Do you know about the difference between Data Science and Artificial Intelligence(AI)? Shrikant Mishra 05-Apr-2021 45 1 Answers Do you know about the difference between Data Science and Artificial Intelligence? Last updated:4/5/2021 3:07:01 AM
Rahul Roi 06-Apr-2021 Difference between Data Science and Artificial Intelligence Factors Data Science Artificial Intelligence Scope Involves various underlying data operations Limited to the implementation of ML algorithms Type of Data Structured and Unstructured Standardized in the form of embeddings and vectors Techniques Data Science will involve many different methods of statistics. Artificial Intelligence will use algorithms in computers to solve the problem Tools R, Python, SAS, SPSS, TensorFlow, Keras, Scikit- Learn Scikit-Learn, Kaffe, PyTorch, TensorFlow, Shogun, Mahout Applications Advertising, Marketing, Internet Search Engines Manufacturing, Automation, Robotics, Transport, Healthcare The Hierarchy of Needs in Data Science Where we know that Artificial Intelligence is a part of Data Science, Now we will discuss the six different hierarchy of needs in Data Science: First Step: Artificial Intelligence and Deep Learning Second Step: The A/B Testing, Experimentation, and Simple ML Algorithms Third Step: The Analytics, Metrics, Segments, Aggregates, Features, and Training Data Fourth Step: The Cleaning, Anomaly Detection, and Prep Fifth Step: The Reliable Data Flow, Infrastructure, Data Pipelines, ETL, Structured and Unstructured Data Storage Sixth Step: The Instrumentation, Logging, Sensors, External Data, and User Generated Content
Rahul Roi
Difference between Data Science and Artificial Intelligence
Factors
The Hierarchy of Needs in Data Science
Where we know that Artificial Intelligence is a part of Data Science, Now we will discuss the six different hierarchy of needs in Data Science:
First Step: Artificial Intelligence and Deep Learning
Second Step: The A/B Testing, Experimentation, and Simple ML Algorithms
Third Step: The Analytics, Metrics, Segments, Aggregates, Features, and Training Data
Fourth Step: The Cleaning, Anomaly Detection, and Prep
Fifth Step: The Reliable Data Flow, Infrastructure, Data Pipelines, ETL, Structured and Unstructured Data Storage
Sixth Step: The Instrumentation, Logging, Sensors, External Data, and User Generated Content