Introduction to Data Science, AI and Machine Learning #DataScience #ai #ml #InfoTechVMD@Infotechvmd [Video]

Objective: Provide students with a clear understanding of what data science, artificial intelligence (AI), and machinObjective: Provide students with a clear understanding of what data science, artificial intelligence (AI), and machine learning (ML) entail. This includes explaining the roles, skills, and tools within the data science field, as well as introducing real-world applications and career paths.

Topics Covered
Introduction to Data Science

What is Data Science?
Data Science combines domain knowledge, programming skills, and statistical analysis to extract meaningful insights from data.
Involves gathering, cleaning, analyzing, and visualizing data to support data-driven decision-making.
Key Areas in Data Science:
Data Analysis: Interpreting and visualizing data to find patterns and insights.
Data Engineering: Building and maintaining data pipelines and storage.
Machine Learning: Building predictive models using data.
Big Data Analytics: Working with large-scale data using tools like Hadoop and Spark.
What is Artificial Intelligence (AI)?

AI is the branch of computer science focused on creating systems capable of mimicking human intelligence.
It encompasses various techniques and methods to enable machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing images, and making decisions.
Subfields of AI:
Natural Language Processing (NLP): Working with human language to enable machines to understand, interpret, and respond.
Computer Vision: Enabling machines to interpret and process visual data like images and videos.
Robotics: Developing machines that can interact with the physical world.
Introduction to Machine Learning (ML)

What is Machine Learning?
Machine Learning is a subset of AI focused on developing algorithms that allow systems to learn patterns and make decisions based on data.
Key approaches include supervised learning, unsupervised learning, and reinforcement learning.
Types of Machine Learning:
Supervised Learning: Models are trained on labeled data (e.g., predicting house prices based on past sales).
Unsupervised Learning: Models identify patterns in unlabeled data (e.g., customer segmentation).
Reinforcement Learning: Models learn through trial and error (e.g., training a robot to navigate a space).
Key Roles in Data Science and AI/ML

Data Scientist: Analyzes and interprets complex data to aid decision-making, creates machine learning models.
Data Engineer: Focuses on the design and maintenance of data pipelines, ensuring data quality and availability.
ML Engineer: Specializes in creating and deploying ML models in production environments.
Data Analyst: Extracts insights from data, prepares reports, and creates data visualizations for business decision-making.
Business Intelligence (BI) Analyst: Translates business requirements into data insights, often through data visualization.
Skills and Tools for Data Science and AI/ML

Programming Skills: Python, R, SQL for data manipulation and analysis.
Mathematics and Statistics: Basic understanding of probability, linear algebra, and calculus.
Machine Learning Frameworks: Scikit-learn, TensorFlow, and Keras for building models.
Data Visualization Tools: Matplotlib, Seaborn, Tableau, Power BI.
Big Data Tools: Hadoop, Spark for managing and processing large data sets.
Applications of Data Science, AI, and ML in Industry

Healthcare: Predicting patient outcomes, disease diagnosis, and personalized treatments.
Finance: Fraud detection, credit scoring, stock price prediction, and risk management.
Retail: Customer segmentation, recommendation systems, and inventory management.
Marketing: Targeted advertising, sentiment analysis, and customer relationship management.
Automotive: Autonomous driving systems, predictive maintenance, and traffic pattern analysis.
Activities
Case Study Review

Choose a Case Study: Select a well-known case study in Data Science, AI, or ML. For example:
Netflix Recommendation System: Discuss how data science is used to personalize content for users based on viewing history.
Healthcare Predictive Models: Examine how ML models are used to predict patient readmissions.

Topic: “How can data science impact your field of interest?”
Instructions: Students individually or in groups reflect on how data science or AI/ML can be applied to a field they are passionate about (e.g., sports, education, environmental science).
Outcome: This helps students connect their personal interests with data science, making the learning journey more meaningful.
Career Path Exploration Exercise

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