Python for data visualization and data analysis

With the rapid growth of the Internet, we are generating more data than ever before. A recent report by Domo claims that 2.5 quintillion bytes are created daily! That’s a lot of data, and if used wisely, it can help businesses make more efficient decisions. Data visualization is an easy way to look at large amounts of data and understand patterns in the data. An interesting example of how data visualization can help people understand data better is the image below written and drawn by Florence Nightingale regarding causes of death in a buy brazilian sms number during the Crimean war. Instead of showing a table with numbers, showing the data visually makes the data more meaningful and impactful.

Data visualization created by Florence Nightingale

Libraries like matplotlib and seaborn make viewing super easy. With just a few lines of programming, you can build bar graphs, heat maps or data distribution. In data analysis you use the data visualization, along with other parameters like mean, median and mode, to understand the data and make decisions based on it. Understanding and presenting data to guide business decisions can make significant savings. For example, Netflix uses data visualization to understand what factors help subscribers stay subscribed. Incorporating these findings into the customizations and recommendation engines. Netflix saves about $8 billion a year.

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Data visualization using Seaborn

Web development includes all activities used to create DV Leads and web-based applications. There are two parts to a Website – Client-side where the code runs in the browser of the user’s computer; and Server-side, where the code runs on the web server. For example, JavaScript is a client-side language that runs in the browser, and allows user interaction on a website. Python, on the other hand, runs on the server, and is responsible for processing the logic of User Inputs, interacting with databases and other servers, etc. Python developers can use the web’s own frameworks to quickly and efficiently build dynamic web applications, without having to learn a client-side language like JavaScript. Frameworks significantly reduce development time by automating common web tasks.

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