Data Visualization

 Data Visualization

What is Data Visualization?

Data visualization is a vital part of analyzing data and extracting meaning from patterns

that are displayed. Displaying various types of graphs to visualize a high level view of

that data provides better insight than looking through the details of each individual

data point.

These graphs can provide a quick view of the aggregation of a period via pie charts or

trends via line charts.

By leveraging various shapes and colors data visualization can provide an impactful

view of a story that the data can tell the user.

Below is a list of visualizations and uses.

Line Charts show trends like stock performance over a period of time.

Bar Charts can visualize differences in results like surveys of people who like or

dislike a product.

Scatter Plots show correlations and relationships.

Heat Maps can show density of data in a geographical area.

Pie chart a representation of a whole 100% view of results of categorized data.sents

a specific category, and the size of that slice (its arc length, central angle, and area)

is proportional to the quantity it represents.

Tableau

Tableau is a leading Data visualization tool used by companies today. Helps companies

to build all major graphs by pulling in their collected private data from sales,

manufacturing, personnel, etc. Basically all departments in the organization have a use

for Tableau dashboards. 

These dashboards provide users the ability to access company data and build their

own charts and graphs.

Some of the important features of Tableau

Drag and drop fields help build visualization without the need of a developer.

Ability to pull in large volumes of data without degradation.

Users can customize chart details.

Data visualization purposes

Idea Generation - Showing data may help in product selection or problem solutions.

Visual Discovery -  Helps users with trends and patterns in the data. Buying patterns

of consumers.

Story telling - Presentations often have visualization to help visually get points across.

Set Context: Show data comparisons so results can be easily viewed as good or bad.

Know the Audience: Data should be known and understood to the audience viewing it.

Keep it Simple: Make sure charts are clear, easy to read and not cluttered.

Matplotlib

In python we use the matplotlib to build visualizations

Again we need to have the libraries installed in the system.

We will use cloud storage modules to copy the graph image to a Google cloud bucket

for it can be viewed properly.

john_iacovacci1@cloudshell:~/yf (cloud-project-examples)$ pip3 install matplotlib

matplot.py

======================================================

import matplotlib.pyplot as plt

from google.cloud import storage

# use  your project ID from Google cloud

PROJECT_ID = 'cloud-project-examples'

bucket_name = "cloud-storage-exam"

storage_client = storage.Client()

bucket = storage_client.bucket(bucket_name)

# 1. Prepare the data

msdate = ['Mon 2/23', 'Tue 2/24', 'Wed 2/25', 'Thu 2/26', 'Fri 2/27']

msprice = [384, 389, 400, 401, 392]

# 2. Create the plot

plt.figure(figsize=(8, 5)) # Sets the window size

plt.plot(msdate, msprice, color='green', marker='o', linestyle='-', linewidth=2)

# 3. Add "Context" (Best Practice)

plt.title('MSFT prices last week', fontsize=14)

plt.xlabel('Date')

plt.ylabel('Price')

plt.grid(True, linestyle='--', alpha=0.6) # Adds a light grid for readability

# 4. Show the chart

plt.show()

plt.savefig('msft_price.png')

# send plot file to your bucket

plot_filename = 'msft_price.png'

destination_blob_name = f'{plot_filename}'

source_file_name = 'msft_price.png'

blob = bucket.blob(destination_blob_name)

blob.upload_from_filename(source_file_name)  #upload file to specified destination

print(f'File {source_file_name} uploaded to {destination_blob_name}.')

===================================================

Results

john_iacovacci1@cloudshell:~/yf (cloud-project-examples)$ python3 matplot.py

File msft_price.png uploaded to msft_price.png.

Data visualization and big data

The “age of Big Data” kicks into high gear.

As data becomes more important in today's society, visualization is an important

tool to help make sense of these vast datasets.

Learn more about big data.