With some knowledge of scripting and algorithm design, you can easily encapsulate a repetitive task in a loop that starts with a single command and runs in the background of your schedule. It makes a huge difference compared to manually renumbering 1000 files. The larger the data set, the greater the savings in researcher time, reduced human error and increased reproducibility and standardization. Here you’ll learn Bash scripting basics and be introduced to two of the most widely used programming languages, R and Python.

## Table of Contents

1. Basics of Algorithm Structure2. Introduction to Bash scripting

2.1 Process multiple files

2.2 Introduction to regular expression

3. Introduction to Python programming

3.1 Open-Close & Read-Write of data files

3.2 JSON module - encoding & decoding JSON data

3.3 Math module - various mathematical functions

3.4 Pandas library - data structure manipulation tool

3.5 NumPy library - multi-dimensional arrays parser

3.6 SciPy library - algorithms for scientific computing

4. Introduction to R programming

4.1 Base R - core functionality for statistical computing

4.2 Base R Graphics - traditional statistical graphing

4.3 ggplot2 - customizable graphs and charts

4.4 dplyr - data manipulation and transformation

4.5 tidyverse - advanced data manipulation, exploration and visualization

4.6 data.table - aggregation and manipulation of large data sets

5. Introduction to Julia programming

5.1 Julia setup: installation, environments and Jupyter integration