Join dotData Sr. Data Scientist Sharada Narayanan as she dives into the strengths, uses, and limitations of popular time-series forecasting techniques like ARIMA and Prophet. Sharda will walk through real-world examples, share code snippets, and explore how ARIMA Prophet compare when building models using Feature Engineering techniques and advanced machine learning algorithms. Dive into the insights of each method and see how programmatic feature engineering and machine learning can supercharge your time series analysis.

Join dotData Sr. Data Scientist Sharada Narayanan as she dives into the strengths, uses, and limitations of popular time-series forecasting techniques like ARIMA and Prophet.

Sharada will walk through real-world examples, share code snippets, and explore how ARIMA Prophet compare when building models using Feature Engineering techniques and advanced machine learning algorithms. Dive into the insights of each method and see how programmatic feature engineering and machine learning can supercharge your time series analysis.

The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI.

In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.

The past decade has seen rapid development of Artificial Intelligence (AI) and Machine Learning (ML) across different industries and for a multitude of successful use cases. However, one key challenge many businesses face for larger-scale adoption of AI and ML is that their data is often not ready for AI/ML. Automated feature engineering is a technology that aims to address the fundamental challenges of data readiness for AI. In this talk, we will review automated feature engineering technology and discuss how data scientists can benefit from this technology to transform your data and enable AI applications.