonstrates expertise in the field of data science and performs tasks utilizing statistics, machine learning and data modeling.
  
  
• Ensure you understand, document, and communicate timelines, priorities, delivery, and measurement across technology and supply chain clients.
  
  
• Ensure you can provide technical expertise in the areas of architecture, design, implementation, and testing.
  
  
• Manage: superior analytical skills with diverse analytics and statistical software and applications.
  
  
• Establish that your organization applications are accepted year round, with multiple start dates organized around orientation trainings.
  
  
• Standardize: work closely with software developers to resolve issues identified during design review and testing.
  
  
• Establish that your organization validates and tests security architecture and design solutions to recommended vendor technologies.
  
  
• Govern: work in a team environment to deliver high quality data science analysis that supports marketing efforts and outside team needs.
  
  
• Supervise: faithfully translate your organization mission and core values into everyday tasks and projects.
  
  
• Control: work closely with development teams to ensure that platforms are designed with operability in mind.
  
  
• Manage work with your Data Warehouse, Data Science, and Product teams to ensure that you have high quality data that meets the needs of the business.
  
  
• Develop: conduct projects from early stage research through development, in consultation with stakeholders.
  
  
• Govern: constantly evaluate the test automation strategy and approach to identify areas of improvement.
  
  
• Have designed in collaboration with BI team a set of Pricing KPI reports to monitor ongoing progress in pricing goals.
  
  
• Ensure you can go toe to toe with a development engineer and handle the push back from the developer.
  
  
• Introduce and advocate for industry best practices of a modern data warehouse as customer centric, adaptable, automated, elastic, governed, secure, etc.
• Provide insights into machine learning techniques focused on classification, supervised learning, reinforced learning and eventually deep learning.
  
  
• Ensure your organization aims to exploit data and discover information, recognize patterns and relationships and predict future behavior and performance.
  
  

 

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.



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 994 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 994 standard requirements:




1. How does your financial organization build a data science platform for fraud prevention that supports agility by allowing analysts to build and bring own models?
   
   

2. Do the employees authorized to handle data know when there is an outlier and how to engage the necessary process to adhere to data handling practices?
   
   

3. Is there an established communication channel between your team and your organization who has requested the development of the data solution?
   
   

4. Have you empowered the appropriate legal, privacy and compliance controls to allow for the necessary employees are empowered to acquire data?
   
   

5. What challenges, if any, has your organizations IT/infrastructure team had supporting the data science team/data science-focused individuals?
   
   

6. How can a combination of social network and other customer data be used to forecast customer behaviour in a telecoms environment?
   
   

7. Should big data and data science be a new discipline, or should it be seen as a complementary skill set for other disciplines?
   
   

8. Is your organization intending to pursue cloud-enabled providers, or is there a preference for maintaining data on-premise?
   
   

9. Do you have a centralized team responsible for establishing advanced analytic and modeling standards in your organization?
   
   

10. Is there a requirement for data from the analytics environment to be sent back to the originating applications?
    
    




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 994 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 1500 Data Science project requirements and success criteria:




Examples; 10 of the check box criteria:




1. Lessons Learned: Was there a Data Science project Definition document. Was there a Data Science project Plan. Were they used during the Data Science project?
   
   

2. Cost Baseline: Pcs for your new business. what would the life cycle costs be?
   
   

3. Duration Estimating Worksheet: Will the Data Science project collaborate with the local community and leverage resources?
   
   

4. Probability and Impact Assessment: What are the channels available for distribution to the customer?
   
   

5. Stakeholder Management Plan: What is the process for purchases that arent acceptable (eg damaged goods)?
   
   

6. Project Charter: Environmental stewardship and sustainability considerations: what is the process that will be used to ensure compliance with the environmental stewardship policy?
   
   

7. Procurement Audit: What are your ethical guidelines for public procurement?
   
   

8. Team Member Performance Assessment: How is the timing of assessments organized (e.g., pre/post-test, single point during training, multiple reassessment during training)?
   
   

9. Quality Metrics: How do you communicate results and findings to upper management?
   
   

10. Planning Process Group: Do the partners have sufficient financial capacity to keep up the benefits produced by the programme?




 
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.



 



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.