IT Career Paths 2023

Career Paths Introduction

Look at different paths, level, tasks and responsibilities, and skill of fields as of year 2023
It is never too late to get into IT
Popularity based on growth, pay/demand - IT engineers:
- Software
- DevOps
- Cloud
- Security
- Data
- Machine learning
Lay out a map to learn these paths

Try out multiple things, see options and opportunities, but do in structured learning
Learn:
- Programming basics - same for all languages
  - Suggestion: JavaScript / most popular language
    - Due to use of front and back end, mobile, and easier to learn than say Java
  - Learn end to end application development
    - Say web with HTML and CSS
    - Mobile app
    - Develop an application you can use rather than a tutorial
- Concepts
- Learning one path will help with other paths if you change your mind later
- Learn one path at a time, at least 6 months at a time
- Complete software development life cycle

See Also Nana’s DevOps and Beginner’s IT bootcamp

Software engineer / system administration / networks / testers can transition to DevOps, recommended with IT background
Automation of processes in software engineering

Build and maintains infrastructure in cloud
Can be entry level
In demand due to migrations to cloud, can be related to DevOps
How to started? learn popular cloud platforms like AWS, Microsoft Azure - get platform certifications (e.g. AWS cloud practitioner certificate

Security is common to all IT fields due to need to secure sensitive data and affect all areas of IT
- High demand and value due to cost of breaches
Specialize in security
- Identify issues, vulnerabilities
  - Multiple layers, people protection
- Fix issues, test
- Validate security
- Secure organization
Understand concept of what you are securing first like - network, data (like passwords), software
- Sponsored technology: passbolt - opensource password manager with enterprise support

Big data: media, apps, machine generated, high data growth each year
Used for optimization, making decisions - data only has value after processing, analysis, and visualization and consumed by people like decision makers
Data analyst - entry level
- Analyze and interpret processed data
- Skills of math and statistics, data visualization, tools (python, R), communication, business understanding
Data engineer
- Process data from base data
- Skill examples
  - Programming: Python, Java, Scala
  - Database and queries: SQL
  - Big data: Apache Hadoop
Data scientist - usually highest paid in this path
- Similar to data analyst, but require advanced math, statistics, analysis skills like computer science python, Java, SQL, and machine learning
- Make future predictions

Related to big data, except using data with machines for outcomes
Take data from data engineer and feed to machines - machines can perform tasks without being explicitly programmed to do a task
Write ML algorithms, train the ML model by feeding it with data
Overlap with data engineering with math and statistics skills
Skills:
- Programming: Python
- ML learning libraries, frameworks
- Math and statistics

Language:
- Python is present in most paths - python is a general purpose language, but each path uses it differently
  - Have libraries useful for each path
Online course, college/university, bootcamp, self teaching
Nana’s combination was university, then work in industry and online self teaching
Important is roadmap for learning to structure
Choose entry level career and start learning