What This Covers (and Why Not A Bootcamp)

PYTHON: Python is a high-level, interpreted programming language used for web development, data analysis, machine learning, AI, automation, and creating software and games. This is the start of everything, where you'll be building your "data house" and career. These skills you'll keep refining and building upon. Everything here is custom-tailored to getting you employed and functional, doing the types of work that are common in both private and public sector work.

SQL: SQL is a standard language used for managing and manipulating databases, enabling users to create, retrieve, update, and delete data.

DOCKER: A platform that uses OS-level virtualization to deliver software in packages called containers, ensuring it works seamlessly in any environment.

KUBERNETES: An open-source platform for automating deployment, scaling, and managing containerized applications across clusters of hosts. (This relates to Docker, above. Once you have a software in a container, you may want to scale your application, manage it, etc.)

AWS, AZURE AND GCP BASICS: Learn how "the cloud" works, because most enterprises are going to work with at least some cloud services, and many of these cloud services will make your life easier.

AND THEN… THE EVERYDAY TASKS, IDEAS, PROCESSES, AND JOBS YOU NEED TO KNOW:

• Data Warehouses + Data Lakes + Data "Lakehouses": There are plenty of ways that data can be stored for enterprises, and you'll need to understand where it goes, how it goes there, how you put it in, how you take it out, and how you organize it. This is the "big data" part of what you probably see on LinkedIn. Data warehouses and lakehouses aren't that daunting. To be honest, it's more like data hoarding than anything. Most organizations just compulsively hoard data, whether it's valuable or not.
• Data Governance: Data has rules, and when you're engineering big data systems, you'll often need to create naming conventions, access controls.
• ETL (Extract, Transform, Load): Get data from one place to another. Twist it, pull it, turn it, bop it. Turn your data into something else, and then stick it where it belongs.
• Data Visualization: It's not just Tableau and PowerBI. There are plenty of open source tools out there that allow you to visualize your data, and turn it into actionable intelligence.
• Business Intelligence & Storytelling: If you're going to engineer all this data, it helps to have an idea how to advise your clients or coworkers on what all the data means, with context. Every industry is different, but the storytelling formats are very much the same.

BECOME A DATA DEALER. ONCE YOU'RE IN THE GAME, YOU…

• API Development: Develop APIs (Application Programming Interfaces) to facilitate efficient data extraction and integration. APIs provide a standardized way for different software applications to interact with each other. You're the data plug now. Bag it up, and get your data into distribution, so people can enjoy that sweet, sweet data.
• Data Pipeline Construction: You'll often need to design, build, and manage data pipelines, which are systems for extracting, transforming, and loading data from different sources into different destination formats.
• Database Design and Management: Designing and managing databases is an everyday thing. This involves organizing data in an efficient and meaningful manner to allow easy data storage, extraction, and manipulation.
• Data Cleaning and Preprocessing: Both data engineers and data analysts often need to clean and preprocess data, which involves dealing with missing, broken, corrupted, truncated, or inconsistent data and transforming it into a format that can be easily analyzed.
• Data Analysis (As a Function of Data Engineering): While this is a key task primarily for data analysts, you'll need to perform some level of data analysis to understand the requirements for their data pipelines or system you're building.

LET'S START SIMPLE, SO YOU KNOW WHAT TO EXPECT

Coding is simply giving instructions to a computer. Since a computer is a machine, you have to give it the correct order, process, and form. Sometimes, you'll sprinkle in a little math.

BECAUSE CODING BOOTCAMPS ARE FUCKING EXPENSIVE AS FUCK

I have hired people out of coding bootcamps. Most of them are good. I'm not going to talk shit about them, because for some people they have worked wonderfully and are totally worth the money.

But they all tend to range in price from $8,000 to $25,000. Some have repayment plans on the back, some require a monthly payment. Some get financed. Whatever the cost… it's a fucking lot of money.

For a lot of people, that's the cost of a car: a big commitment to something, not knowing how they'll actually like engineering and development. Baby steps, y'all.

IF YOU STILL WANT A BOOTCAMP LATER

If you finish this course and want to take a coding bootcamp to learn more, that's totally fine. You may also want to enroll in a local community college, or even start a 4-year program. Continuing your education is always a good idea.

If you choose to go the coding bootcamp route, here are some I personally recommend:

• Nashville Software School (https://nashvillesoftwareschool.com/)
• Galvanize (https://www.galvanize.com/hack-reactor/): VET TEC Benefits accepted for Veterans
• Flatiron (https://flatironschool.com/)