Using_python_webdevolopment_datascience.pdf

Using_python_webdevolopment_datascience.pdf

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  preencoded.png Python: Your Gatewayto Data Science & Web Development Python is a versatile, high-level programming language. It's interpreted and dynamically typed, perfect for beginners. This presentation focuses on project-based learning with Python. Dr.Sudipta Bhattacharya
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  preencoded.png Python Fundamentals: Essential BuildingBlocks Data Types Integers, floats, strings, booleans, lists, dictionaries Control Flow Using if, else, elif and loops like for and while Functions Defining/calling functions, using arguments and return values Example Simple "Hello, World!" script and data manipulation basics
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  preencoded.png Data Science Project1: Exploratory Data Analysis (EDA) Pandas & NumPy Libraries to manipulate and analyze data efficiently Project Tasks • Clean and preprocess Titanic dataset • Create visualizations with Matplotlib and Seaborn • Compute summary statistics to find patterns
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  preencoded.png Data Science Project2: Machine Learning Model Scikit-learn Library Powerful Python tool for machine learning Project Steps • Select features from Iris dataset • Train classification model • Evaluate model's accuracy and performance Goal Predict categories based on learned data patterns
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  preencoded.png Web Development Project1: Simple Web App with Flask Flask Framework Lightweight micro web framework in Python Project Highlights Create a functional To-Do List app with routes and templates Key Skills Routing, handling user input, rendering HTML templates
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  preencoded.png Web Development Project2: Interactive Dashboard with Dash Dash Framework Build analytical web apps with Python Project Tasks • Add visualization components • Implement callbacks for interactivity Goal Create a dynamic, user-friendly data explorer
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  preencoded.png Key Python Librariesfor Projects Data Science • Pandas, NumPy • Scikit-learn • Matplotlib, Seaborn Web Development • Flask, Django • Dash, Requests Others • Beautiful Soup (Scraping) • TensorFlow, PyTorch (Deep Learning)
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  preencoded.png Next Steps &Resources Recap Projects Data analysis, machine learning, and web apps with Python Practice Experiment and build your own projects Explore Resources Documentation, tutorials, and GitHub repos Start Your Portfolio Showcase your Python skills and projects
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  preencoded.png Career Paths withPython • Data Scientist: Analyze complex data sets to derive actionable insights that support business decisions. Use Python libraries such as Pandas, NumPy, and Scikit-learn to clean, process, and model data. Collaborate with stakeholders to identify data-driven opportunities and communicate results effectively. • Machine Learning Engineer: Design and implement machine learning models to automate predictions and decision- making processes. Work with Python frameworks like TensorFlow or PyTorch to build, train, and deploy models. Optimize model performance and integrate solutions into production systems. • Web Developer: Create dynamic and responsive websites and web applications using Python frameworks such as Django and Flask. Handle both frontend integration and backend server-side logic. Manage databases and APIs to deliver seamless user experiences. • Data Analyst: Extract, clean, and visualize data to provide meaningful business intelligence. Utilize Python tools like Matplotlib, Seaborn, and SQLAlchemy to generate reports and dashboards. Support decision-making by interpreting trends and patterns within the data. • Software Engineer: Develop scalable and maintainable applications and backend systems using Python and other relevant technologies. Write efficient code, perform testing, and troubleshoot issues as part of the software development lifecycle. Collaborate within development teams to deliver high-quality software products.
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  preencoded.png Web-Based Projects UsingPython • Flask Web Apps: Build lightweight, scalable web applications with Python, designed for flexibility and rapid development. Flask's micro-framework architecture allows you to easily customize and extend functionality to meet various project needs, from simple websites to complex RESTful APIs. • Dash Dashboards: Create dynamic data visualizations and interactive interfaces that are ideal for displaying real-time analytics and insights. Dash enables seamless integration of graphs, charts, and user input elements, making it a powerful tool for building analytical web applications without extensive frontend development. • User Interaction: Handle user inputs efficiently through forms, callbacks, and APIs to enhance the user experience. Python's robust support for processing HTTP requests and event-driven programming allows developers to build responsive and interactive web features. • Integration: Connect to databases, APIs, and external services seamlessly for a comprehensive web solution. Python's extensive ecosystem offers libraries like SQLAlchemy for database management and Requests for API consumption, enabling smooth communication and data exchange between components.
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  preencoded.png Data Science Project2: Machine Learning Model Feature Selection Identify key variables from the dataset to improve model accuracy. Model Training Use algorithms like decision trees or logistic regression to train. Evaluation Assess model performance with metrics such as accuracy and recall. Prediction Deploy the model to predict categories on new, unseen data.