Today, companies are producing huge amounts of data in terms of volumes in their offices and work places.This has led to large files in data stores or even more server space to store this data.Compared to traditional ways or days, where data was collected and then pre-processed, data nowadays is ready for use into different areas making work easier and faster.This has been made possible by algorithms running in some servers somewhere or offices like yours or mine e.g. Google Servers or Social Media sites like Facebook. Implementing these techniques of machine learning, enhancing insights and developing better knowledge base has led to the great technology era we are experiencing at the moment. As evident at Mastering Python data visualization , accurate and correct data leads to better information, knowledge and insights.
There exist great tools to aid in data analysis and visualization. A list can be found at Life in GIS comprising the libraries or tools that one can practice on to automate data analysis operations and also visualize in different ways. The choice of tool and algorithm are project dependent as not all of these are the same in terms of operation. As Jason Brownlee discusses at How to compare machine learning in Python algorithms , there a number of factors to consider when choosing an algorithm for your task.
I have personally tested a number of machine learning algorithms and tools and its a superb sector worth taking a look at if you are handling big data at work, or even in your business.For example, I have used the Anaconda distribution which houses over 720 open source packages to enhance analysis and actensure a one-stop tool availability. As discussed at Using Jupyter for data analysis, this is an extremely great package manager aiding easier and comprehensive data analysis operations. In this manager, tools exist which one can choose from depending on your task objectives. These include Plotly, Pandas, Jupyter, matplotlib, Bokeh just to mention a few.
Just to sample a few of the works completed using these tools; As illustrated in the examples at Bubble maps ;
Charts and graphs can also be generated using these tools such as this one generated using scikit-learn at machine learning mastery;
Plotting data in different aspects
As seen in these examples, a lot can be done to derive useful insights, information and knowledge from our data.This was an overview, more details to come.
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