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Questions and Answers
What is the primary responsibility of a data engineer?
What is the primary responsibility of a data engineer?
What role does a data scientist play in a project?
What role does a data scientist play in a project?
Which role is responsible for the project's genesis and core business problem?
Which role is responsible for the project's genesis and core business problem?
What is a key responsibility of a project manager?
What is a key responsibility of a project manager?
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Who benefits from the end results of the project and can consult on its value?
Who benefits from the end results of the project and can consult on its value?
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What is a key responsibility of a Data Engineer?
What is a key responsibility of a Data Engineer?
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Which of the following statements accurately describes the focus of the Data Analytics Lifecycle?
Which of the following statements accurately describes the focus of the Data Analytics Lifecycle?
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What is a distinguishing factor of data science projects compared to other analytic projects?
What is a distinguishing factor of data science projects compared to other analytic projects?
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Which role is responsible for determining if enough information exists to draft an analytic plan?
Which role is responsible for determining if enough information exists to draft an analytic plan?
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What essential element is required before building a model in data analytics?
What essential element is required before building a model in data analytics?
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Study Notes
Introduction to Data Science
- Data science course lecture 3
- Introduces the topic of data science
Data Analytics Lifecycle
- Focuses time effectively
- Ensures accuracy and completeness
- Enables smooth transitions within multi-functional analytics teams
- Supports scalability for additional analysts
- Aims to validate findings
Key of an Analytics Project
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Stakeholder: A person directly involved in a project or whose interests are affected by its completion (positively or negatively)
- Plays a crucial role in the project's success
- The number of people involved can vary based on factors such as project scope, organizational structure, and participant skills.
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Business User: A person with deep understanding of the domain, who benefits from project results
- Provides valuable insights and advice to the project team, especially in the context of their work
- Defines how outputs will be used operationally in the business
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Project Sponsor: The individual responsible for initiating the project
- Sets the direction, defines the core business problem, and provides funding
- Identifies the degree of value added in outputs of the project team, and establishes project priorities
Key Roles in an Analytics Project
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Project Manager: Oversees the project's timeline and quality
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Business Intelligence Analyst: Provides insights through business domain expertise, KPIs, key performance indicators and business intelligence.
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Database Administrator (DBA): Sets up the database environment
- Confirms the databases and tables accessibility for the project team
- Maintains proper security levels for data repositories
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Data Engineer: Supports data management & extraction using SQL.
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Data Scientist: Expertise in analytical techniques, data modeling, to address business problems and meet overall analytical goals.
Key Outputs from a Successful Analytic Project, by Role
- Business User: Benefits from project results or advice to the project team on deliverables
- Project Sponsor: Responsible for project initiation, core business problem, funding, establishes project priorities
- Project Manager: Ensures project milestones and objectives are met; ensuring quality.
- Business Intelligence Analyst: Deep domain expertise in data, KPIs, and business intelligence from a reporting perspective.
- Data Engineer: Supports data management, extraction, and data ingestion into the analytic sandbox
- Database Administrator (DBA): Provisions and configures the database for analytical needs
- Data Scientists: Provide subject-matter expertise for analytical techniques & data modeling. Delivers on analytical objectives.
Need For a Process to Guide Data Science Projects
- Well-defined processes help guide analytic projects.
- Data analytics projects focus on data, while business intelligence focuses on past performance.
- More consultative approach is required in data science projects.
Data Analytics Lifecycle. Phases
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Discovery: Gathering information, drafting analytic plan, and sharing with peers for review
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Data Prep: Assessing data quality to start building a model
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Model Planning: Identifying the type of model and refining the analytical plan
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Model Building: Building and refining the model
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Operationalize: Implementation of the model
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Communicate Results: Evaluating the model, and communicate results
Data Analytics Lifecycle Phase 1: Discovery
- Learn the Business Domain
- Define the amount of domain-specific knowledge needed to correctly analyze the data.
- Determine the type of data analysis (e.g., clustering, classification).
- Research the field being analyzed.
- Learn from the Past
- Determine if similar problems have been tackled before.
- Understand the reasons for previous failures to avoid repetition.
- Analyze how conditions/processes have changed.
- Resources: Assess available technology, data availability, team members, and project timeline. Assess if sufficient resources exist to carry-out the project. If not, then consider augmentation.
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Frame the Problem/ Framing: Define the problem to be tackled, its significance and to whom. Define stakeholders and their interests. Identify the current state and associated pain points. Specify business objectives and what needs to be done to achieve the objectives. Define the success criteria, associated risks, and key stakeholders.
- Interviewing Considerations: Be prepared, use Open-ended question; probe for detail, allow ample thinking time, clarify, actively listen, summarize and ask clarifying question. Avoid leading questions, and opinions.
Tips for Interviewing the Analytics Sponsor (part 1)
- Prepare questions in advance (with colleagues, team)
- Utilize open-ended questions.
- Probe for details; follow up.
Tips for Interviewing the Analytics Sponsor (part 2)
- Do not fill silent moments
- Allow thoughts to be expressed
- Seek clarifying questions.
Tips for Interview Questions for the Analytics Sponsor
- Focus on the analytics problem (problem to solve, desired outcome)
- Evaluate the scope/focus of the issue based on different influencing factors (time, people, financial resources, risk analysis, data attributes and size
- Seek the data source, industry impact or issues, and the project timeline.
- Identifying potential project leads and decision power.
Data Analytics Lifecycle Phase 1: Discovery (Additional)
- Formulate Initial Hypothesis: Formulate hypotheses, gathering and evaluating input from key stakeholders and domain experts.
- Identify Data Sources: Assemble data sources, providing a high-level understanding and exploring data.
- Review Raw Data: Review the structure of raw data, and determine necessary tools and needed scope for the project type.
Using a Sample Case Study to Track the Phases in the Data Analytics Lifecycle
- Retail bank, Yoyodyne Bank, seeks to enhance the Net Present Value (NPV) and customer retention rate.
- To reduce customer churn, and analyze reasons behind customer turnover for retention purposes
How to Frame an Analytics Problem
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Understand business problems and their qualifiers.
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Define Analytical Approach.
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Example use case in the Yoyodyne Bank. Churn predictions are required.
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Key influencing factors are identified.
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Description
This quiz covers key concepts from Lecture 3 of the Data Science course. It explores the data analytics lifecycle, the roles of stakeholders in analytics projects, and the importance of accuracy and scalability. Test your understanding of these critical components of data science.
