AI_In_Big_data_and_Datascience_Updated (2).pptx

FDP on "AI for Future" @ KG Reddy College of Engineering, Hy
derabad
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AI in Data Science and Big Data
•Welcome to the Faculty Development Program on AI in Data Science and
Big Data. This session will explore the powerful intersection of these
fields, showcasing how AI is transforming data analysis and insights.

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12/17/2024 FDP on "AI for Future" @ KG Reddy College of Engineering, Hyderabad 6

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AI's Role in
Data
Science
and Big
Data
Automation
AI can automate repetitive tasks,
such as data cleaning, preprocessing,
and model training, allowing data
scientists to focus on more complex
tasks.
Enhanced Analysis
AI algorithms can process large volumes of data,
identify patterns, and generate insights that are not
easily discernible through traditional methods.

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AI
Technique
s
Supervised Learning
Models trained on
labeled data, predicting
outcomes based on
learned relationships.
Unsupervised Learning
Models discover patterns
and structures in unlabeled
data, clustering data into
groups or identifying
anomalies.
Deep Learning
Utilizes artificial neural networks, inspired by the human
brain, to learn complex patterns from large datasets.

FDP on "AI for Future" @ KG Reddy College of Engineering, Hy
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Tools for Big Data Analytics
Apache Spark
A fast and general-purpose
distributed computing engine
for real-time and batch data
processing.
Apache Hadoop
A framework for distributed
storage and processing of large
datasets, providing scalability
and fault tolerance.
Python
A versatile programming
language widely used in data
science, offering extensive
libraries for data analysis,
visualization, and machine
learning.
R
A statistical programming
language, powerful for
statistical analysis, data
visualization, and machine
learning.

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Applications of AI in Industries
1
Healthcare
AI assists in medical diagnosis, drug discovery, and personalized treatment
plans.
2
Finance
AI detects fraud, manages risk, and automates financial
processes.
3
Retail
AI personalizes shopping experiences, optimizes inventory management, and improves customer
service.
4
Manufacturing
AI enhances production efficiency, predicts equipment failure, and streamlines supply chain
operations.
5
Transportation
AI powers autonomous vehicles, optimizes traffic flow, and improves transportation
safety.

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Challenges in AI and Big Data
1
Data Quality
Inaccurate or incomplete data can lead to biased or unreliable AI
models.
2
Data Privacy
Protecting sensitive data is crucial, and AI systems must be designed with strong
privacy measures.
3
Explainability
Understanding how AI models arrive at their decisions is vital,
especially in critical applications.
4
Ethical Considerations
AI systems should be developed and deployed
responsibly, considering potential biases and
societal impacts.

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Key Takeaways
•AI and Big Data are revolutionizing data
analysis and insights, offering powerful
solutions for a wide range of industries.
Understanding the foundations,
techniques, and applications of AI in this
context is essential for navigating the
future of data-driven decision-making.
•Thank you for your participation in this
Faculty Development Program!

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AI_In_Big_data_and_Datascience_Updated (2).pptx

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