Arrange that your organization as the authoritative source of key data sets, you are at the forefront of database technology and are heavily involved in Data Engineering, Data Science, Data Visualization, and MachinE Learning efforts across your organization.
More Uses of the Data Science Toolkit:
- Contribute to ongoing expansion of Data Science expertise and credentials by keeping up with industry Best Practices, developing new skills, and Knowledge Sharing.
- Guide: work hand in hand with your Data Science, Data Engineering, and visualization teams to translatE Business needs into comprehensive Technical Requirements.
- Collaborate with product, engineering, design, and Data Science Teams to capture feedback and requirements and better understand the future of the product.
- Facilitate meetings with Engineering, Product, Data Science, and other stakeholders to capture project requirements, action items, and next steps.
- Pilot: work closely with various cross functional teams at one concern (solution, Data Science, engineering, Product Management, and business development) to build innovative and practical solutions and Product Offerings.
- Collaborate with product, engineering, design, and Data Science Teams to gather feedback and requirements and better understand the future of the product.
- Make sure that your corporation complies; clients rely on you for Talent Acquisition to hire quality permanent and contract Data Science, Big Data, Risk And Compliance, Artificial intelligence and IT Professionals.
- Ensure you control; build relationships and collaborate with cross functional teams as Product Management, Engineering, Data Science, Operations and Marketing.
- Be accountable for harnessing Data Driven MachinE Learning and Artificial intelligence to provide a capability that can be pervasively applied across the enterprise building a Data Science competency function for your organization.
- Confirm you enforce; lead building of advanced Data Science/MachinE Learning models to create significant Business Impact for clients and ensure Client Satisfaction.
- Steer: partner with stakeholders (science, product, engineering) to support the execution of data intensive projects, enable superior Decision Making and analysis, and standardize Data Science practices throughout your organization.
- Ensure you contribute; lead a Data Science Team and build MachinE Learning models through all phases of development, from design, testing, Data Gathering, training, evaluation, validation, and implementation.
- Apply Statistical Modeling techniques as linear regression, Logistic Regression, Time Series Analysis, and even Data Science techniques where appropriate.
- Provide Technical Support and guidance to Data Science Teams in the successful delivery of client projects that result in repeat work.
- Formulate: Best In Class modelling/Data Science highly skilled analytic consultants customized solutions that help your clients to differentiate and win.
- Create new Data Science analytic methods or modify current methods ( as MachinE Learning) and use current technology to achieve the desired results.
- Be certain that your enterprise complies; Solutions Support enterprise Information Management, Master Data management, Business Intelligence, MachinE Learning, Data Science, and other business interests.
- Manage work with other members of the Data Science Team to establish a Center Of Excellence and create Standard Operating Procedures, accelerators, and sales assets.
- Collaborate with is (data engineers, Data Architects, and data analysts) in model production and deployment of Data Science solutions when automatically integrated into Business Processes.
- Ensure you bolster; lead the delivery of a broad range of Data, Analytics, Advanced Analytics, Visualization, Data Science, RPA and AI related Strategy / Advisory engagements.
- Serve as a Data Science expert in the analysis and classification of Public Health surveillance, research, and administrative Health Data.
- Be an analytics thought leader and a brainstorm partner for stakeholders across your organization, translating Business Needs and problems into Data Science and BI solutions.
- Ensure you foster; lead the entire End To End Data Science process from ETL to building models, to deploying the model into workflows to measuring the financial impact.
- Lead: partner with Product Management, content design, design, Data Science, engineering, marketing, and other technical roles to conduct and share research.
- Warrant that your enterprise provides professional support to staff or department members in defining the project and applying principals of Data Science in manipulation, statistical applications, programming, analysis and modeling.
- Manage a vibrant, diverse team of insurance experts, software and Data Science engineers and Security Professionals on an active mission to reinvent how businesses manage Cyber Risks and Cyber Insurance.
- Direct: partner with Business Stakeholders to adapt the Data Lake to meet the needs of analytical reporting, Data Science, Data Governance policies, and represent data as enterprise assets.
- Ensure you build; lead with expertise in advanced Data Science methods as Artificial intelligence, MachinE Learning, natural Language Processing, data linkage, Predictive Analytics.
- Manage: creatively inCorporate Data Science and Statistical Methods to the challenges of Product Development, understanding how science impacts end User Behavior.
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
- 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 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 improvements can be made.
Examples; 10 of the 999 standard requirements:
- What tools do you use once you have decided on a Data Science strategy and more importantly how do you choose?
- What is your organizations system for selecting qualified vendors?
- How can you best use all of your knowledge repositories to enhancE Learning and sharing?
- How do you verify your resources?
- Are accountability and ownership for Data Science clearly defined?
- What are thE Business goals Data Science is aiming to achieve?
- Are there any activities that you can take off your to do list?
- Why is Data Science important for you now?
- Are your outputs consistent?
- Which measures and indicators matter?
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:
- Cost Management Plan: Eac -estimate at completion, what is the total job expected to cost?
- Activity Cost Estimates: In which phase of the Acquisition Process cycle does source qualifications reside?
- Project Scope Statement: Will all Data Science project issues be unconditionally tracked through the Issue Resolution process?
- Closing Process Group: Did the Data Science Project Team have enough people to execute the Data Science Project Plan?
- Source Selection Criteria: What are the guidelines regarding award without considerations?
- Scope Management Plan: Are Corrective Actions taken when actual results are substantially different from detailed Data Science Project Plan (variances)?
- Initiating Process Group: During which stage of Risk planning are risks prioritized based on probability and impact?
- Cost Management Plan: Is your organization certified as a supplier, wholesaler, regular dealer, or manufacturer of corresponding products/supplies?
- Procurement Audit: Was a formal review of tenders received undertaken?
- Activity Cost Estimates: What procedures are put in place regarding bidding and cost comparisons, if any?
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
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.