A Revision List is a curated, task-tracked syllabus of technologies, frameworks, and concepts that a practitioner aims to learn or revisit, typically structured as a prioritised backlog with completion states. In a data science and AI context, such a list spans programming languages, ML frameworks, cloud platforms, DevOps tooling, and conceptual foundations such as deep learning, NLP, and reinforcement learning. It functions as a personal knowledge-gap audit and learning roadmap.
Semantic Classification
Content
- from here xandie985/data-scientist-roadmap2024 (github.com)
- 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 :LOGBOOK: CLOCK: [2024-04-19 Fri 12:26:41]—[2024-04-19 Fri 12:26:42] ⇒ 00:00:01 :END:
- Compute Engine :LOGBOOK: CLOCK: [2024-04-19 Fri 12:26:42]—[2024-04-19 Fri 12:26:42] ⇒ 00:00:00 :END:
- Cloud SQL :LOGBOOK: CLOCK: [2024-04-19 Fri 12:26:42]—[2024-04-19 Fri 12:26:42] ⇒ 00:00:00 :END:
- Cloud Functions :LOGBOOK: CLOCK: [2024-04-19 Fri 12:26:42]—[2024-04-19 Fri 12:26:42] ⇒ 00:00:00 :END:
- BigQuery :LOGBOOK: CLOCK: [2024-04-19 Fri 12:26:42]—[2024-04-19 Fri 12:26:42] ⇒ 00:00:00 :END:
- AI Platform (includes Vertex AI) :LOGBOOK: CLOCK: [2024-04-19 Fri 12:26:42]—[2024-04-19 Fri 12:26:42] ⇒ 00:00:00 :END:
- 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
- GCP (Google Cloud Platform)
- 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)
- 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:
- Airflow (Workflow orchestration tool)
- MLFlow (Machine learning lifecycle management)
- Prometheus (Monitoring and alerting system)
- Grafana (Data visualization and analytics tool)
- Git version control (e.g., GitLab, GitHub)
- Data Visualization Tools:
- Tableau
- Matplotlib (Python plotting library)
- Seaborn (Statistical data visualization library built on top of Matplotlib)
- Power BI (Microsoft business intelligence platform)
- Other:
- ETL (Extract, Transform, Load) processes
- Optimisation algorithms (can be broader than just machine learning)
- Distributed training
- Curse of dimensionality
- Financial modeling
- LLMs
- Lang-chain Agents
- Prompt engineering
- RAG
- Fine-tuning