Python Developer Certificate
IDCPY

• Learn Python fundamentals, including variables, data types, functions, loops, and control flow, for building robust programs
• Work with complex data structures such as dictionaries and lists to efficiently organize and access data
• Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration
• Generate descriptive statistics and apply filtering, grouping, and pivoting techniques to gain deeper insights
• Visualize data using Matplotlib and create clear, customized charts, including bar graphs, histograms, and scatter plots
• Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows

Learn how to build AI-powered web applications using Flask and the OpenAI API. This course covers web development fundamentals, API integration, and AI-driven features for interactive applications.

• Set up Flask projects and create routes for handling web requests and rendering templates.
• Design and style web applications using HTML, CSS, and Flask’s templating system.
• Integrate the OpenAI API to implement AI-powered features like sentiment analysis.
• Handle user input with dynamic forms and process data for real-time interactions.
• Implement error handling and debugging techniques to ensure smooth application performance.
• Deploy and test Flask applications for real-world use and AI-enhanced functionality.

Transform raw data into interactive visual insights by building dashboards with Python’s top visualization tools. This course blends analysis, design, and deployment to help you showcase data professionally.

• Work with real-life datasets using Python’s core libraries, including NumPy for numerical computing and Pandas for data manipulation
• Create static and interactive visualizations using Matplotlib, Seaborn, and Plotly to clearly communicate trends and patterns
• Build multi-component dashboards using Dash Enterprise, incorporating callbacks, sliders, date pickers, and more
• Practice hands-on development by applying new skills to personalized projects with guided instructor support
• Publish your dashboards online using GitHub and Heroku to demonstrate your work to potential employers or clients
• Explore best practices for styling and structuring visual narratives that are clear, persuasive, and engaging

• Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data
• Understand the difference between regression and classification problems and when to apply each approach
• Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest
• Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error
• Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs
• Use Python's essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms
• Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon
• Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems