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Fundamentals of Analytics > Module 2 > Flashcards

Module 2 Flashcards

Business and Organization Skills, Technical Skills, Workplace Skills

1
Q

How many skills are under Analytics Competencies?

A

Four

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2
Q

Four Categorized Skills of Analytics Competencies

A

Business and Organization Skills
Technical Skills
Workplace Skills

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3
Q

How many distinct competencies does Business and Organization Skills have?

A

Four

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4
Q

Four Distinct Competencies of Business and Organization Skills.

A
  • Domain Knowledge and Application
  • Data Management and Governance
  • Operational Analytics
  • Data Visualization and Presentation
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5
Q

How many distinct competencies does Technical Skills have?

A

Five

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6
Q

Five Distinct Competencies of Technical Skills.

A
  • Research Methods
  • Data Engineering Principles
  • Statistical Techniques
  • Data Analytics, Methods, and Algorithms
  • Computing
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7
Q

How many distinct competencies does Workplace Skills have?

A

One

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8
Q

How many Competencies are there overall?

A

Ten Competencies (Categorized into Four)

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9
Q

T or F

every competencies has three level
proficiency expectations.

A

True

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10
Q

What are the three levels of proficiency?

A

Entry Level
Immediate Level
Expert Level

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11
Q

What does Entry level proficiency do?

A

Perform tasks with Guidance

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12
Q

What does Immediate level proficiency do?

A

Formulate task to achieve Organizational Goals
and works independently.

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13
Q

What does Expert level proficiency do?

A

Identifying new approaches to achieve
Organizational Goals. Provides solution to a problem.

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14
Q

In the domain of Knowledge Application and Domain Expertise, one must have the following skills:

A
  • Domain-Related Knowledge
  • Insights to effectively contextualize data
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15
Q

These skills defined a Functional Analyst, it encompasses what?

A

industry knowledge, business experience, and domain expertise.

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16
Q

Comprehend the collected data, and grasp the methods by which they are managed and applied within the specific industry domain

A

Entry Level Domain Knowledge and Application

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17
Q

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.

A

Immediate Level Domain Knowledge and Application

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18
Q

Formulate compelling business cases aimed at enhancing domain-related
procedures by leveraging data-driven decision-making strategies.

A

Expert Level Domain Knowledge and Application

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19
Q

Competency needed for Functional Analysts

A

Domain Knowledge and Application

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20
Q

Competency needed for Data Stewards

A

Data Management and Governance

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21
Q

In the domain of Data Management and Governance, one must have the following skills:

A
  • Develop and Implement Data Management Strategies
  • Enforcing Privacy and Data Security
  • Implement Data Policies and Regulations
  • Understand Ethical Considerations
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22
Q

They are the Data
Gatekeepers of an organization.

A

Data Stewards

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23
Q
  • Maintain vigilant awareness and consistently implement policies and measures to
    uphold data security, privacy, intellectual property, and ethical standards.
A

