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within analytics _________, four categorized skills are outlined as follows: business & organization skills, technical skills, workplace skills
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enumerate the 4 categorized skills in analytics competencies
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ANALYTICS MIDTERMS
| Question | Answer |
|---|---|
| within analytics _________, four categorized skills are outlined as follows: business & organization skills, technical skills, workplace skills | competencies |
| enumerate the 4 categorized skills in analytics competencies | business & organization skills, technical skills, and workplace skills (BTW) |
| what is the only one distinct competency in the realm of workplace skills? | 21st Century Skills |
| overall there are how many distinct competencies in the realm of data science skills? | 10 |
| enumerate the 3 levels of proficiency expectations | entry level, immediate level, and expert level (EIE) |
| this level of proficiency expectation is defined as: "Perform tasks with guidance and supervision" | entry level |
| this level of proficiency expectation is defined as: "Formulate task to achieve organizational goals and works independently" | immediate level |
| this level of proficiency expectation is defined as: "Identifying new approaches to achieve organizational goals. Provides solution to a problem" | expert level |
| enumerate the 4 distinct competencies in the realm of business & organization skills and their respective professions | Domain Knowledge and Application (Functional Analyst), Data Management and Governance (Data Steward), Operational Analytics (Analytics Manager), Data Visualization and Presentation (Data Storytelling) (D-KA, D-MG, O-A, D-VP) |
| in this domain, one must have the following skills: domain-related knowledge, and insights to effectively contextualize data | domain knowledge and application |
| the skills defined in this domain defines a functional analyst, as it encompasses industry knowledge, business experience, and domain expertise | domain knowledge and application |
| identify the domain and the level: "Comprehend the collected data, and grasp the methods by which they are managed and applied within the specific industry domain" | domain knowledge and application, entry level |
| identify the domain and the level: "Craft a comprehensive content strategy and design an effective information architecture tailored to support the unique needs of a given industry domain and its diverse audiences" | domain knowledge and application, immediate level |
| identify the domain and the level: "Formulate compelling business cases aimed at enhancing domain-related procedures by leveraging data-driven decision-making strategies" | domain knowledge and application, expert level |
| in this domain, one must have the following skills: develop & implement data management strategies, enforcing privacy & data security, implement data policies & regulations, and understand ethical considerations | data management and governance |
| the skills defined in this domain defines a data steward, as they are the data gatekeepers of an organization | data management and governance |
| identify the domain and the level: "Maintain vigilant awareness and consistently implement policies and measures to uphold data security, privacy, intellectual property, and ethical standards" | data management and governance, entry level |
| identify the domain and the level: "Effectively implement and enforce policies and procedures pertaining to data security, privacy, intellectual property, and ethical considerations" | data management and governance, immediate level |
| identify the domain and the level: "Formulate comprehensive policies addressing data security, privacy, intellectual property, and ethical considerations" | data management and governance, expert level |
| in this domain, one must have the following skills: general knowledge of business analytics, specialized knowledge of business techniques, and insight derivation for decision-making | operational analytics |
| the skills defined in this domain defines an analytics manager, as they have the project management skills | operational analytics |
| identify the domain and the level: "Conduct comprehensive business analysis on designated tasks and datasets" | operational analytics, entry level |
| identify the domain and the level: "Determine the business implications arising from identified trends and patterns" | operational analytics, immediate level |
| identify the domain and the level: "Discover fresh opportunities to leverage historical data for optimizing organizational processes" | operational analytics, expert level |
| in this domain, one must have the following skills: create & communicate compelling & actionable insights, and utilizing data visualization and presentation | data visualization and presentation |
