Level 1

<aside> 🛠 Data Engineering

</aside>

• [ ] Small datasets
• [ ] Tabular data
• [ ] Simple data sources (CSV, Excel, JSON)
• [ ] Simple preprocessing steps

<aside> 📊 Data Analysis

</aside>

• [ ] Data Visualization Libraries
• [ ] Statistics and Probability Fundamentals
• [ ] Basic Exploratory Data Analysis

<aside> 🤖 Machine Learning

</aside>

• [ ] Simple Regression
• [ ] Simple Classification
• [ ] Evaluation Metrics

<aside> 👩‍💻 Software Engineering

</aside>

• [ ] Jupyter Notebooks
• [ ] Code versioning (git)
• [ ] Terminal navigation
• [ ] Virtual environments & dependencies

Level 2

<aside> 🛠 Data Engineering

</aside>

• [ ] Medium size datasets
• [ ] Time-Series data
• [ ] Text data (NLP)
• [ ] Relational Databases (SQL)
• [ ] Preprocessing Pipelines

<aside> 📊 Data Analysis

</aside>

• [ ] Chart Design Principles
• [ ] Advanced Exploratory Data Analysis
• [ ] Data Storytelling

<aside> 🤖 Machine Learning

</aside>

• [ ] Advanced Regression
• [ ] Advanced Classification
• [ ] Time-Series specific models
• [ ] Text specific (NLP) models
• [ ] Clustering

<aside> 👩‍💻 Software Engineering

</aside>

• [ ] Advanced IDEs (PyCharm, VSCode, etc.)
• [ ] Unit Testing
• [ ] CI/CD Pipelines

Level 3

<aside> 🛠 Data Engineering

</aside>

• [ ] Large datasets
• [ ] Image data
• [ ] Audio data
• [ ] Imbalanced datasets
• [ ] Collect / generate own data (Web Scraping, APIs)
• [ ] Non-Relational Databases (No-SQL)

<aside> 📊 Data Analysis

</aside>

• [ ] Dashboard Design Principles
• [ ] BI-Tools (Tableau, PowerBI, etc.)

<aside> 🤖 Machine Learning

</aside>

• [ ] AutoMLs
• [ ] Model tuning
• [ ] Feature Importance
• [ ] Experiment tracking
• [ ] Neural networks

<aside> 👩‍💻 Software Engineering

</aside>

• [ ] Cloud provider environment (AWS, GCP, Azure)
• [ ] Model Deployment (Docker)
• [ ] Model lifecycle management (MLFlow, Metaflow, etc.)