Gain insights into data processing using Bash, sed, awk, and RegEx. Explore sorting, searching, replacing, and optimizing data tasks interactively with animated videos, projects, quizzes, and tasks.

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2500+ students have taken this innovative project-based data learning course (includes video lectures). It demonstrates the use of Bash shell (Bash, sed and awk including RegEx) in processing textual data. It can help to learn to sort, search, match, replace, clean and optimize various aspects of data with Bash Shell. The target audience (students, researchers, scientists, journalists, data miners, developers) didn't have to go through any tough learning curve. This course also should have helped RedHat, SuSE and Ubuntu Linux learners and Data Science enthusiasts.  Regularly updated, new projects to come!

- Learn Bash commands interactively
- Projects with own stories and conclusive decisions
- Animated video lectures (for visual learners)
- Demonstrations
- Quizzes
- Learning tasks

Bloom's taxonomy (remember, understand, apply, analyze, evaluate and create) in developing your Linux skills. Learn Scientific Programming! scientificprogramming.io

Learn Data Science with Bash Shell

Apache Pig offers a high-level scripting language, popularly used for the writing of data pipelines in the world of Hadoop. The scripting language is known as `Pig Latin`. It appears as a blend of SQL and many other scripting languages, it is reasonably simple to read and write, so you tend to move up and running with the use of Pig Latin pretty rapidly. It is explanatory and concise just like SQL. You also enjoy more explicit control on various datasets at each phase in the pipeline.

Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties:
- Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.

-    Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.
-    Extensibility. Users can create their own functions to do special-purpose processing.


https://pig.apache.org/

Course Introduction

Project 1: Analyzing the 'US News' University Ranking Data

Project 2: Facebook Data Mining

Project 3: Australian Cities Crime Statistics

Project 4: Shakespearean-era plays and poems data mining

Bash Tutorials

REGEX Tutorials

AWK Tutorials

SED, GREP and Find Tutorials

Beyond the Text Files! Enter into the Big Data Landscape - Concepts

Conclusion

Pig