| this is the term referring to how these data visualization techniques are not just about charts but about telling a story | data storytelling |
| identify the domain and the level: "Create data visualization reports or narratives according to specified requirements" | data visualization and presentation, entry level |
| identify the domain and the level: "Design infographics to facilitate the effective presentation and communication of actionable outcomes" | data visualization and presentation, immediate level |
| identify the domain and the level: "Choose suitable visualization methods and innovate new approaches tailored to a specific industry" | data visualization and presentation, expert level |
| enumerate the 5 distinct competencies in technical skills and their respective professions | Research Methods (Data Scientist), Data Engineering Principles (Data Engineer), Statistical Techniques (Data Scientist), Data Analytics/Methods/Algorithms (Data Scientist), and Computing (Data Scientist/Engineer) (RDCDS) |
| in this domain, one must have the following skills: utilize scientific and engineering methods, and discover and create new knowledge and insights | research methods |
| the skills defined in this domain defines a data scientist, as it encompasses strategies, processes, and techniques | research methods |
| they are utilized in collection of data to uncover new information | data scientist (domain: research methods) |
| identify the domain and the level: "Employ the 4-step research model, comprising hypothesis formulation, research methods selection,artifact creation, and evaluation, to enhance understanding and application in research endeavors" | research methods, entry level |
| identify the domain and the level: "Formula research questions centered on identified issues within established research or business process models" | research methods, immediate level |
| identify the domain and the level: "Create experiments incorporating both passive and active data collection methods to facilitate hypothesis testing and effective problem solving" | research methods, expert level |
| in this domain, one must have the following skills: utilize software and system engineering, and develop data analytics application | data engineering principles |
| the skills defined in this domain defines a data engineer, as it encompasses the ETL method (Extract, Transform, Load) | data engineering principles |
| they are the ones who bring all the data in one repository | data engineer (domain: data engineering principles) |
| identify the domain and the level: "Proficiency in programming selected SQL and NoSQL platforms for data storage and access with a specific focus on writing Extract, Transform, Load (ETL) scripts" | data engineering principles, entry level |
| identify the domain and the level: "Architect and construct both relational and non-relational databases, ensuring the implementation of efficient Extract, Transform, Load (ETL) processes tailored for large datasets" | data engineering principles, immediate level |
| identify the domain and the level: "Demonstrated advanced expertise in leveraging modern Big Data technologies for processing diverse data types sourced from multiple chanels" | data engineering principles, expert level |
| in this domain, one must have the following skills: apply statistical concepts and methodologies for data analysis | statistical techniques |
| the skills defined in this domain defines a data scientist, as it encompasses Mathematics and Statistics | statistical techniques |
| they are utilized to analyze raw data especially from a research data to extract information | data scientist (domain: statistical techniques) |
| identify the domain and the level: "Possess proficiency in emploting statistical methods, including sampling, ANOVA, hypothesis testing, descriptive statistics, regression analysis, and other relevant methodologies" | statistical techniques, entry level |
| identify the domain and the level: "Evaluate and recommend the most suitable statistical methods and tools tailored to specific tasks and datasets" | statistical techniques, immediate level |
| identify the domain and the level: "Recognize issues within collected data and propose corrective measures, encompassing additional data collection, inspection, and pre-processing as needed" | statistical techniques, expert level |
| in this domain, one must have the following skills: implement and evaluate machine learning methods and algorithms, and deriving insights from data for decision-making | data analytics/methods/algorithms |
| the skills defined in this domain defines a data scientist, as it encompasses Algorithm and Machine Learning | data analytics/methods/algorithms |
| they are utilized to identify the most appropriate methods or algorithms to extract insights from data | data scientist (domain: data analytics/methods/algorithms) |
