The relationship between data science and artificial intelligence, their complementary functions, and their potential coexistence and evolution in the rapidly changing technological landscape.

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DEFINING DATA SCIENCE AND AI
INTERSECTION OF DATA SCIENCE
AND AI
DATA SCIENE AS THE BACKBONE
OF AI
DATA SCIENCE FOR CONTINUOUS
IMPROVEMENT
ETHICAL CONSIDERATION AND
INTERPRETATION
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3. Data science is an interdisciplinary field that uses
statistical and mathematical tools, data analysis, machine
learning, and data visualization to extract insights from
structured and unstructured data.
DATA SCIENCE:
ARTIFICIAL
INTELLIGANCE:
AI aims to create machines replicating human intelligence,
including natural language processing, computer vision,
and decision-making, with machine learning incorporating
algorithms for data-driven performance enhancement.

4. The intersection of data science and AI is where the two
sciences meet. Data science provides the data and insights
needed for machine learning algorithms to learn and
predict, laying the framework for AI. AI relies on data
science to work properly.
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5. Data scientists are crucial in the development of AI,
ensuring the accuracy and performance of machine learning
models by obtaining, cleaning, and preparing large
datasets. They also play a vital role in feature engineering,
identifying and altering relevant data properties to improve
model performance. Analyzing data helps identify patterns,
outliers, and potential biases for ethical and fair AI
systems.
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6. Data science and AI are interconnected in a feedback loop,
with AI models generating more data for retraining and
improvement, while data scientists evaluate performance,
identify areas for improvement, and develop strategies for
AI algorithm enhancement.
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7. The human element is crucial in interpreting AI results,
ensuring ethical use, and bringing a human touch to
decision-making processes, as complex tasks and
processes often require human intervention and oversight,
despite the automation of tasks.
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8. In the future, AI and other fields will most likely coexist and
collaborate. The basis of AI is built on data and insights
from various domains, which play an important role in data
collection, preparation, analysis, and ethical concerns,
increasing AI's responsibility and trustworthiness.
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