Unlock Data Analysis with Go: The Modern Approach

Go (Golang) has emerged as one of the fastest and most efficient technologies for system development, yet its application in data analysis is still underexplored. This course will guide you step-by-step on using Go for loading, transforming, analyzing, and visualizing data in a professional, modern, and automated manner. We’ll begin with the fundamentals of the language, covering control structures, types, functions, errors, and code organization. You’ll learn to process CSV files without libraries, manipulating each line, validating data, and saving transformed results.

After mastering the basics, you will leverage libraries like ‘gota’ and ‘gonum’ to work with DataFrames, apply filters, create new columns, clean null data, and calculate statistics, percentiles, correlations, or even simple linear regression. Techniques such as grouping, file merging, and generating metrics by groups will also be covered. For visualization, you’ll explore tools like ‘asciigraph’ for terminal graphs, ‘gonum/plot’ for static images in PNG format, and optionally ‘go-echarts’ for exportable HTML dashboards.

A key highlight of this course is the incorporation of concurrency in Go, where you’ll learn to parallelize file processing using goroutines, channels, and WaitGroup, optimizing execution times and scalability. Finally, you will build a command-line tool with Cobra, automating an entire workflow that can read data, analyze it, and export results in CSV, JSON, or Excel. You’ll conclude with a final challenge based on a real dataset, implementing best practices such as structured logging, business validations, and automated testing. This course not only teaches you how to use Go for data analysis but also provides the tools to build high-performance, scalable solutions.

What you will learn:

• Understanding the fundamentals of Go and its application in data analysis.
• Processing CSV files, cleaning, and transforming data without external libraries.
• Working with DataFrames, filters, statistics, and linear regression using gonum.
• Visualizing data with terminal graphs, PNG images, or HTML dashboards.
• Applying concurrency in Go for efficient parallel data processing.
• Creating CLI tools, automating workflows, and facing a final challenge with a real dataset.

Course Content:

• Sections: 11
• Lectures: 50
• Duration: 6h 13m

Requirements:

• Basic programming knowledge (preferably in a language like Python or JavaScript).
• A computer with Windows, macOS, or Linux.

Who is it for?

• Programmers looking to learn Go applied to data analysis.
• Developers seeking more efficient and concurrent alternatives to Python or R.
• Data scientists wanting to automate processes and create CLI tools in Go.
• Students or self-learners interested in using Go beyond traditional backend development.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

👉 Don’t miss this coupon! – Cupón DIC25-W2