- Artificial Intelligence
- Data
- Data Science
- Features
- Generative AI
- Machine Learning
-
Modeling
- Autopilot Mode
- Classification
- Confusion Matrix
- Cross-Validation
- Deep Learning Algorithms
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Tuning
- Multiclass Classification
- Neural Network
- Open Source Model Infrastructure
- Overfitting
- Regression
- Training Sets, Validation Sets, and Holdout Sets
- Underfitting
- Predictions
- View global site search results
- A
- AI Engineer
- AI Observability
- AIOps
- Artificial Intelligence Wiki
- Automated Machine Learning
- Autopilot Mode
- B
- Big Data
- C
- Citizen Data Scientist
- Classification
- Cognitive Computing
- Confusion Matrix
- Cross-Validation
- D
- Data Collection
- Data Governance
- Data Insights
- Data Management
- Data Preparation
- Data Profiling
- Data Science
- Deep Learning Algorithms
- E
- Explainable AI
- F
- Feature Engineering
- Feature Impact
- Feature Selection
- Feature Variables
- G
- Generative AI
- L
- Large Language Model Operations (LLMOps)
- M
- Machine Learning
- Machine Learning Algorithms
- Machine Learning Life Cycle
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Machine Learning Operations (MLOps)
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Monitoring
- Model Tuning
- Multiclass Classification
- N
- Natural Language Processing
- Neural Network
- O
- Open Source Model Infrastructure
- Overfitting
- P
- Prediction
- Prediction Explanations
- Predictive Maintenance
- Production Model Governance
- Production Model Lifecycle Management
- R
- Regression
- S
- Scoring Data
- Semi-Supervised Machine Learning
- Stacked Predictions
- Supervised Machine Learning
- T
- Target Leakage
- Target Variable
- Text Mining
- Training Sets, Validation Sets, and Holdout Sets
- U
- Underfitting
- Unsupervised Machine Learning
- W
- What is Artificial Intelligence (AI)?
- View global site search results
- Artificial Intelligence
- Data
- Data Science
- Features
- Generative AI
- Machine Learning
-
Modeling
- Autopilot Mode
- Classification
- Confusion Matrix
- Cross-Validation
- Deep Learning Algorithms
- Machine Learning Model
- Machine Learning Model Accuracy
- Machine Learning Model Deployment
- Model Blueprint
- Model Fitting
- Model Interpretability
- Model Tuning
- Multiclass Classification
- Neural Network
- Open Source Model Infrastructure
- Overfitting
- Regression
- Training Sets, Validation Sets, and Holdout Sets
- Underfitting
- Predictions
Data Science
What is Data Science?
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.
Why Data Science is Important?
More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.
Data Science + DataRobot
Ramping up data science efforts is difficult even for companies with near-unlimited resources. The DataRobot AI Platform democratizes data science and AI, enabling analysts, business users, and other technical professionals to become Citizen Data Scientists and AI Engineers, in addition to making data scientists more productive. It automates repetitive modeling tasks that once occupied the vast majority of data scientists’ time and brainpower. DataRobot bridges the gap between data scientists and the rest of the organization, making enterprise machine learning more accessible than ever.
