Assignment :
Please work through the following tutorials located at the following locations:
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier. Pandas builds on packages like NumPy and matplotlib to give you a single, convenient, place to do most of your data analysis and visualization work. In this python data science tutorial, you’ll use Pandas to analyze data on video game reviews from IGN, a popular video game review site. The data was scraped by Eric Grinstein, and can be found here. As you analyze the video game reviews, you’ll learn key Pandas concepts like indexing.
Exercise 1 Link: https://www.dataquest.io/blog/pandas-python-tutorial/
You need basic Python knowledge for this tutorial. If you understand if-else statements, while and for loops, lists, and dictionaries, you’re set to make the most out of this tutorial. You also need a code editor like Visual Code Studio, PyCharm, or Atom. In addition, while we walk through every line of code so you never feel lost, knowing basic pandas would help. Check out our pandas tutorial if you need a refresher.
Exercise 2 Link: https://www.dataquest.io/blog/regular-expressions-data-scientists/
Please screenshot your results and upload them to this Assignment Link.
Discussion :
Many data science, analyst, and technology professionals have encountered regular expressions at some point. This esoteric, miniature language is used for matching complex text patterns, and looks mysterious and intimidating at first. However, regular expressions (also called “regex”) are a powerful tool that only require a small time investment to learn. They are almost ubiquitously supported wherever there is data.
What are regular expressions? Why are regular expressions useful? How would you use regular expressions in data visualizations?
Discussion Length (word count): At least 250 words (not including direct quotes).
References: At least two peer-reviewed, scholarly journal references.
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