Data Science & Machine Learning 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 Science & Machine Learning, not a summary of it.

1. 1

   Exploratory Data Analysis Workflow

   3 topics

   • Univariate Analysis
     • Distribution of single variables
   • Bivariate and Multivariate Analysis
     • Relationships between features
     • Correlation heatmaps
   • Summary Statistics and Profiling
     • describe and info
     • Automated profiling reports
2. 2

   Visualization with Matplotlib

   3 topics

   • Plot Anatomy
     • Figure and axes objects
     • Subplots and layout
   • Basic Chart Types
     • Line and scatter plots
     • Bar and histogram
   • Customization
     • Labels, legends and titles
     • Colors and styles
3. 3

   Statistical Visualization with Seaborn

   3 topics

   • Distribution Plots
     • histplot, kdeplot and boxplot
   • Relationship Plots
     • scatterplot and pairplot
     • Regression plots
   • Categorical Plots
     • countplot and violinplot
     • Heatmaps
4. 4

   Interactive and Dashboard Visualization

   3 topics

   • Plotly Basics
     • Interactive charts
   • Insight Communication
     • Choosing the right chart
     • Storytelling with data
   • Dashboards and Reporting
     • Streamlit and Power BI overview

Exploratory Data Analysis and Visualization flashcards for Data Science & Machine Learning

23 of 50 cards from the Exploratory Data Analysis and Visualization deck — real questions with worked answers.

1. What is Exploratory Data Analysis (EDA)?

   An approach to analyzing datasets to summarize their main characteristics, often using visual methods, to discover patterns, spot anomalies, test hypotheses, and check assumptions before formal modeling. The term was popularized by John Tukey.

2. What is univariate analysis?

   The analysis of a single variable at a time. Its purpose is to describe and summarize that one variable, examining its distribution, central tendency, and spread, without examining relationships to other variables.

3. Which plots are commonly used for univariate analysis of a numerical variable?

   Histograms, density (KDE) plots, box plots, and violin plots — used to show the distribution, shape, spread, and outliers of a single numerical variable.

4. Which plots are commonly used for univariate analysis of a categorical variable?

   Bar charts (count plots) and pie charts, which show the frequency or proportion of each category.

5. What is bivariate analysis?

   The analysis of the relationship between exactly two variables at a time, to determine whether and how they are associated (e.g., correlation, dependence, or differences across groups).

6. What is multivariate analysis?

   The simultaneous analysis of three or more variables to understand relationships, interactions, and combined effects among them (e.g., using pair plots, correlation heatmaps, or color/size encodings).

7. Which plot type is best for visualizing the relationship between two numerical variables?

   A scatter plot, which plots one variable on the x-axis and the other on the y-axis to reveal correlation, clusters, trends, and outliers.

8. How can you visualize the relationship between a numerical and a categorical variable?

   Using grouped box plots, violin plots, or bar plots of an aggregate (e.g., mean) of the numerical variable across the categories.

9. What tool visualizes the relationship between two categorical variables?

   A contingency table (cross-tabulation), often visualized with a grouped/stacked bar chart or a heatmap of the counts.

10. What is a pair plot (scatterplot matrix) used for?

    It is a multivariate tool that plots pairwise scatter plots for every combination of numerical variables in a dataset (with distributions on the diagonal), giving a quick overview of all bivariate relationships.

11. List the common measures of central tendency.

    The mean (arithmetic average), the median (middle value when sorted), and the mode (most frequent value).

12. List the common measures of dispersion (spread).

    Range, variance, standard deviation, and the interquartile range (IQR).

13. What is the formula for the sample variance?

    s² = Σ(xᵢ − x̄)² / (n − 1), the average of squared deviations from the mean using Bessel's correction (n − 1 denominator).

14. How is the standard deviation related to the variance?

    The standard deviation is the square root of the variance. It expresses spread in the same units as the original data.

15. What is the interquartile range (IQR) and how is it calculated?

    The IQR is the spread of the middle 50% of the data, calculated as Q3 − Q1 (the 75th percentile minus the 25th percentile).

16. What is the 1.5×IQR rule for identifying outliers?

    A value is flagged as an outlier if it falls below Q1 − 1.5×IQR or above Q3 + 1.5×IQR. This is the basis for the whiskers in a standard box plot.

17. What does skewness measure?

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

18. What does kurtosis measure?

    Kurtosis measures the 'tailedness' (heaviness of the tails) of a distribution relative to a normal distribution. High kurtosis indicates heavy tails/more outliers; low kurtosis indicates light tails.

19. In pandas, what does df.describe() produce?

    A summary of descriptive statistics for numerical columns: count, mean, standard deviation, min, 25%, 50% (median), 75%, and max.

20. What is data profiling in EDA?

    The process of examining and summarizing a dataset's structure and quality — data types, missing values, unique counts, distributions, and statistics — often automated by tools like ydata-profiling (pandas-profiling).

21. Name the main anatomical components of a Matplotlib plot.

    The Figure (the overall container), the Axes (an individual plot/subplot), the axis (x and y with ticks, tick labels, and axis labels), the title, the legend, and the plotted artists (lines, markers, bars).

22. What is the difference between a Figure and an Axes in Matplotlib?

    The Figure is the top-level container holding everything; an Axes is a single plotting area (with its own data, ticks, and labels) inside the Figure. A Figure can contain multiple Axes.

23. What is the difference between Matplotlib's pyplot (state-based) and object-oriented interfaces?

    The pyplot interface (plt.plot, plt.title) implicitly acts on the 'current' Figure/Axes and is convenient for quick plots; the object-oriented interface (fig, ax = plt.subplots(); ax.plot()) explicitly references objects and is preferred for complex, multi-axes figures.

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