| identify the domain and the level: "Illustrate comprehension of statistical hypothesis testing and proficiently conduct such tests, providing clear explanations regarding the statistical significance of collected data" | data analytics/methods/algorithms, entry level |
| identify the domain and the level: "Apply quantitative techniques, such as time series analysis, optimization, and simulation, to deploy suitable models for analysis and prediction" | data analytics/methods/algorithms, immediate level |
| identify the domain and the level: "Evaluate data reliability and appropriateness. Choose suitable approaches while considering their impact on analysis and the quality of results" | data analytics/methods/algorithms, expert level |
| in this domain, one must have the following skills: apply information technology and computational thinking, utilize programming languages for analyxis, and utilize software and hardware solutions also for analysis | computing |
| the skills defined in this domain defines both a data scientist and a data engineer | computing |
| identify the domain and the level: "Conduct fundamental data manipulation, analysis, and visualization tasks proficiently" | computing, entry level |
| identify the domain and the level: "Utilize computational thinking to translate formal data models and algorithmic processes into program code" | computing, immediate level |
| identify the domain and the level: "Choose suitable application and statistical programming languages, as well as development platforms, tailored to specific processes and datasets" | computing, expert level |
| enumerate the 4 essential 21st century skills | critical thinking, communication, collaboration, and creativity/attitude (4Cs) |
| enumerate the other 7 21st century skills | planning & organizing, business fundamentals, customer focus, working with tools/technology, dynamic (self-) re-skilling, professional network, and ethics (PWD B CPE) |
| they are conceivable as the new resource in this digital world | data |
| this is the process of refinement of data | analytics |
| a complete process of creating, collecting, processing, analyzing, and extracting value from data within an organization | data value chain |
| it covers the entire lifecycle of data | data value chain |
| a stage in data value chain that involves the creation of data which can be generated from biodata, machine logs, and so on | data stage |
| most generated data originates from _________, as data creation fundamentally relies on human involvement | human activity |
| a stage in data value chain that involves the extraction and consolidation of data where it is stored into a single repository | information stage |
| data creation and generation falls on what DVC stage? | data stage |
| data cleaning, categorization, transformation, and aggregation falls on what DVC stage? | information stage |
| this stage enables organizations to answer the question, "What happened?" | information stage |
| data analysis falls on what DVC stage? | insights stage |
| a stage in data value chain that involves finding patterns and trends | insights stage |
| using consolidated information, patterns are uncovered to address "Why did it happen?" and "What is likely to happen next?" in this stage | insights stage |
| a stage in data value chain that involves translating the analyzed data into practical imperatives and recommendations for future actions | imperatives stage |
| in this stage, organizations can now take decisive actions informed by analyzed data in which addresses a pivotal question, "What steps should be taken next?" | imperatives stage |
| enumerate the 4 disciplines in the data stage of DVC | Data Governance, Data Management, Data Security, Data Ethics (GEMS) |
| a discipline on policies for quality and compliance | data governance |
| a discipline on end-to-end oversight of data processes | data management |
| a discipline on safeguarding data from unauthorized access | data security |
| a discipline on responsible and ethical data handling | data ethics |
| enumerate the 4 disciplines in the information stage of DVC | Data Engineering, Data Warehousing, Data Architecture, Business Intelligence (WEA-BI) |
| a discipline on building data systems | data engineering |
| a discipline on managing structured data | data warehousing |
| a discipline on designing data systems | data architecture |
| a discipline on extracting insights for decisions for businesses | business intelligence |
| which DVC stage is integral to descriptive analytics? | information stage |
| this is responsible for summarizing historical data | descriptive analytics |
| enumerate the 3 disciplines in the insights stage of DVC | Data Mining, Algorithms, Machine Learning (MAD) |
| a discipline on extracting insights in general | data mining |
| a discipline on processing data steps | algorithms |
| a discipline on learning and improving from experience | machine learning |
| which DVC stage is integral to diagnostic and predictive analytics? | insights stage |
