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Python and Big DataPurdue University GlobalBig data is used by many organizations to predict behavior, gather consumer data, and much more. What does the term big data mean what does it pertain too? In the article oBig Data? by Troy Segal, he explains that big data is a term used to describe diverse sets of information that grow at ever-increasing rates. The article goes on to categorize big data into structured or unstructured, structured data is data that is managed by the organization whereas unstructured is data that is uncategorized. Python is an excellent analytical tool to decipher this data into a more usable form. Python is a simple to use programming language that has a vast number of libraries that can be scaled to tackle any job. Whether analyzing a small amount of data or an endless amount python is a scalable language that has tools and libraries that can be used across many different web applications. In the article oWhy is Python Programming a perfect fit for Big Data?
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Big Data Python and Big Data Purdue University Global Big data is used by many organizations to predict behavior, gather consumer data, and much more. What does the term big data mean what does it pertain too? In the article “Big Data” by Troy Segal, he explains that big data is a term used to describe diverse sets
Big Data of information that grow at ever-increasing rates. The article goes on to categorize big data into structured or unstructured, structured data is data that is managed by the organization whereas unstructured is data that is uncategorized. Python is an excellent analytical tool to decipher this data into a more usable form. Python is a simple to use programming language that has a vast number of libraries that can be scaled to tackle any job. Whether analyzing a small amount of data or an endless amount python is a scalable language that has tools and libraries that can be used across many different web applications. In the article “Why is Python Programming a perfect fit for Big Data?” by Scarlett Rose, gives several benefits of using python as an analytical tool for big data because it is a simple language, open source, and has a vast amount of libraries for; data analysis, statistical analysis, and numerical computing. This is how python can be a powerful tool for analyzing data to be used by an organization for reaching is consumer base or advertising to new markets. Before the smartphone and social media data was quite small. Companies used data like names, phone numbers, zip code, etc. to determine consumer information. Now with the invention of the smartphone and everything that came along after it, organizations can now gather data that goes beyond our name but data about what we like to eat, where we like to shop, and so much more. This data that is accumulated can be used to determine customer behavior and much more. In the article “Finding Hidden Customer Behavior Patterns Using Big Data Analytics” by Ivan Martinez, explains that businesses can use analytical tools to data mine information about its consumers to better advertise products and services that they are more likely to buy. Combining this data with behavioral analytics software helps give businesses insights on human behavior to market
Big Data several others. Pandas is a high-performance data analysis tool than can be used for manipulating, updating, or plotting the data in a histogram. A scenario for using Panda could be used for shopping. Imagine you want to keep details about your grocery shopping well Pandas can help. You can store details like the name of the store where you shop, the location, and the price you spend at these locations. You can use Pandas to create a dataframe than can later accept variables. Next you create an input to ask details about your shopping visit like, when and where you went as well as how much you spent. You append this data to the existing list and can display the results via dataframe. The results will display a list of when you went shopping, where you went, how much you spent, or any other details you would like to know. Using Pandas we were able to create a program that can accept new inputs about a user shopping and display the results in a legible easy to read fashion. Bibliography
Big Data Bronshtein, Adi. “A Quick Introduction to the ‘Pandas’ Python Library.” Medium , Towards Data Science, 29 Oct. 2019, towardsdatascience.com/a-quick-introduction-to-the-pandas-python-library- f1b678f34673. Desai, Rashi. “Top 10 Python Libraries for Data Science.” Medium , Towards Data Science, 20 Dec. 2019, towardsdatascience.com/top-10-python-libraries-for-data-science-cd82294ec266. Kumar, Krishna. “Why Python Is Important for Big Data and Analytics Applications?” Eduonix Blog , 26 Apr. 2016, blog.eduonix.com/bigdata-and-hadoop/python-important-big-data-analytics- applications/. Madan, Naren. “3 Ways Big Data Can Influence Decision-Making for Organizations.” Simplilearn.com , Simplilearn, 28 Aug. 2020, www.simplilearn.com/how-big-data-can-influence-decision-making- article. Martinez, Ivan. “Finding Hidden Customer Behavior Patterns Using Big Data Analytics .” Dzone.com , DZone, 8 Mar. 2017, dzone.com/articles/find-hidden-customer-behavior-patterns-using-big-d. Rose, Scarlett. “Why Is Python Programming a Perfect Fit for Big Data?” Medium , Towards Data Science, 27 Dec. 2019, towardsdatascience.com/why-is-python-programming-a-perfect-fit-for-big- data-5ac54ee8f95e. Segal, Troy. “Big Data.” Investopedia , Investopedia, 5 July 2019, www.investopedia.com/terms/b/big- data.asp.
