Data Science Interview Challenge

Challenge 5: Matrix Plots

Data often comes in a matrix format, where rows and columns represent different variables or categories. To visualize this structured data effectively, matrix plots come to the rescue. Seaborn, known for its comprehensive plotting capabilities, offers specialized tools for creating powerful matrix visualizations.

Matrix plots in Seaborn allow you to:

Visualize the relationship between two categorical variables.
Display the distribution of data in a heatmap format.
Explore hierarchical structures in the data using cluster maps.

Leveraging Seaborn's matrix plots, analysts can navigate intricate data structures, extract patterns, and make data-driven decisions with ease.

Завдання

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Using Seaborn, demonstrate the matrix structure in a dataset:

Plot a heatmap of a correlation matrix.
Annotate the heatmap with the actual correlation values.

Рішення

import seaborn as sns

import matplotlib.pyplot as plt

# Sample data

data = sns.load_dataset('flights').pivot(index='month', columns='year', values='passengers')

# 1. Heatmap of a correlation matrix

sns.heatmap(data.corr())

plt.title('Correlation Matrix Heatmap')

plt.show()

# 2. Annotated heatmap

sns.heatmap(data.corr(), annot=True)

plt.title('Annotated Correlation Matrix Heatmap')

plt.show()

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Секція 5. Розділ 5

import seaborn as sns

import matplotlib.pyplot as plt

# Sample data

data = sns.load_dataset('flights').pivot(index='month', columns='year', values='passengers')

# 1. Heatmap of a correlation matrix

sns.___(data.corr())

plt.title('Correlation Matrix Heatmap')

plt.show()

# 2. Annotated heatmap

sns.___(data.corr(), ___=True)

plt.title('Annotated Correlation Matrix Heatmap')

plt.show()

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Data Science Interview Challenge

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1. Python

Let's take a look at what we'll be working with in this course. The first section will acquaint you with Python, a flexible and advanced programming language known for its clear syntax and readability.

Challenge 1: List Comprehension

Challenge 2: String Manipulation

Challenge 3: Dictionary Manipulation

Challenge 4: Nested Loops

Challenge 5: Classes

Theoretical Questions

2. NumPy

NumPy is a fundamental library in Python that facilitates efficient numerical computations with powerful n-dimensional arrays and mathematical functions.

Challenge 1: Array Creation

Challenge 2: Array Manipulation

Challenge 3: Statistical Insights

Challenge 4: Handling Missing Values

Challenge 5: Subarray Sorting

Theoretical Questions

3. Pandas

Pandas provides intuitive and versatile data structures for efficient data manipulation and analysis, streamlining the initial stages of the data science pipeline.

Challenge 1: DataFrame Creation

Challenge 2: Data Grouping

Challenge 3: Indexing and MultiIndexing

Challenge 4: Altering DataFrame

Challenge 5: Iterating Over Data

Theoretical Questions

4. Matplotlib

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations in Python.

Challenge 1: Fundamentals of Plotting

Challenge 2: Other Graph Types

Challenge 3: Subplots

Challenge 4: Customizing Plots

Challenge 5: Adding Text to Plots

Theoretical Questions

5. Seaborn

Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

Challenge 1: Visualizing Distributions

Challenge 2: Exploring Categorical Data

Challenge 3: Relational Plots

Challenge 4: Regression Plots

Challenge 5: Matrix Plots

Theoretical Questions

6. Statistics

Statistics provides data scientists with foundational techniques and tools to extract meaningful insights from data, allowing them to make informed decisions and predictions based on empirical evidence.

Challenge 1: Probabilities and Distributions

Challenge 2: Bayes' Theorem

Challenge 3: Hypothesis Testing

Challenge 4: Confidence Intervals

Challenge 5: Correlation

Theoretical Questions

7. Scikit-learn

Scikit-learn is an open-source Python library that provides simple and efficient tools for data analysis and modeling, particularly for machine learning. Data scientists use it extensively for its comprehensive collection of algorithms and processing techniques, enabling them to quickly develop and deploy predictive models.

Challenge 1: Data Scaling

Challenge 2: Basic Model Creation

Challenge 3: Pipelines

Challenge 4: Cross-validation

Challenge 5: Hyperparameter Tuning

Theoretical Questions