Data Visualization

Course Content
MODULE 1: INTRODUCTION TO DATA SCIENCE
In this module, students will learn the fundamentals of Data Science, which combines statistics, mathematics, and computer science to extract insights from data.
0/3
Overview of Data Science
Data Science Lifecycle
TEST ON INTRODUCTION TO DATA SCIENCE
10:00
MODULE 2: PRACTICING THE BASICS
This module will give students foundational knowledge and hands-on experience in programming for data science using Python.
0/4
Python for Data Science
Introduction to SQL
Introducing the SELECT Statement for Retrieving Data
TEST ON PYTHON AND INTRODUCTION TO SQL
10:00
MODULE 3: STATISTICS AND PROBABILITY
This module on Statistics and Probability provides a comprehensive introduction to essential concepts and techniques.
0/10
Introduction to Statistics
Data Collection and Sampling Techniques
Descriptive Statistics
Probability Basics
Conditional Probability
Discrete Probability Distributions
Continuous Probability Distributions
Hypothesis Testing
Correlation and Regression
TEST ON STATISTICS AND PROBABILITY
10:00
MODULE 4: MATHEMATICAL FOUNDATIONS
The Mathematical Foundations module provides a solid understanding of linear algebra and calculus concepts that are essential for data science and machine learning.
0/3
Linear Algebra
Calculus
TEST ON MATHEMATICAL FOUNDATIONS
19:00
MODULE 5: EXPLORATORY DATA ANALYSIS (EDA)
This module on Exploratory Data Analysis (EDA) emphasizes the importance of data cleaning and visualization in the data analysis process.
0/3
Data Cleaning
Data Visualization
QUIZ ON EXPLORATORY DATA ANALYSIS (EDA)
10:00
Introduction to Data Science and Analytics
About Lesson

Data Visualization

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Tools for Data Visualization

Matplotlib:

  • A widely used Python library for creating static, animated, and interactive visualizations.
  • Supports a variety of plots like line charts, bar charts, histograms, and scatter plots.

Example:

import matplotlib.pyplot as plt

plt.figure(figsize=(10, 5))
plt.plot(df['x_column'], df['y_column'], marker='o')
plt.title('Line Plot Example')
plt.xlabel('X Axis Label')
plt.ylabel('Y Axis Label')
plt.grid(True)
plt.show()

 

Seaborn:

  • Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics.
  • It simplifies the creation of complex visualizations like heatmaps, violin plots, and pair plots.

Example:

import seaborn as sns

sns.set(style='whitegrid')
plt.figure(figsize=(10, 5))
sns.boxplot(x='category_column', y='value_column', data=df)
plt.title('Box Plot Example')
plt.show() 

 

Plotly:

  • A library for creating interactive, web-based visualizations that can be embedded in web applications.
  • Supports a wide range of plots and allows for real-time data updates.

Example:

import plotly.express as px

fig = px.scatter(df, x='x_column', y='y_column', color='category_column')
fig.show() 

 

Tableau:

    • A powerful data visualization tool that provides an intuitive interface for creating interactive dashboards and reports.
    • Allows users to connect to various data sources and create visualizations without extensive programming knowledge.

Techniques for Data Visualization

Univariate Analysis:

  • Analyzing a single variable to understand its distribution and characteristics.
  • Common visualizations include histograms, box plots, and density plots.

Example:

sns.histplot(df['column_name'], bins=30, kde=True)
plt.title('Histogram with Density Plot')
plt.show() 

 

Bivariate Analysis:

  • Exploring the relationship between two variables.
  • Common visualizations include scatter plots, bar plots, and line graphs.

Example:

sns.scatterplot(x='x_column', y='y_column', data=df)
plt.title('Scatter Plot Example')
plt.show() 

 

Multivariate Analysis:

  • Analyzing relationships among three or more variables.
  • Techniques include pair plots, heatmaps, and 3D scatter plots.

Example:

sns.pairplot(df, hue='category_column')
plt.title('Pair Plot Example')
plt.show() 

 

Interactive Visualizations:

    • Creating visualizations that allow users to interact with the data (e.g., filtering, zooming).
    • Tools like Plotly and Tableau excel in creating interactive visualizations that can enhance data exploration.
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