Data Analytics Foundations of Data Analytics Syllabus

Foundations of Data Analytics syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Foundations of Data Analytics in Data Analytics, not a summary of it.

1. 1

   Introduction to Data Analytics

   4 topics

   • What Is Data Analytics
   • Roles in Analytics
   • Data-Driven Decision Making
   • Analytics Maturity Models
2. 2

   Types of Analytics

   4 topics

   • Descriptive Analytics
   • Diagnostic Analytics
   • Predictive Analytics
   • Prescriptive Analytics
3. 3

   The Data Analysis Lifecycle

   6 topics

   • Ask: Defining the Problem
   • Prepare: Collecting Data
   • Process: Cleaning Data
   • Analyze: Finding Patterns
   • Share: Communicating Insights
   • Act: Driving Decisions
4. 4

   Data Types and Sources

   5 topics

   • Structured, Semi-Structured, Unstructured Data
   • Qualitative vs Quantitative Data
   • Data Collection Methods
   • First-Party, Second-Party, Third-Party Data
   • Sampling and Bias
5. 5

   Data Ethics, Privacy and Governance

   5 topics

   • Data Ethics and Responsible Use
   • Privacy Regulations
   • PII and Anonymization
   • Data Governance and Stewardship
   • Bias and Fairness in Data

Foundations of Data Analytics flashcards for Data Analytics

25 of 50 cards from the Foundations of Data Analytics deck — real questions with worked answers.

1. What is data analytics?

   Data analytics is the science of collecting, cleaning, transforming, and examining raw data to extract meaningful insights, discover patterns, and support decision-making.

2. How does data analytics differ from data science?

   Data analytics focuses on examining existing data to answer specific questions and generate actionable insights, while data science is broader, building models, algorithms, and systems (often including machine learning) to predict and automate.

3. What are the four main types (levels) of analytics, in increasing sophistication?

   Descriptive (what happened), Diagnostic (why it happened), Predictive (what will happen), and Prescriptive (what should we do).

4. In the four types of analytics, which two look at the past and which two look toward the future?

   Descriptive and Diagnostic analyze the past; Predictive and Prescriptive look toward the future.

5. What is the primary goal of a data analyst?

   To turn raw data into actionable insights that help an organization make better, evidence-based decisions.

6. Distinguish the roles of Data Analyst, Data Scientist, and Data Engineer.

   A Data Analyst interprets data to answer business questions; a Data Scientist builds advanced/predictive models and algorithms; a Data Engineer builds and maintains the pipelines and infrastructure that move and store data.

7. What does a Business Intelligence (BI) Analyst primarily do?

   A BI Analyst focuses on reporting and dashboards, tracking KPIs and historical performance to give stakeholders an ongoing view of the business.

8. What is the role of a Data Engineer in an analytics team?

   To design, build, and maintain data pipelines, databases, and infrastructure so that clean, reliable data is available for analysts and scientists.

9. What is data-driven decision making (DDDM)?

   Making decisions based on the analysis and interpretation of data rather than on intuition, observation alone, or gut feeling.

10. What is a key benefit of data-driven decision making over intuition-based decisions?

    It reduces bias and guesswork, produces more consistent and objective decisions, and lets outcomes be measured and validated against evidence.

11. What is the difference between a data-driven and a data-informed decision?

    A data-driven decision is dictated primarily by the data; a data-informed decision uses data as one important input combined with human judgment, context, and experience.

12. What is an analytics maturity model?

    A framework that describes the stages an organization progresses through as it develops its analytics capabilities, from basic reporting to advanced, automated decision-making.

13. Name the four stages of the Gartner analytics maturity model.

    Descriptive analytics, Diagnostic analytics, Predictive analytics, and Prescriptive analytics, in order of increasing value and difficulty.

14. In an analytics maturity model, how do 'value' and 'difficulty' typically change as you advance from descriptive to prescriptive?

    Both value (business impact) and difficulty (complexity) increase as an organization moves from descriptive toward prescriptive analytics.

15. What is descriptive analytics?

    The type of analytics that summarizes historical data to describe what has happened, using metrics, aggregations, and reports (e.g., totals, averages, trends).

16. Give two common outputs or tools of descriptive analytics.

    Dashboards, reports, KPIs, summary statistics, and data visualizations such as charts and tables.

17. What is diagnostic analytics?

    Analytics that examines data to determine why something happened, identifying causes, correlations, and relationships behind observed outcomes.

18. Name two techniques commonly used in diagnostic analytics.

    Drill-down, data mining, correlation analysis, and root-cause analysis.

19. What key question does diagnostic analytics answer, and how does it build on descriptive analytics?

    It answers 'Why did it happen?' by digging beneath the 'What happened?' that descriptive analytics reports, exploring causes and relationships.

20. What is predictive analytics?

    Analytics that uses historical data, statistical models, and machine learning to forecast what is likely to happen in the future.

21. Name two common techniques used in predictive analytics.

    Regression analysis, machine learning classification, time-series forecasting, and decision trees.

22. What does predictive analytics produce, and does it guarantee outcomes?

    It produces probabilities or forecasts of future events; it estimates likelihood and does not guarantee that outcomes will occur.

23. What is prescriptive analytics?

    The most advanced analytics type, which recommends specific actions to take by combining predictions with optimization to achieve a desired outcome.

24. Name two techniques associated with prescriptive analytics.

    Optimization, simulation, and recommendation engines (often powered by machine learning or AI).

25. How does prescriptive analytics build on predictive analytics?

    Predictive analytics forecasts what will happen; prescriptive analytics goes further by recommending what actions should be taken to influence or optimize that outcome.

See more Foundations of Data Analytics flashcards →

Planning Foundations of Data Analytics for Data Analytics

Foundations of Data Analytics (Data Analytics) FAQ