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Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

book
Challenge 3: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.
  • Present the relationship between multiple variables across complex datasets.
  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Task
test

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Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

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Section 5. Chapter 3
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book
Challenge 3: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.
  • Present the relationship between multiple variables across complex datasets.
  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Task
test

Swipe to show code editor

Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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