Skip to main content

What is Data Visualization and Communication

Image
By

Data Visualization Principles and Tools

Content of Data Visualization :

  • Data Visualization Principles
  • Storytelling with Data
  • Data Reporting and Dashboards
  • Data Visualization Tools (Tableau, PowerBI)

Data visualization is a critical aspect of data science that involves creating visual representations of data to facilitate understanding, communication, and decision-making. Effective data visualization requires a solid understanding of visualization principles, storytelling, and data reporting.

Data Visualization Principles:


Some fundamental principles of data visualization include:

Clarity: The visualization should be clear and easy to understand.

Simplicity: The visualisation should be straightforward and free of extraneous detail

Accuracy: The visualization should accurately represent the underlying data.

Consistency: The visualization should use consistent visual cues to represent different types of data.

Context: The visualization should provide enough context to help the viewer understand the data.

Example code for data visualization using Matplotlib:

Example code for data visualization using Matplotlib:

python code

import pandas as pd

import matplotlib.pyplot as plt

# Load data

data = pd.read_csv('data.csv')

# Create a bar chart of the data

plt.bar(data['Category'], data['Value'])

# Add labels and title

plt.xlabel('Category')

plt.ylabel('Value')

plt.title('Data Visualization')

# Show the chart

plt.show()

This code loads data from a CSV file and creates a bar chart using the Matplotlib library. The chart shows the value of each category in the data. The chart includes labels and a title to provide context for the data.

Storytelling with Data:

Effective data visualization requires storytelling skills to communicate insights and findings to others. A good data visualization tells a story that engages the audience and helps them understand the data.

Data Reporting and Dashboards:


Data reporting involves presenting data and insights to stakeholders in a structured and meaningful way. Dashboards are a popular way to provide stakeholders with real-time data and insights in a user-friendly format. There are various data reporting and dashboard tools available in the market such as Tableau, PowerBI, and QlikView.

Example code for creating a dashboard using Tableau:

markdown code

1. Connect to the data source

2. Drag and drop the relevant fields into the view

3. Choose the appropriate chart type

4. Customize the chart by adding labels, titles, colors, and other formatting options

5. Add filters, groups, and hierarchies to the dashboard

6. Publish the dashboard to Tableau Server or Tableau Online for sharing and collaboration


This code explains the high-level steps involved in creating a dashboard using Tableau. The process involves connecting to the data source, selecting relevant fields, choosing appropriate chart types, customizing the chart, and adding filters, groups, and hierarchies. Finally, the dashboard is published to Tableau Server or Tableau Online for sharing and collaboration with stakeholders.

Power BI and QlikView are two popular data visualization tools used in the industry.

Microsoft's Power BI is a service for business analytics that offers interactive visualizations and business intelligence features with a user interface that is easy enough for end users to utilize to build their own reports and dashboards.It can connect to a wide range of data sources, including Excel files, SQL databases, and cloud services such as Azure and Salesforce.

QlikView is a business intelligence software that allows users to analyze data from multiple sources through an intuitive interface. It uses an in-memory data model to rapidly process large data sets and provide quick insights. QlikView can also integrate with multiple data sources, including big data platforms like Hadoop.

Example code for data visualization using Power BI:

python code

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

Load data

data = pd.read_csv('data.csv')

Create visualizations

sns.pairplot(data, hue='target')

plt.show()

Example code for data visualization using QlikView:

qlikview

Load data from CSV

LOAD *

FROM data.csv

(txt, codepage is 1252, embedded labels, delimiter is ',', msq);

Create bar chart

Chart1:

LOAD category,

sum(sales) as total_sales

Resident data

Group by category;

Chart

Bar Chart (total_sales, category)


Both Power BI and QlikView provide a range of data visualization options beyond simple scatter and bar charts, including heatmaps, tree maps, and geographic maps. These tools can also be used to create interactive dashboards and reports, allowing users to explore data and gain insights in real-time.


To Main (Topics of Data Science)

                                                        Continue to (Ethics and Privacy)


Labels:
Location: Guntur, Andhra Pradesh, India

Comments

Post a Comment

Requesting you please share your opinion about my content in this blog for further development in a better way. Thank you. Dr.Srinivas

Popular posts from this blog

What is Data Science

By
Learn Data Science - Introduction Introduction to Data Science History The field of data science has its roots in statistics and computer science and has evolved to encompass a wide range of techniques and tools for understanding and making predictions from data. The history of data science can be traced back to the early days of statistics when researchers first began using data to make inferences and predictions about the world. In the 1960s and 1970s, the advent of computers and the development of new algorithms and statistical methods led to a growth in the use of data to answer scientific and business questions. The term "data science" was first coined in the early 1960s by John W. Tukey, a statistician and computer scientist . In recent years, the field of data science has exploded in popularity, thanks in part to the increasing availability of data from a wide range of sources, as well as advances in computational power and machine learning. Today, data science is us...
Post a Comment
Read more

What is Model Evaluation and Selection

By
Understanding the Model Evaluation and Selection  Techniques Content of  Model Evaluation •     Model Performance Metrics •     Cross-Validation Techniques •      Hyperparameter Tuning •      Model Selection Techniques Model Evaluation and Selection: Model evaluation and selection is the process of choosing the best machine learning model based on its performance on a given dataset. There are several techniques for evaluating and selecting machine learning models, including performance metrics, cross-validation techniques, hyperparameter tuning, and model selection techniques.     Performance Metrics: Performance metrics are used to evaluate the performance of a machine learning model. The choice of performance metric depends on the specific task and the type of machine learning model being used. Some common performance metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC score. Cross-...
Post a Comment
Read more

What is the Probability and Statistics

By
Undrstand the Probability and Statistics in Data Science Contents of P robability and Statistics Probability Basics Random Variables and Probability Distributions Statistical Inference (Hypothesis Testing, Confidence Intervals) Regression Analysis Probability Basics Solution :  Sample Space = {H, T} (where H stands for Head and T stands for Tail) Solution :  The sample space is {1, 2, 3, 4, 5, 6}. Each outcome is equally likely, so the probability distribution is: Hypothesis testing involves making a decision about a population parameter based on sample data. The null hypothesis (H0) is the hypothesis that there is no significant difference between a set of population parameters and a set of observed sample data. The alternative hypothesis (Ha) is the hypothesis that there is a significant difference between a set of population parameters and a set of observed sample data. The hypothesis testing process involves the following steps: Formulate the null and al...
Post a Comment
Read more