Data Analytics Exploratory Data Analysis and Visualization Syllabus

Exploratory Data Analysis and Visualization syllabus — full chapter and topic list

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

1. 1

   Exploratory Data Analysis

   5 topics

   • Univariate Analysis
   • Bivariate and Multivariate Analysis
   • Distribution Analysis
   • Correlation Analysis
   • Feature Engineering Basics
2. 2

   Principles of Data Visualization

   4 topics

   • Visual Encoding and Perception
   • Choosing the Right Chart Type
   • Color, Scale and Layout
   • Avoiding Misleading Visuals
3. 3

   Chart Types and Use Cases

   5 topics

   • Bar and Column Charts
   • Line and Area Charts
   • Scatter and Bubble Plots
   • Histograms and Box Plots
   • Heatmaps and Treemaps
4. 4

   Tableau

   5 topics

   • Connecting to Data Sources
   • Dimensions and Measures
   • Calculated Fields and Parameters
   • Filters and Sets
   • Dashboards and Stories
5. 5

   Power BI

   4 topics

   • Power Query and Data Modeling
   • DAX Fundamentals
   • Relationships and Star Schema
   • Reports and Dashboards
6. 6

   Dashboard Design and Storytelling

   4 topics

   • Defining Metrics and KPIs
   • Dashboard Layout Best Practices
   • Interactivity and Drill-Downs
   • Data Storytelling and Narrative

Exploratory Data Analysis and Visualization flashcards for Data Analytics

23 of 53 cards from the Exploratory Data Analysis and Visualization deck — real questions with worked answers.

1. What is univariate analysis?

   The examination of a single variable at a time to summarize and describe its characteristics, without addressing relationships or causes. It focuses on central tendency, dispersion, and shape of one variable's distribution.

2. Name the three main measures of central tendency and when each is preferred.

   Mean (best for symmetric, no outliers), median (robust to skew/outliers), and mode (best for categorical or multimodal data).

3. How are variance and standard deviation defined for a sample?

   Variance $s^{2} = \frac{1}{n-1}\sum_{i=1}^{n}(x_i - \bar{x})^{2}$ and standard deviation $s = \sqrt{s^{2}}$; the $n-1$ denominator (Bessel's correction) gives an unbiased estimate.

4. What is the interquartile range (IQR) and how is it computed?

   $\text{IQR} = Q_3 - Q_1$, the spread of the middle 50% of the data. It is a robust measure of dispersion unaffected by extreme values.

5. State the common IQR rule for flagging outliers.

   A value is an outlier if it lies below $Q_1 - 1.5\,\text{IQR}$ or above $Q_3 + 1.5\,\text{IQR}$.

6. What is bivariate analysis versus multivariate analysis?

   Bivariate analysis studies the relationship between exactly two variables; multivariate analysis simultaneously examines three or more variables and their interrelationships.

7. Which chart is standard for visualizing the relationship between two continuous variables in bivariate analysis?

   A scatter plot, which reveals direction, form (linear/nonlinear), strength, and outliers of the relationship.

8. How do you explore the relationship between a categorical and a continuous variable?

   Use grouped summaries or visuals such as side-by-side box plots, grouped bar charts of means, or violin plots split by category.

9. What is a contingency (cross-tabulation) table used for?

   To display the joint frequency distribution of two categorical variables, enabling analysis of association (e.g., via the chi-square test).

10. What does a distribution describe in EDA?

    How the values of a variable are spread across their range — including central location, spread, shape, skewness, modality, and the presence of outliers.

11. Define skewness and interpret positive versus negative skew.

    Skewness measures asymmetry of a distribution. Positive (right) skew has a long right tail with mean > median; negative (left) skew has a long left tail with mean < median.

12. What does kurtosis measure?

    The 'tailedness' of a distribution. High kurtosis (leptokurtic) means heavy tails and a sharp peak; low kurtosis (platykurtic) means light tails and a flatter peak. Normal distribution has excess kurtosis of $0$.

13. What are the key properties of a normal (Gaussian) distribution?

    Symmetric and bell-shaped, fully described by mean $\mu$ and standard deviation $\sigma$, with mean = median = mode, following the empirical rule.

14. State the empirical (68-95-99.7) rule for a normal distribution.

    Approximately 68% of data lie within $\mu \pm \sigma$, 95% within $\mu \pm 2\sigma$, and 99.7% within $\mu \pm 3\sigma$.

15. How is a z-score (standard score) calculated and what does it mean?

    $z = \frac{x - \mu}{\sigma}$; it expresses how many standard deviations a value lies from the mean, enabling standardized comparison across distributions.

16. What is the purpose of correlation analysis?

    To quantify the strength and direction of the linear (or monotonic) association between two variables, without implying causation.

17. Give the formula and range of the Pearson correlation coefficient.

    $r = \frac{\sum (x_i-\bar{x})(y_i-\bar{y})}{\sqrt{\sum (x_i-\bar{x})^{2}}\sqrt{\sum (y_i-\bar{y})^{2}}}$, ranging from $-1$ to $+1$; $0$ indicates no linear relationship.

18. When should Spearman's rank correlation be used instead of Pearson's?

    When the relationship is monotonic but nonlinear, data are ordinal, or there are outliers/non-normality; Spearman correlates the ranks rather than the raw values.

19. Why is 'correlation does not imply causation' a key principle?

    Because two variables can be associated due to coincidence, reverse causation, or a confounding (lurking) variable, so correlation alone cannot establish that one causes the other.

20. What is a correlation matrix and how is it commonly visualized?

    A square table of pairwise correlation coefficients among many variables, commonly visualized as a heatmap to spot strongly related feature pairs.

21. What is feature engineering?

    The process of creating, transforming, or selecting variables from raw data to improve model performance and better expose underlying patterns to algorithms.

22. Distinguish normalization from standardization in feature scaling.

    Normalization (min-max) rescales to a fixed range, usually $[0,1]$: $x' = \frac{x - x_{\min}}{x_{\max} - x_{\min}}$. Standardization rescales to zero mean and unit variance: $x' = \frac{x - \mu}{\sigma}$.

23. What is one-hot encoding and why is it used?

    A technique that converts a categorical variable with $k$ levels into $k$ binary (0/1) indicator columns, so nominal categories can be used by models without implying false ordinal relationships.

See more Exploratory Data Analysis and Visualization flashcards →

Planning Exploratory Data Analysis and Visualization for Data Analytics

Exploratory Data Analysis and Visualization (Data Analytics) FAQ