Learn to Analyze Text Data in Bash Shell and Linux

An animated course for learning data analytics and big data pre-processing with Bash shell scripting in Linux

This course is specifically designed to show you how to use Bash commands and shell programming to handle textual data which can be a csv format data or systems log file. In this course you will learn Bash by doing projects.

However, you need to understand the fact that Bash may not the best way to handle all kinds of data! But there often comes a time when you are provided with a pure Bash environment, such as what you get in the common Linux based Super-computers and you just want an early result or view of the data before you drive into the real programming, using Python, R and SQL, SPSS, and so on. Expertise in these data-intensive languages also comes at the price of spending a lot of time on them.

In contrast, bash scripting is simple, easy to learn and perfect for mining textual data! Particularly if you deal with genomics, microarrays, social networks, life sciences, and so on. It can help you to quickly sort, search, match, replace, clean and optimise various aspect of your data, and you wouldn’t need to go through any tough learning curves. We strongly believe, learning and using Bash shell scripting should be the first step if you want to say, Hello Big Data!

This course starts with some practical bash-based flat file data mining projects involving:

University ranking data
Facebook data
AU Crime Data
(Data sets are provided at the end of each section)

If you haven’t used Bash before, feel free to skip the projects and get to the tutorials part (supporting materials: eBook). Read the tutorials and then come back to the projects again. The tutorial section will introduce with bash scripting, regular expressions, AWK, sed, grep and so on. Finally, it gives you a concise beginner friendly guide to the big data landscape including an overview of the critical Big Data tools such as HDFS, MapReduce, YARN, Flume, Hive and more. The course finishes with a near-complete list of references to all the relevant command line and Big data tools.