Entry Level Data Management and Governance

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24
Q

Competency needed for Analytics Managers

A

Operational Analytics

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24
In the domain of Operational Analytics, one must have the following skills:
* General Knowledge of Business Analytics * Specialized Knowledge of Business Techniques * Insight Derivation for Decision-Making.
24
* Effectively implement and enforce policies and procedures pertaining to data security, privacy, intellectual property, and ethical considerations.
Immediate Level Data Management and Governance
25
* Formulate comprehensive policies addressing data security, privacy, intellectual property, and ethical considerations.
Expert Level Data Management and Governance
25
These skills defined an Analytics Manager as they have what skills?
Project Management Skills.
26
Conduct comprehensive business analysis on designated tasks and datasets.
Entry Level Operational Analytics
27
Determine the business implications arising
Immediate Level Operational Analytics
28
Discover fresh opportunities to leverage historical data for optimizing organizational processes.
Expert Level Operational Analytics
29
In the domain of Data Visualization and Presentation, one must have the following skills:
* Create and Communicate Compelling and Actionable Insights * Utilizing Data Visualization and Presentation
30
These data visualization techniques are not just about charts but about telling a story
Data-Storytelling
31
* Create data visualization reports or narratives according to specified requirements.
Entry Level Data Visualization and Presentation
32
* Design infographics to facilitate the effective presentation and communication of actionable outcomes.
Immediate Level Data Visualization and Presentation
33
* Choose suitable visualization methods and innovate new approaches tailored to a specific industry.
Expert Level Data Visualization and Presentation
34
In the domain of Research Methods, one must have the following skills:
* Utilize scientific and engineering methods * Discover and create new knowledge and insights
35
These skills defined a Data Scientist, it encompasses what?
strategies, processes, and techniques.
36
* Employ the 4-step research model, comprising hypothesis formulation, research methods selection, artifact creation, and evaluation, to enhance understanding and application in research endeavors.
Entry Level Research Methods
37
* Formulate research questions centered on identified issues within established research or business process models.
Immediate Level Research Methods
38
* Create experiments incorporating both passive and active data collection methods to facilitate hypothesis testing and effective problem-solving.
Expert Level Research Methods
39
In the domain of Data Engineering Principles, one must have the following skills:
* Utilize software and system engineering * Develop data analytics application
40
These skills defined a Data Engineer, it encompasses what?
ETL Method (Extract, Transform, Load).
41
Proficiency in programming selected SQL and NoSQL platforms for data storage and access, with a specific focus on writing Extract, Transform, Load (ETL) scripts.
Entry Level Data Engineering
42
* Architect and construct both relational and non-relational databases, ensuring the implementation of efficient Extract, Transform, Load (ETL) processes tailored for large datasets.
Immediate Level Data Engineering
43
* Demonstrated advanced expertise in leveraging modern Big Data technologies for processing diverse data types sourced from multiple channels.
Expert Level Data Engineering
44
In the domain of Statistical Techniques, one must have the following skills:
* Apply Statistical Concepts and Methodologies for data analysis
45
They are utilized to analyze raw data especially from a research data to extract information.
Mathematics and Statistics
46
Under Statistical Techniques these skills are also defined by Data Scientist, it encompasses what
Mathematics and Statistics
47
Possess proficiency in employing statistical methods, including sampling, ANOVA, hypothesis testing, descriptive statistics, regression analysis, and other relevant methodologies.
Entry Level Statistical Techniques
48
* Evaluate and recommend the most suitable statistical methods and tools tailored to specific tasks and datasets.
Immediate Level Statistical Techniques
49
* Recognize issues within collected data and propose corrective measures, encompassing additional data collection, inspection, and pre-processing as needed.
Expert Level Statistical Techniques
50
In the domain of Data Analytics, Method and Algorithms, one must have the following skills:
* Implement and Evaluate Machine Learning Methods and Algorithms * Deriving Insights from data for Decision-Making.
51
Under Data Analytics, Methods and Algorithms these skills are also defined by Data Scientist, it encompasses what?
Algorithm and Machine Learning.
52
They are utilized to identify the most appropriate methods or algorithms to extract insights from data.
Algorithm and Machine Learning.
53
* Illustrate comprehension of statistical hypothesis testing and proficiently conduct such tests, providing clear explanations regarding the statistical significance of collected data.
Entry Level Data Analytics, Methods and Algorithms
54
* Apply quantitative techniques, such as time series analysis, optimization, and simulation, to deploy suitable models for analysis and prediction.
Immediate Level Data Analytics, Methods and Algorithms
55
* Evaluate data reliability and appropriateness. Choose suitable approaches while considering their impact on analysis and the quality of results.
Expert Level Data Analytics, Methods and Algorithms
56
In the domain of Computing, one must have the following skills:
* Apply information technology and computational thinking. * Utilize programming languages for analysis. * Utilize software and hardware solutions also for analysis.
56
* Conduct fundamental data manipulation, analysis, and visualization tasks proficiently.
Entry Level Computing
57
* Utilize computational thinking to translate formal data models and algorithmic processes into program code.
Immediate Level Computing
58
* Choose suitable application and statistical programming languages, as well as development platforms, tailored to specific processes and datasets.
Expert Level Computing
59
There’s no really three level skill set as this is a necessary skill for any type of field. The following essential skills are included but not limited to:
* Critical Thinking * Communication * Collaboration * Creativity and Attitude * Planning and Organizing * Business Fundamentals * Customer Focus * Working with Tools and Technology * Dynamic (Self-) Re-Skilling * Professional Network * Ethics

Fundamentals of Analytics (3 decks)

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