Keshav Vhanamane Seminar.pptx topic of AI

Ai realted topic

1. Genba Sopanrao Moze College Of Engineering Balewadi,Pune . Department of MCA Seminar on Data Science MCA II Year – IV SEM Project Guide Prof. DHANASHREE KOLAPE Presented By Keshav Yashawant Vhanamane MASTER OF COMPUTER APPLICATION 2024-2025

2. Data Science: Bridging Technology and Insight Data science is a field that uses scientific methods, algorithms, and programming to extract knowledge and insights from data, encompassing the process of collecting, analyzing, and interpreting large amounts of data to uncover hidden patterns and trends Kv

3. The Data Science Ecosystem Programming Languages Python R SQL Key Tools Pandas NumPy Scikit-learn Cloud Platforms AWS Google Cloud Azure

4. Data Acquisition and Preprocessing 1 Sources APIs, databases, web scraping. 2 Cleaning Handling missing values and outliers. 3 Feature Engineering Strategies for optimized data quality. Data quality is essential, often comprising 80% of data science work.

5. Exploratory Data Analysis (EDA) Visualization Techniques for clear data representation. Statistical Summaries Providing data overviews. Correlation Analysis Investigating relationships. Tools like Matplotlib, Seaborn, and Plotly are used to derive initial insights from raw data.

6. Machine Learning Fundamentals Supervised Learning Classification, regression. Unsupervised Learning Clustering. Model Evaluation Performance metrics: accuracy, precision, recall. Cross-validation techniques are vital for model selection.

7. Deep Learning and Neural Networks 1 ANN Architecture Artificial neural network architecture. 2 CNN and RNN Convolutional and recurrent neural networks. 3 Transfer Learning Leveraging pre-trained models. Real-world applications are found in image and text recognition.

8. Big Data Technologies Distributed Computing Platforms for processing large datasets. Apache Spark and Hadoop Scalable data processing tools. Cloud-Based Solutions Leveraging cloud infrastructure. Real-time data streaming technologies are also essential.

9. Ethical AI and Responsible Data Science 1 Fairness Equitable data representation. 2 Transparency Algorithmic decision-making clarity. 3 Privacy Protecting sensitive data. Addressing bias detection and ethical guidelines in data science is crucial.

10. Career Paths and Opportunities 1 Industries 2 Essential Skills 3 Networking Salary ranges from $95,000 to $165,000. Networking and professional development are key.

11. Future of Data Science AI Emerging trends. Quantum Computing advancements. Interdisciplinary Wider applications. Continuous learning is essential for aspiring data scientists.

12. Thank you for attending the seminar and for your valuable participation and engagement. We hope you found the topics informative and insightful.  Keshav Yashawant Vhanamane