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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📢 Resource Alert: UCI Machine Learning Repository

If you're looking for datasets to practice and experiment with machine learning, check out the UCI Machine Learning Repository!

It's a long-standing resource, widely used by students, educators, and researchers to access a variety of datasets for ML projects.

Explore it here: https://archive.ics.uci.edu/datasets
@javascript_resources
#MachineLearning #DataScience #AI #Resources

💡 A complete package for success in data science and machine learning interviews!

👩🏻‍💻 I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!

❓ What do you find in it?

1⃣ Practical cheat sheets: Important tips gathered in one place.

🔢 Cool books: resources worth your time!

🔢 Frequently Asked Interview Questions: Topics that are asked in most interviews and that you are likely to encounter.

🔢 Portfolio projects: To make your resume stronger.


✅ In short, a complete package for preparing for data science interviews, without the confusion!

🔗 Here is the link: 👇

🔗 Cracking the data science interview
https://github.com/khanhnamle1994/cracking-the-data-science-interview?tab=readme-ov-file

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⚠️ 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 .

✔️ To use the online and PDF versions of these books, you can use the following links:👇

0⃣ Python Data Science Handbook
┌ Online
└ PDF

1⃣ Python for Data Analysis book
┌ Online
└ PDF

🔢 Fundamentals of Data Visualization book
┌ Online
└ PDF

🔢 R for Data Science book
┌ Online
└ PDF

🔢 Deep Learning for Coders book
┌ Online
└ PDF

🔢 DS at the Command Line book
┌ Online
└ PDF

🔢 Hands-On Data Visualization Book
┌ Online
└ PDF

🔢 Think Stats book
┌ Online
└ PDF

🔢 Think Bayes book
┌ Online
└ PDF

🔢 Kafka, The Definitive Guide
┌ Online
└ PDF

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https://t.me/javascript_resources ✅

Looking to level up your knowledge in Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI)?

Check out this comprehensive cheat sheet compiled by experts from Stanford University and MIT! It covers:

✅ Probability & Statistics – The backbone of ML & AI
✅ Supervised Learning – Linear regression, logistic regression, SVMs, and more
✅ Unsupervised Learning – Clustering, PCA, ICA, and dimensionality reduction
✅ Deep Learning – Neural networks, CNNs, RNNs, reinforcement learning
✅ Mathematical Foundations – Linear algebra, calculus, optimization
✅ ML Tips & Tricks – Model selection, performance metrics, and debugging
@javascript_resources
A must-have for anyone diving into AI, whether you're a beginner or a pro!

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Free Certification Courses to Learn Data Analytics in 2025:

1. Python
🔗 https://imp.i384100.net/5gmXXo

2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
🔗https://imp.i384100.net/rQqomy

9. Tableau
🔗https://imp.i384100.net/MmW9b3

10. PowerBI
🔗 https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N

12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
🔗http://matlabacademy.mathworks.com

14. Statistics
🔗 https://lnkd.in/df6qksMB

15. Data Visualization
🔗https://imp.i384100.net/k0X6vx

16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9

17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling

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
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY

26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2

27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://t.me/javascript_resources ✈️