| this is responsible for analyzing data why an event occurred | diagnostic analytics |
| this is responsible for identifying future events based on historical data | predictive analytics |
| enumerate the 2 disciplines in the imperatives stage of DVC | Optimization, Simulation (OS) |
| a discipline on enhancing efficiency | optimization |
| a discipline on modeling real-world scenarios | simulation |
| this is responsible for recommending actions derived from descriptive & predictive analyses | prescriptive analytics |
| enumerate the 5 professions in DVC and their corresponding stages | Data Steward (Data), Data Engineer (Information), Data Scientist (Insights), Functional Analyst (Imperatives), Analytics Manager (Oversees All Stages) |
| a profession whose responsibilities are to develop, enforce, and maintain | data steward |
| additionally, this profession must also involve with data security and data usage | data steward |
| what are the 2 domains of data stewards? | Business, Industry Domains (B-ID) |
| typically, data stewards are also called as _____ as they are responsible of keeping the data and maintaining it into its quality | data gatekeepers |
| the jobs data privacy officer, data security officer, data governance manager, data curator, and data librarian are under what DVC profession? | data steward |
| a profession whose responsibilities are to design, construct, test, and maintain | data engineer |
| this profession is typically involved with ETL (Extract, Transform, Load) | data engineer |
| what are the 3 domains of data engineers? | IT, IS, CS (IIC) |
| typically, data engineers also work with ____________, which is where the transformed data are stored | data repositories |
| the jobs ETL developer, data architect, data warehousing professional, and big data engineer are under what DVC profession? | data engineer |
| a profession whose responsibilities are statistical techniques and creating statistical models | data scientist |
| this profession is involved with applying trends, patterns (current / historical) to make data-driven predictions | data scientist |
| what are the 2 domains of data scientists? | Mathematics, Statistics (SM) |
| the jobs statistician, statistical modeler, and advanced analytics professional are under what DVC profession? | data scientist |
| a profession whose responsibilities are to utilize data and leverage derived insights | functional analyst |
| this profession validates the insights of the data scientist | functional analyst |
| what are the 2 domains of functional analysts? | Business, Industry Domains (B-ID) |
| this profession is responsible for crafting the definitive prescriptions that outline the necessary actions for their clients or stakeholders | functional analyst |
| the jobs research analyst, HR analyst, marketing analyst, financial analyst, and operations analyst are under what DVC profession? | functional analyst |
| a profession whose responsibilities are to develop and guide data-driven projects | analytics manager |
| this profession includes heavily with Project Management, which includes the Initiation, Planning, Execution, Monitoring and Closure | analytics manager |
| enumerate the 5 steps of project management | Initiation, Planning, Execution, Monitoring, Closure (IPEMC) |
| what is the domain of analytic managers? | Project Management |
| the jobs chief data officer, project manager, data engineering manager, data science manager, and analytics translator are under what DVC profession? | analytics manager |
| analytics helps organizations to provide data-driven decisions; this is why Analytics are called ___________ | Decision Support System |
| in the end, the _______ has the final say whether to act upon them or not | end user |
| enumerate the 4 processes which the data becomes information | Data Cleaning, Data Categorization, Data Transformation, Data Aggregation |
| data creation/generation is associated with what stage in the DVC? | data stage |
| data gathering is associated with what stage in the DVC? | information stage |
| data analysis is associated with what stage in the DVC? | insights stage |
| decision making is associated with what stage in the DVC? | imperatives stage |
| enumerate the 5 related jobs for data steward | Data Privacy Officer, Data Security Officer, Data Governance Manager, Data Curator, Data Librarian (PSGCL) |
| enumerate the 4 related jobs for data engineer | Big Data Engineer, ETL Developer, Data Architect, Data Warehousing Professional (BEDD) |
| enumerate the 3 related jobs for data scientist | Advanced Analytics Professional, Statistician, Statistical Modeler (ASS) |
| enumerate the 5 related jobs for functional analyst | Operations Analyst, Financial Analyst, HR Analyst, Research Analyst, Marketing Analyst (OF HRM) |
| enumerate the 5 related jobs for analytics manager | Chief Data Officer, Project Manager, Data Engineering Manager, Analytics Translator, Data Science Manager (CP DAD) |
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