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Brian Harris
Brian Harris
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For a first DS/ML stack, pick one language and master it end-to-end. Python is usually the most practical start because its ecosystem (NumPy, pandas, Matplotlib/Seaborn, scikit-learn; plus PyTorch/TensorFlow) makes data work fast to prototype and easy to learn from abundant tutorials and examples.

Java shines later in production settings (JVM services, Spark on large clusters, enterprise codebases), but its DS ergonomics are heavier for beginners. Learning options: A structured course can be helpful to understand how Java and Python are used in real DS/ML workflows. For fundamentals, consider Great Learning Academy’s Python Programming course; for a cohort-based, project-driven path, Le Wagon’s Data Science course; and for a beginner-friendly, practice-first approach, CodeWithHarry’s The Ultimate Job-Ready Data Science Course. Choose based on your schedule, budget, and learning style—thestyle,the goal is to ship 2–3 portfolio projects, not just finish a syllabus.

For a first DS/ML stack, pick one language and master it end-to-end. Python is usually the most practical start because its ecosystem (NumPy, pandas, Matplotlib/Seaborn, scikit-learn; plus PyTorch/TensorFlow) makes data work fast to prototype and easy to learn from abundant tutorials and examples.

Java shines later in production settings (JVM services, Spark on large clusters, enterprise codebases), but its DS ergonomics are heavier for beginners. Learning options: A structured course can be helpful to understand how Java and Python are used in real DS/ML workflows. For fundamentals, consider Great Learning Academy’s Python Programming course; for a cohort-based, project-driven path, Le Wagon’s Data Science course; and for a beginner-friendly, practice-first approach, CodeWithHarry’s The Ultimate Job-Ready Data Science Course. Choose based on your schedule, budget, and learning style—the goal is to ship 2–3 portfolio projects, not just finish a syllabus.

For a first DS/ML stack, pick one language and master it end-to-end. Python is usually the most practical start because its ecosystem (NumPy, pandas, Matplotlib/Seaborn, scikit-learn; plus PyTorch/TensorFlow) makes data work fast to prototype and easy to learn from abundant tutorials and examples.

Java shines later in production settings (JVM services, Spark on large clusters, enterprise codebases), but its DS ergonomics are heavier for beginners. Learning options: A structured course can be helpful to understand how Java and Python are used in real DS/ML workflows. For fundamentals, consider Great Learning Academy’s Python Programming course; for a cohort-based, project-driven path, Le Wagon’s Data Science course; and for a beginner-friendly, practice-first approach, CodeWithHarry’s The Ultimate Job-Ready Data Science Course. Choose based on your schedule, budget, and learning style,the goal is to ship 2–3 portfolio projects, not just finish a syllabus.

Source Link
Brian Harris
Brian Harris
  • 19
  • 4

For a first DS/ML stack, pick one language and master it end-to-end. Python is usually the most practical start because its ecosystem (NumPy, pandas, Matplotlib/Seaborn, scikit-learn; plus PyTorch/TensorFlow) makes data work fast to prototype and easy to learn from abundant tutorials and examples.

Java shines later in production settings (JVM services, Spark on large clusters, enterprise codebases), but its DS ergonomics are heavier for beginners. Learning options: A structured course can be helpful to understand how Java and Python are used in real DS/ML workflows. For fundamentals, consider Great Learning Academy’s Python Programming course; for a cohort-based, project-driven path, Le Wagon’s Data Science course; and for a beginner-friendly, practice-first approach, CodeWithHarry’s The Ultimate Job-Ready Data Science Course. Choose based on your schedule, budget, and learning style—the goal is to ship 2–3 portfolio projects, not just finish a syllabus.