DAC Data Analysis Fundamentals 1 — Questions and Answers
Question 1: What are the four types of data analytics?
- Descriptive (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what should we do) (Correct answer)
- Only descriptive and predictive
- Qualitative and quantitative only
- Input, process, output, and feedback
Correct answer: Descriptive (what happened), diagnostic (why), predictive (what will happen), and prescriptive (what should we do)
Analytics progresses from descriptive (summarizing past data), to diagnostic (identifying causes), to predictive (forecasting future outcomes), to prescriptive (recommending actions), each building on the previous level.
Question 2: What is the difference between structured and unstructured data?
- Structured data is organized in rows/columns (databases); unstructured lacks predefined format (text, images, video) (Correct answer)
- All data is structured
- Unstructured data cannot be analyzed
- They are the same thing
Correct answer: Structured data is organized in rows/columns (databases); unstructured lacks predefined format (text, images, video)
Structured data fits neatly into relational databases with defined schemas (spreadsheets, SQL databases). Unstructured data lacks a predefined format (emails, social media posts, images, videos) and requires different processing approaches.
Question 3: What is a p-value in statistical hypothesis testing?
- The probability of obtaining results at least as extreme as observed, assuming the null hypothesis is true (Correct answer)
- The percentage of data that is correct
- The power of a statistical test
- The precision of a measurement
Correct answer: The probability of obtaining results at least as extreme as observed, assuming the null hypothesis is true
A p-value represents the probability of observing results as extreme as or more extreme than the data, given that the null hypothesis is true. A p-value below a chosen threshold (typically 0.05) leads to rejecting the null hypothesis.
Question 4: What is ETL in data analytics?
- Extract, Transform, Load — the process of moving data from sources, cleaning/transforming it, and loading into a data warehouse (Correct answer)
- A programming language for data analysis
- An encryption technique for data security
- A type of data visualization
Correct answer: Extract, Transform, Load — the process of moving data from sources, cleaning/transforming it, and loading into a data warehouse
ETL is the process of extracting data from various sources, transforming it (cleaning, standardizing, aggregating), and loading it into a target data warehouse or analytics platform for analysis.
Question 5: What is the difference between correlation and causation?
- Correlation measures the statistical relationship between variables; causation means one variable directly causes changes in another (Correct answer)
- They always mean the same thing
- Causation is weaker than correlation
- Correlation always implies causation
Correct answer: Correlation measures the statistical relationship between variables; causation means one variable directly causes changes in another
Correlation indicates two variables move together (positive or negative relationship), but causation requires proof that one variable directly influences the other. Confounding variables can create spurious correlations.
Question 6: What is data cleaning and why is it important?
- Identifying and correcting errors, inconsistencies, and missing values in datasets; critical because analysis quality depends on data quality (Correct answer)
- Deleting all data and starting over
- Only removing duplicate records
- A cosmetic process that doesn't affect analysis
Correct answer: Identifying and correcting errors, inconsistencies, and missing values in datasets; critical because analysis quality depends on data quality
Data cleaning (data wrangling) involves handling missing values, correcting errors, standardizing formats, removing duplicates, and resolving inconsistencies — typically consuming 60-80% of an analyst's time but essential for reliable results.
DAC Data Analysis Fundamentals 1
What are the four types of data analytics?