The Ultimate DevOps Developer Roadmap

1 - Programming Languages
Pick and master one or two programming languages. Choose from options like Python, JavaScript, Go, Ruby, etc.

2 - Operating Systems
Master the ins and outs of major operating systems like Linux, Windows, Mac, and so on.

3 - Source Control Management
Learn about source control management tools such as Git, GitHub, GitLab, and Bitbucket.

4 - Networking
Master the basics of networking concepts such as DNS, IP, TCP, and SSH.

5 - CI/CD
Pick tools like GitHub Actions, Jenkins, or CircleCI to learn about continuous integration and continuous delivery.

6 - Scripting and Terminals
Learn scripting in bash, and PowerShell along with knowledge of various terminals and editors.

7 - Hosting and Platforms
Master multiple hosting platforms such as AWS, Azure, GCP, Docker, Kubernetes, Digital Ocean, Lambda, Azure Functions, etc.

8 - Infrastructure as Code
Learn infrastructure as code tools like Terraform, Pulumi, Ansible, Chef, Puppet, Kubernetes, etc.
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9 - Monitoring and Logging
Master the key tools for monitoring and logging for infrastructure and applications such as Prometheus, Elasticsearch, Logstash, Kibana, etc.

10 - Basics of Software Development
Learn the basics of software development such as system availability, data management, design patterns, and team collaboration.

Over to you: What else would you add to this roadmap?

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How to use Python to automate & monitor — Dark Web OSINT



Blog 1: https://publication.osintambition.org/python-for-dark-web-osint-automate-threat-monitoring-5994b63c4d4a

Blog 2: https://codewithc.com/dark-web-monitoring-using-python/
@javascript_resources
#CyberSecurity
#Python #OSINT #DarkWeb #DarkWebOSINT #PythonProgramming

⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .

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🔢 Fundamentals of Data Visualization book
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🔢 R for Data Science book
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Free Certification Courses to Learn Data Analytics in 2025:

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2. SQL
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4. Data Science: R Basics
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5. Excel and PowerBI
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6. Data Science: Visualization
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7. Data Science: Machine Learning
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8. R
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9. Tableau
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10. PowerBI
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11. Data Science: Productivity Tools
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12. Data Science: Probability
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13. Mathematics
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15. Data Visualization
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16. Machine Learning
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17. Deep Learning
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18. Data Science: Linear Regression
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19. Data Science: Wrangling
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20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
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24. Data Analysis
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25. IBM Data Science Professional Certificate
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27. Supervised Machine Learning: Regression and Classification
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https://t.me/javascript_resources ✈️