### Data Science

Data science is the process of processing and interpreting data to extract insights and inform decision-making, often using machine learning and advanced statistical techniques.

## How Data Science Works

Data science starts with data processing, which involves data ingestion and decompression to collect and unpack data, followed by data cleaning to remove errors and inconsistencies. Next, data analytics is performed to extract insights. Machine learning and deep learning are then used to train models on the processed data, allowing them to learn patterns. These trained models can then make predictions or inferences, providing valuable insights and helping in decision-making processes.

## Explore Data Science Tools and Technologies

These tools, and more, are built on CUDA-X™ Data Science, a collection of open-source libraries that accelerate the data science and data processing ecosystem.

### NVIDIA cuDF

NVIDIA cuDF is an open source CUDA-X™ library that accelerates popular libraries like pandas, Polars, and Apache Spark on NVIDIA GPUs — delivering massive speed-ups for DataFrame operations with no code changes required.

### NVIDIA cuML

NVIDIA cuML is an open-source CUDA-X™ Data Science library that accelerates scikit-learn, UMAP, and HDBSCAN on GPUs — supercharging machine learning workflows with no code changes required.

### NVIDIA NeMo Curator

NVIDIA NeMo Curator provides pre-built accelerated pipelines to process multimodal data at scale, improving the performance of agentic systems.

### RAPIDS Accelerator for Apache Spark

The RAPIDS Accelerator for Apache Spark combines the power of the RAPIDS cuDF library and the scale of the Spark distributed computing framework, accelerating your existing Apache Spark applications with minimal code changes.

### NVIDIA Morpheus

NVIDIA Morpheus is a GPU-accelerated, end-to-end AI framework enabling developers to create optimized applications for filtering, processing, and classifying large volumes of streaming cybersecurity data.

### NVIDIA cuOpt

NVIDIA® cuOpt™ is a world-record-setting, GPU-accelerated logistics solver that uses heuristics, metaheuristics, and optimizations to calculate complex vehicle routing problems with a wide range of constraints.

### NVIDIA NeMo Retriever

NVIDIA NeMo™ Retriever is a collection of generative AI microservices enabling enterprises to seamlessly connect models to diverse business data and deliver highly accurate responses.

### NVIDIA cuVS

NVIDIA cuVS is an open-source library for GPU-accelerated vector search and data clustering. It enables higher throughput, lower latency, and faster index build times, and improves the efficiency of semantic search within pipelines and applications such as information retrieval or RAG.

### NVIDIA NeMo Data Designer

NVIDIA NeMo™ Data Designer generates high-quality, domain-specific synthetic data from scratch or seed examples—accelerating model development while eliminating privacy risks and data collection bottlenecks.

## Data Science Learning Library

### Filters

**Learning Level**
- Advanced 9
- Beginner 10
- General Interest 5
- Intermediate 62

**Content Type**
- Blog 95

### Showing 95 results

1. ### 3 pandas Workflows That Slowed to a Crawl on Large Datasets—Until We Turned on GPUs
2. ### 7 Drop-In Replacements to Instantly Speed Up Your Python Data Science Workflows
3. ### Accelerate Apache Spark ML on NVIDIA GPUs with Zero Code Change
4. ### Accelerate Decision Optimization Using Open Source NVIDIA cuOpt
5. ### Accelerate Deep Learning and LLM Inference with Apache Spark in the Cloud
6. ### Accelerated Vector Search: Approximating with NVIDIA cuVS Inverted Index
7. ### Accelerating Apache Parquet Scans on Apache Spark with GPUs
8. ### Accelerating Inference on End-to-End Workflows with H2O.ai and NVIDIA
9. ### Accelerating JSON Processing on Apache Spark with GPUs
10. ### Accelerating Large-Scale Data Analytics with GPU-Native Velox and NVIDIA cuDF
11. ### Accelerating Time Series Forecasting with RAPIDS cuML
12. ### Accelerating Vector Search: Fine-Tuning GPU Index Algorithms