1.**Visualizations of Correlations between Features**: Data scientists excel at exploring and visualizing data to uncover meaningful patterns and relationships. By creating heatmaps and visualizations of correlations between features, they provide insights into the interdependencies and associations within the dataset. These visualizations help stakeholders understand the relationships between variables, identify influential factors, and make informed decisions based on data-driven insights. By publishing the results on Ocean, you can allow others to build on your work.
2.**Conducting Feature Engineering**: Feature engineering is a critical step in the data science workflow. Data scientists leverage their domain knowledge and analytical skills to engineer new features or transform existing ones, creating more informative representations of the data. By identifying and creating relevant features, data scientists enhance the predictive power of models and improve their accuracy. This process often involves techniques such as scaling, normalization, one-hot encoding, and creating interaction or polynomial features.
3.**Conducting Experiments to Find the Best Model**: Data scientists perform rigorous experiments to identify the most suitable machine learning models for a given problem. They evaluate multiple algorithms, considering factors like accuracy, precision, recall, and F1-score, among others. By systematically comparing different models, data scientists select the one that performs best in terms of predictive performance and generalization. This process often involves techniques such as cross-validation, hyperparameter tuning, and model selection based on evaluation metrics. 
4.**Testing Out Different Models**: Data scientists explore various machine learning models to identify the optimal approach for a specific problem. They experiment with algorithms such as linear regression, decision trees, random forests, support vector machines, neural networks, and more. By testing out different models, data scientists gain insights into the strengths and weaknesses of each approach, allowing them to select the most appropriate model for the given dataset and problem domain.
5.**Deploy a model with Compute-to-Data:** After building a robust model, data scientists can utilize C2D to deploy their model for personal or third-party use. At this final stage of value creation for data scientists, they can provide direct value to the ecosystem by driving data consume volume and overall usage of Ocean Protocol.  
