INFORMATION VIZUALISATION

ASSIGNMENTS

NETFLIX - ANALYSIS

The Tableau project analyzed Netflix's performance by examining subscriber numbers, revenue, and content offerings, including TV shows, movies, and ratings. The data was represented in various types of charts and maps, including bar charts, line charts, heat maps, and geographic maps. These visualizations helped to present the data in an easy-to-understand format and allowed for the identification of trends and patterns in the data.

By using a variety of visualizations, the Tableau project was able to provide a comprehensive analysis of Netflix's performance, highlighting key insights and trends that helped the company make data-driven decisions about its business strategies.

Netflix Analysis

The D3 project analyzed Netflix's performance by examining subscriber numbers, revenue, and content offerings, such as TV shows, movies, and ratings. The data was loaded into D3 and represented in various types of charts, such as bar charts, line chart and Donut chart, which helped to present the data in an easy-to-understand format.

These visualizations allowed for the identification of trends and patterns in the data, enabling the project to provide a comprehensive analysis of Netflix's performance.

Sales Analysis

The sales analysis in Python using pandas and matplotlib involved analyzing a company's sales data to identify trends, patterns, and insights that could be used to make data-driven business decisions. The analysis was conducted using two popular data science libraries in Python: pandas and matplotlib.

The resulting charts and graphs provided valuable insights into sales trends and patterns, including changes over time, comparisons across different regions or products, and relationships between variables such as sales volume and price. By leveraging the power of data science, this sales analysis helped to drive data-driven decisions and support business growth.

GEPHI (Social Network )

Social network visualization using Gephi involves importing social network data into the software, cleaning up the data, choosing a layout algorithm, applying filters, customizing the visualization, and analyzing the network structure and characteristics. Gephi provides various tools and metrics for network analysis, and the visualization can be exported in different formats for further analysis or presentation.

D3 (Netflix Analysis)

Netflix Data Analysis using Javascript Liberary {D3}

D3 (Data-Driven Documents) is a powerful JavaScript library used for creating interactive and dynamic data visualizations on the web. It provides a range of tools and functions for manipulating data and generating various types of charts, including bar charts, line charts, scatter plots, and geographic maps. D3 allows users to bind data to HTML, SVG, and CSS elements and apply data-driven transformations to those elements, creating dynamic visualizations that change based on the data being visualized. D3 is a popular choice for creating data visualizations because it is flexible, customizable, and can be integrated with other JavaScript libraries and frameworks for more complex web applications.

By using a variety of visualizations, the D3 project was able to provide a comprehensive analysis of Netflix's performance, highlighting key insights and trends that helped the company make data-driven decisions about its business strategies.

PYTHON (Sales Analysis)

Sales data analysis using Python Liberaries

Pandas is a Python library for data analysis and manipulation, which provides user-friendly data structures and tools for working with structured data, including time-series data. It also offers support for reading and writing data in various formats, such as CSV, Excel, and SQL databases. With pandas, you can easily clean, transform, and aggregate data, handle missing data, and perform statistical analysis.

Matplotlib, on the other hand, is a popular Python library for creating static visualizations. It offers a wide range of tools and functions for creating different types of charts and plots, such as line charts, scatter plots, bar charts, histograms, and more. Matplotlib is highly customizable, and you can adjust every aspect of your visualizations, from the axis labels and tick marks to the colors and styles of the lines or bars. It also supports multiple output formats, including PNG, PDF, and SVG, and can be integrated with other libraries like NumPy and pandas for data analysis and manipulation.

Regarding the Data Set

This sales analysis dataset contains information related to sales transactions recorded between January 1, 2019, and December 31, 2019, in a particular region. The dataset includes various columns, such as date and time, product category, product type, sales revenue, quantity sold, customer demographics, and marketing channels. The sales revenue column represents the total revenue generated by each transaction, while the quantity sold column indicates the number of units sold in each transaction. Additionally, the dataset includes information about customer demographics, such as age, gender, and location, as well as details about the marketing channels used to promote the products. Overall, this dataset provides useful information for sales analysis and prediction in the region under consideration.