- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
Data Science
What is Data Science?
Data Science is an interdisciplinary field of study that combines the use of domain expertise, mathematics/statistics knowledge, and computer programming to obtain value from data. Data scientists can apply machine learning algorithms to numbers, text, images, video, and audio in order to extract actionable insights and produce artificial intelligence systems. These systems can perform tasks that traditionally require human input.
Applications of Data Science
Data science apps can be used to manage promotions and discounts in real-time. It can also scan social media networks to help forecast what products will be in high demand in the future.
Credit Scoring
An analysis of a customer’s banking history through data science can estimate creditworthiness. This helps banks with risk management.
Sepsis Prevention
AI can be used to predict treatment or dosage levels that are likely to be most effective based on patient history. Early detection and more precise care results in less aggressive and less costly treatments.
Why is Data Science Important?
Data science, artificial intelligence, and machine learning are helping organizations remain relevant in competitive markets. When implemented correctly, data science helps organizations mitigate risks by better navigating future market trends and consumer behaviors.
Data Science FAQs
What are examples of how data science can be used?
Data Science is an interdisciplinary field of study that combines statistics, scientific methods, artificial intelligence, and data analysis to obtain value from data.
Use cases include:
- Infrastructure and Operations
- Customer Acquisition
- Personalized Offers
- Customer Service and Retention
- Financial Markets
- Fraud and Compliance
- Customer 360
- IoT
- Predictive Maintenance
What skills are needed for a data scientist?
Below is a list of skills needed for data scientists:
- Statistics
- Programming Knowledge
- Data Manipulation and Analysis
- Data Visualization
- Machine Learning
- Deep Learning
- Software Engineering
- Model Deployment
H2O.ai and Data Science: H2O Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish key machine learning tasks in just minutes or hours, not months. By delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, bring your own recipe, time-series and automatic pipeline generation for model scoring, H2O Driverless AI provides companies with an extensible customizable data science platform that addresses the needs of a variety of use cases for every enterprise in every industry.
Data Science vs Other Technologies & Methodologies
Data science vs computer science
Data science is a specific field of knowledge within the study of computers, focusing on programming, analytics, and statistics. Computer science deals with building hardware and programming software.
Data science vs data analytics
Data science focuses on finding meaningful correlations between large datasets, while data analytics is designed to uncover the specifics of extracted insights.
Data science vs information science
Data science is the discovery of knowledge and actionable information in data. Information science is the design of practices for storing, retrieving, and interacting with information.
Data science vs data engineering
A data scientist analyzes data, answers questions, and provides metrics to solve business problems. A data engineer develops, tests, and maintains data pipelines and architectures, which the data scientist uses for analysis.