Advanced Data Visualization

Techniques and Statistical

Method

BY Peter Barasa
 Session objectives
By the end of the session, participants will have
gained the following skills:-
a) Advanced data visualization techniques using
libraries like Plotly, Bokeh, and Altair.
b) Statistical methods for exploratory data analysis,
hypothesis testing, and feature importance.
c) Time series analysis and spatial data visualization.
 Introduction
Overview of Advanced Data Visualization
Techniques and Statistical Methods:
•In today's data-driven world, the ability
to effectively visualize and analyze data is
crucial for extracting valuable insights and
making informed decisions.
 Introduction
• Advanced data visualization techniques
and statistical methods play a vital role
in the field of data science, enabling
data scientists and analysts to explore,
understand, and communicate complex
data in a meaningful way.
 Introduction
• Advanced data visualization techniques
go beyond traditional charts and graphs,
offering interactive and dynamic ways to
represent data.
• Libraries such as Plotly, Bokeh, and
Altair provide powerful tools for
creating visually appealing and
interactive visualizations.
 Introduction
• Statistical methods, on the other hand,
provide a rigorous framework for analyzing
and interpreting data.
• Exploratory Data Analysis (EDA) is a
fundamental statistical approach that
involves summarizing and visualizing the main
characteristics of a dataset. EDA
techniques, such as calculating descriptive
statistics and creating visualizations like
histograms and box plots, help in
understanding the distribution, central
tendency, and variability of the data.
 Introduction
• Hypothesis testing is another essential
statistical method used to make decisions
or draw conclusions based on data
evidence. It involves formulating and
testing hypotheses to determine if
observed patterns or relationships in the
data are statistically significant.
• Additionally, feature importance
techniques help identify the most
informative or relevant variables in a
predictive model, guiding feature selection
and model interpretation..
Analysis in Data Science:
• Effective data visualization and analysis
are critical components of data science.
They enable data scientists to uncover
hidden patterns, relationships, and trends
within the data, leading to valuable
insights and data-driven decision making.
Here are some key reasons why effective
data visualization and analysis are
important in data science:
Analysis in Data Science:
• Effective data visualization and analysis
are critical components of data science.
They enable data scientists to uncover
hidden patterns, relationships, and trends
within the data, leading to valuable
insights and data-driven decision making.
Here are some key reasons why effective
data visualization and analysis are
important in data science:
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Data Understanding: Visualizing data helps
in gaining a deep understanding of its
structure, distribution, and relationships. It
allows data scientists to identify patterns,
outliers, and anomalies that may not be
apparent from raw data alone. Visual
representations make it easier to
comprehend complex datasets and
communicate findings to stakeholders.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Hypothesis Generation: Exploratory data
analysis and visualization can help generate
new hypotheses and research questions. By
visually examining the data, data scientists
can identify potential relationships, clusters,
or trends that warrant further investigation.
This guides the direction of subsequent
analysis and modeling efforts.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Decision Making: Effective data
visualization enables data-driven decision
making by presenting insights in a clear and
intuitive manner. Interactive visualizations
allow stakeholders to explore different
scenarios, filter data, and drill down into
specific details.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• This empowers decision makers to make
informed choices based on data evidence
rather than relying solely on intuition or
guesswork.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Communication and Storytelling: Data
visualization is a powerful tool for
communicating complex ideas and findings to
both technical and non-technical audiences.
Well-designed visualizations can convey key
messages, trends, and insights in a concise
and engaging way.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• They help in telling compelling data stories,
making it easier for stakeholders to
understand and act upon the insights derived
from the data.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Model Evaluation and Refinement:
Statistical methods and visualizations play a
crucial role in evaluating and refining machine
learning models. Techniques like feature
importance analysis help identify the most
influential variables, guiding feature
selection and model interpretation.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Visualizing model performance metrics, such
as accuracy, precision, and recall, aids in
assessing the model's effectiveness and
identifying areas for improvement.
 Importance of Effective
Data Visualization and
Analysis in Data Science:
• Visualizing model performance metrics, such
as accuracy, precision, and recall, aids in
assessing the model's effectiveness and
identifying areas for improvement.
 Plotly:
• Plotly is a powerful library for creating
interactive and publication-quality
visualizations.
• It supports a wide range of chart types,
including line plots, scatter plots, bar charts,
heatmaps, 3D plots, and more.
 Plotly:
• Plotly allows for easy customization of plot
layouts, colors, labels, and hover interactions.
• It provides features like zooming, panning,
and selection for interactive data
exploration.
• Plotly can be used in various environments,
including Python, R, JavaScript, and Jupyter
Notebooks.
 Bokeh:
1. Bokeh is a library for creating interactive
visualizations in web browsers.
2. It focuses on providing high-performance
interactivity for large datasets.
3. Bokeh supports a variety of plot types, including line
plots, scatter plots, bar charts, heatmaps, and
geographical maps.
 Bokeh:
1. It allows for real-time streaming and updating of
data in visualizations.
2. Bokeh provides tools for data selection,
hovering, and zooming interactions.
3. It integrates well with other data analysis
libraries like NumPy, Pandas, and Scipy.
 Bokeh:
1. It allows for real-time streaming and updating of
data in visualizations.
2. Bokeh provides tools for data selection,
hovering, and zooming interactions.
3. It integrates well with other data analysis
libraries like NumPy, Pandas, and Scipy.
 Altair:
1. Altair is a declarative statistical visualization
library based on Vega and Vega-Lite.
2. It provides a concise and expressive API for
creating a wide range of statistical charts.
3. Altair uses a grammar of graphics approach,
allowing users to specify visualizations using a
consistent and intuitive syntax.
 Altair:
1. It supports various chart types, including scatter
plots, line charts, bar charts, heatmaps, and
geographic maps.
2. Altair allows for easy creation of faceted and
layered plots, enabling the visualization of
complex relationships in data.
3. It integrates well with Pandas DataFrames and
supports interactive plot configurations
 Exploratory Data Analysis
(EDA)
1. EDA involves summarizing and visualizing the
main characteristics of a dataset.
2. Techniques include calculating descriptive
statistics (mean, median, standard deviation),
creating histograms, box plots, and scatter
plots.
3. EDA helps identify patterns, outliers, and
relationships in the data.
4. Libraries like Pandas, Matplotlib, and Seaborn
are commonly used for EDA in Python.
 Exploratory Analysis (EDA)
1. EDA involves summarizing and visualizing the
main characteristics of a dataset.
2. Techniques include calculating descriptive
statistics (mean, median, standard deviation),
creating histograms, box plots, and scatter
plots.
3. EDA helps identify patterns, outliers, and
relationships in the data.
4. Libraries like Pandas, Matplotlib, and Seaborn
are commonly used for EDA in Python.
 Hypothesis Testing
1. Hypothesis testing is a statistical method used
to make decisions based on data evidence.
2. It involves formulating a null hypothesis (H0)
and an alternative hypothesis (H1).
3. Common hypothesis tests include t-tests,
ANOVA, chi-square tests, and correlation tests.
 Hypothesis Testing
1. These tests help determine if observed
differences or relationships in the data are
statistically significant.
2. Python libraries like SciPy and Statsmodels
provide functions for various hypothesis tests.
.
 Hypothesis Testing
1. These tests help determine if observed
differences or relationships in the data are
statistically significant.
2. Python libraries like SciPy and Statsmodels
provide functions for various hypothesis tests.
.
 Feature Importance:
1. Feature importance refers to the relative
contribution of each feature (variable) in a
predictive model.
2. It helps identify the most informative features
for making predictions..
 Feature Importance:
1. Feature importance refers to the relative
contribution of each feature (variable) in a
predictive model.
2. It helps identify the most informative features
for making predictions..
 Techniques for feature
importance include:
1. Permutation Feature Importance: Measuring
the decrease in model performance when a
feature is randomly shuffled.
2. Feature Coefficients: Examining the coefficients
or weights assigned to each feature in linear
models.
3. Tree-based Feature Importance: Calculating
the average impurity decrease or Gini
importance in decision tree-based models.
 Techniques for feature
importance include:

