Mastering the tools in this guide - programming languages, machine learning libraries, and cloud platforms - is crucial for data science success. I have marked the:

mandatory and easiest as green,

the mediocre tough as yellow and ****

red denotes the toughest & pro. ****

Programming Languages:

• Python
• R

Frameworks & Libraries:

• Scikit-learn
• Numpy
• Pandas
• TensorFlow
• PyTorch
• XGBoost
• LightGBM
• Keras (High-level deep learning API)
• Jax (High-performance numerical computation)
• CatBoost (Gradient boosting framework)
• StaMPS (Scalable Modeling and Partitioning for Statistics)

Cloud Platforms & Services:

• Docker (Containerization platform)
• Learn any one of the following:
  • GCP (Google Cloud Platform)
    • Cloud Storage
    • Compute Engine
    • Cloud SQL
    • Cloud Functions
    • BigQuery
    • AI Platform (includes Vertex AI)
  • Azure (Microsoft Azure)
    • Blob Storage
    • Virtual Machines
    • SQL Database / Azure Database for PostgreSQL/MySQL
    • Azure Functions
    • Azure Synapse Analytics
    • Azure Machine Learning
  • AWS (Amazon Web Services)
    • AWS S3
    • AWS EC2
    • AWS RDS
    • AWS Lambda
    • AWS Redshift
    • AWS SageMaker
• Kubeflow (Cloud-native machine learning platform)
• Kubernetes (Container orchestration platform)

Data Tools & Libraries:

• SQL (including OLAP & OLTP variations)
• Pandas
• Elasticsearch
• Dask (Parallel computing library for big data)
• Spark (Large-scale data processing framework)
• Airbyte (Open-source data integration platform)

Web Development Frameworks:

• FastAPI
  • Uvicorn (likely mentioned in conjunction with FastAPI)
• Django
• Gradio
• Streamlit (Machine learning app development framework)

Machine Learning Concepts:

• Supervised Learning
  • Regression
  • Classification
• Unsupervised Learning
  • Clustering
  • Dimensionality Reduction
• Recommendation Systems
• Time Series Forecasting
• Natural Language Processing (NLP)
  • Text Mining
  • Natural Language Understanding (NLU)
    • Sentiment Analysis
    • Named Entity Recognition (NER)
    • Question Answering (QA)
  • Natural Language Generation (NLG)
• Deep Learning Techniques
  • Convolutional Neural Networks (CNNs)
  • Long Short-Term Memory networks (LSTMs)
  • Generative AI
• Reinforcement Learning
• Bayesian Optimization

DevOps & MLOps Tools:

• Git version control (e.g., GitLab, GitHub)
• Airflow (Workflow orchestration tool)
• MLFlow (Machine learning lifecycle management)
• Prometheus (Monitoring and alerting system)
• Grafana (Data visualization and analytics tool)

Data Visualization Tools:

• Matplotlib (Python plotting library)
• Seaborn (Statistical data visualization library built on top of Matplotlib)
• Power BI (Microsoft business intelligence platform)
• Tableau