Stacht - Data Science & Analytics

Data Processing and Cleaning in Data Science

Data preprocessing is a crucial step in any Data Science workflow. Raw data is often incomplete, inconsistent, or contains errors that can significantly impact the accuracy of analysis and model performance.

Feature Engineering and Selection

Feature engineering involves transforming raw data into meaningful representations that improve the performance of machine learning models.

Model Training and Evaluation in Data Science

Building a machine learning model involves multiple steps, including selecting an appropriate algorithm, training the model, and evaluating its performance.

Data Storage and Processing Architectures

Handling large-scale data efficiently requires robust storage and processing architectures. The choice of architecture depends on the volume, velocity, and variety of data being processed.