Data Scientist

Data Scientist

Data Scientists giving meaning to data. They generate insights from data that are not visible by just analyzing it. They explore data and connect the dots in unique ways to help businesses truly unlock the potential in their data. This is why data science today when done right can serve as a great competitive advantage to businesses.

How Synctactic helps Data Scientists

The Synctactic Platform is built keeping in mind the entire life cycle of data science initiatives. Every feature is designed and built to make the job of a data scientist less to do with bringing the data together and more to do with building ML/DL models and solving business problems.

Feature Engineering

The platform allows data scientists to easily search for the data they are looking for, bring it together, transform it and enrich the data as well. The drag and drop interface makes it intuitive to select any of the pre-built operators and custom code engine blocks to prepare the data for machine learning. Synctactic’s automated type detection helps data scientists to easily aggregate and generate new features for their datasets.

Data Visualization

Data scientists can visualize their data using built-in charting libraries. Advanced visualizations like sankey diagrams, correlation heatmaps and treemaps let data scientists understand their data better. All data sources in Synctactic provides data scientists with stats such as row and column count, min-max values, mean and averages and several other information helping them make better data prep and feature engineering decsions.

Model Libraries

Common machine learning models are utilized by data scientists while solving business problems. These models are available on the platform through libraries such as SkLearn, SparkMLLib, H2O, TensorFlow and Keras. Data scientists need to select the features, select the models they want to train, input the model parameters and run the pipeline. The platform can execute the training of these models and provide metrics on the evaluation of these models like error rates, f-scores, AUC and ROC curves, confusion matrix etc.

Model Deployment

Data scientists usually rely on IT teams to set up their infrastructure and help them deploy their models into production. With Synctactic’s MLOps features, data scientists can easily deploy multiple models into production and generate REST apis for consumption by other applications. Real-time scoring capabilities and model drift monitoring help data scientists experiment and run their models in real-life scenarios rather than just being POCs restricted to test data.