GDG_sece_dataScience_introduction about Data science and Road map to Data science
- 1. Sri Eshwar college of Engineering - Coimbatore Unveiling Insights : AJourneyThroughDataScience GDG ON CAMPUS Google Develpers Group Data Science Google Developer Groups On Campus
- 4. What is Data science Data science is the study of collecting, analyzing, and interpreting data to gain insights, solve problems, or make decisions. It combines math, statistics, and technology to uncover patterns and trends in various fields. Why Data Science It helps us make better decisions, uncover patterns, predict outcomes, and solve real-world problems across various fields. It uses tools like math, statistics, and coding to organize data, find patterns, and create solutions or predictions. How Does Data Science Work?
- 6. Data Science NON Technical Technical Maths & Stats Analytical Mindset Communication Business Understant ML & DL Computer Vision Data Analysis NLP
- 7. Linear Algebra Calculus Probability Descriptive Statistics Inferential Statistics Mathematics and Statistics to Learn for Data Science Linear Algebra: Used in building machine learning models like recommendation systems. Probability: Applied in risk analysis or predicting customer behavior.
- 8. What is Machine Learning? Machine learning (ML) is a subset of artificial intelligence where computers learn from data and improve their performance over time without being explicitly programmed. Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Email spam detection (classifies emails as spam or not). Market segmentation for targeted marketing Self-driving cars (learning optimal driving strategies)
- 9. Deep learning is a subset of machine learning, which itself is a subset of artificial intelligence (AI). It uses artificial neural networks (ANNs) designed to mimic the way humans learn. These networks are composed of layers of interconnected nodes (neurons) that process data hierarchically. Deep learning excels in tasks involving large datasets and complex patterns, such as image recognition, natural language processing, and autonomous systems. What is Deep Learning?
- 10. Convolutional Neural Networks (CNNs) Purpose: Primarily used for image and video processing. Example Use Cases: Image classification, object detection, facial recognition. Recurrent Neural Networks (RNNs) Purpose: Suited for sequential data and time-series analysis. Example Use Cases: Speech recognition, language modeling, and stock market prediction. Generative Adversarial Networks (GANs) Purpose: Consists of two networks (generator and discriminator) that compete to improve data generation. Example Use Cases: Creating realistic images, video game character modeling, and deepfake generation.
- 11. The network processes each word and generates
- 12. Image Classification What is Computer Vision? Computer Vision is a field of artificial intelligence (AI) and computer science focused on enabling machines to interpret and process visual data, such as images and videos, similar to how humans do. It involves techniques for acquiring, analyzing, and understanding digital images to make actionable decisions. Types of Computer Vision Identifies the class or category of an image (e.g., "cat" or "dog").
- 13. Object Detection Image Segmentation Facial Recognition Optical Character Recognition (OCR) Identifies and locates objects within an image or video (e.g., detecting pedestrians in traffic). Divides an image into meaningful parts or segments (e.g., separating background from foreground). Identifies or verifies individuals using facial features. Converts printed or handwritten text in images into machine-readable text.
- 14. Text Analysis Named Entity Recognition (NER) Speech Recognition Language Translation Language Generation Text Summarization Sentiment Analysis Text-to-Speech (TTS) Question Answering What is NLP? Natural Language Processing (NLP) is a field of artificial intelligence (AI) focused on enabling machines to understand, interpret, generate, and respond to human language in a way that is both meaningful and useful. NLP combines computational linguistics, machine learning, and deep learning to process and analyze large volumes of textual or spoken data. Types of NLP Tasks
- 15. What is Data Analytics? Data Analytics is the process of analyzing raw data to identify patterns, trends, and insights that can help in decision-making. It combines statistical methods, data modeling, and technology to extract meaningful insights from structured and unstructured data. Types of Data Analytics Descriptive Analytics Answers "What happened?" by summarizing historical data using dashboards and reports. Diagnostic Analytics Answers "Why did it happen?" by identifying causes and correlations using tools like regression analysis.
- 16. Predictive Analytics Answers "What is likely to happen?" using statistical models, machine learning, and forecasting techniques. Prescriptive Analytics Answers "What should be done?" by recommending actions or strategies based on predictive outcomes. Tools
- 17. 20 September Follwing domain: Sri Eshwar College of Engineering Google Developers Groups On Campus gdg-on-campus-sece @gdg_sece