Libraries like Scikit-learn and XGBoost provide

tools for calculating feature importance
 Time Series Analysis and
Spatial Data Visualization:
Time Series Analysis:
Time series analysis involves studying data
points collected over time.
Techniques include trend analysis, seasonality
analysis, and forecasting.
 Time Series Analysis and
Spatial Data Visualization:
Time Series Analysis:
Popular libraries for time series analysis in
Python include Pandas, Statsmodels, and
Prophet.
Visualization of time series data can be done
using line plots, scatter plots, and heatmaps.
Interactive plotting libraries like Plotly and Bokeh
are useful for exploring time series data.
 Time Series Analysis and
Spatial Data Visualization:
Time Series Analysis:
Popular libraries for time series analysis in
Python include Pandas, Statsmodels, and
Prophet.
Visualization of time series data can be done
using line plots, scatter plots, and heatmaps.
Interactive plotting libraries like Plotly and Bokeh
are useful for exploring time series data.
 Time Series Analysis and
Spatial Data Visualization:
Spatial Data Visualization:
•Spatial data visualization involves representing
geographic or spatial information on maps.
•Libraries like Folium, GeoPandas, and Plotly
express allow for creating interactive maps.
 Time Series Analysis and
Spatial Data Visualization:
Spatial Data Visualization:
•Spatial data visualization involves representing
geographic or spatial information on maps.
•Libraries like Folium, GeoPandas, and Plotly
express allow for creating interactive maps.
 Time Series Analysis and
Spatial Data Visualization:
Spatial Data Visualization:
•Techniques include choropleth maps, heatmaps,
and marker plots.
•Spatial data can be combined with other data
sources to visualize patterns and relationships.
•Tools like QGIS and ArcGIS are commonly used
for advanced spatial data analysis and
visualization.