Course Description

AI Application Development with Python is the second course in the Applied AI & Data Science Pathway—a rigorous, project-driven experience that advances students from foundational Python programming to building full AI-powered applications. Designed to prepare learners for the Python Institute’s PCAP certification, this course deepens students’ understanding of Python’s data structures, modular design, object-oriented principles, and application architecture, giving them the tools needed to create real, production-style AI programs.

Students explore how modern AI applications are built and deployed by learning to work with external files and datasets, design reusable modules, implement functions and classes, manipulate structured data, and interact with APIs and cloud-based AI services. They apply these skills by creating intelligent applications such as conversational agents, data-driven decision tools, text-processing utilities, and AI-integrated apps that leverage image, speech, or language models.

Throughout the course, learners follow an industry-standard development workflow—planning, prototyping, testing, debugging, refining, and documenting—building the habits required for real-world software engineering. By the end of the course, students can architect multi-file Python applications, integrate AI services, process and transform data, design modular systems, and clearly explain how their AI-powered applications work internally. These experiences prepare them for Course 3: AI Data Science & Automation, where they will apply Python to real-world data, analytics, and automation workflows.

Language

Industry Certification

Coding Concepts

• Modular program design
• Multi-file program structure
• Imports and custom modules
• Code organization and maintainability
• Advanced function parameters
• Return values and function scope
• Lists, dictionaries, sets, and tuples
• Nested data structures
• File handling (read/write)
• Working with structured data (CSV, JSON)
• Data validation and transformation
• Classes, objects, attributes, and methods
• Introductory object-oriented programming

• Encapsulation and code reuse
• Exception handling (try/except)
• Debugging strategies (stack traces, data inspection)
• Testing and validation workflows
• API fundamentals
• Connecting to external AI services
• Parsing and consuming API responses
• Command-line (CLI) applications
• Graphical user interface (GUI) basics
• Logging techniques
• Documentation practices
• Development workflow (plan → build → test → iterate → deploy)

Outcomes

• Write modular, multi-file Python programs.
• Organize code using imports and custom modules.
• Use advanced function parameters, return values, and scope effectively.
• Apply lists, dictionaries, sets, tuples, and nested data structures to manage information.
• Read, write, and manipulate structured data from files (CSV, JSON).
• Validate, clean, and transform data for use in applications.
• Design and use classes, objects, attributes, and methods in introductory OOP.
• Apply encapsulation and code reuse to structure complex programs.
• Implement exception handling to manage runtime errors gracefully.
• Debug programs using stack traces and systematic inspection techniques.
• Test and validate multi-file applications for correctness and reliability.
• Connect Python programs to external APIs and AI services.
• Consume and parse API responses to power AI-driven functionality.
• Build command-line applications and basic GUI programs.
• Log program behavior and document code clearly and professionally.
• Follow a structured development process (plan → build → test → iterate → deploy).
• Create AI-powered applications that integrate real-world data and external services.
• Demonstrate competencies required for the PCAP certification.

Course Description

AI Data Science & Automation is the third course in the Applied AI & Data Science Pathway—a rigorous, project-driven experience that teaches students how to use Python and foundational SQL to analyze real-world data, generate insights, and automate complex workflows. Designed to prepare learners for the Python Institute’s PCED certification, this course develops mastery in data processing, exploratory data analysis, visualization, automation scripting, relational data concepts, and computational statistics—the essential skills that power modern data science and AI systems.

Students work with industry-standard libraries such as pandas, NumPy, and matplotlib, while also learning how data is stored, retrieved, and combined using SQL. They practice querying relational tables, writing basic SQL commands, filtering and aggregating data, and performing simple joins to prepare meaningful datasets for analysis. Throughout the course, students build automated dashboards, insight-generating analytics tools, SQL-powered ETL workflows, and data-processing scripts that clean, transform, and organize large datasets.

By the end of the course, students can analyze and interpret complex datasets, work confidently with relational data sources, automate repetitive workflows, visualize trends, apply statistical reasoning, and explain how structured data pipelines support AI decision-making—fully preparing them for Course 4: Applied Machine Learning & AI Systems, where they will train and evaluate real machine learning models.

Language

Industry Certification

Coding Concepts

• Pandas DataFrames
• Pandas Series
• NumPy arrays
• Vectorized operations with NumPy
• Loading CSV files
• Loading JSON files
• Importing data from SQL tables
• Writing basic SQL queries (SELECT, WHERE, ORDER BY)
• Filtering SQL results
• Grouping and aggregations in SQL (SUM, COUNT, AVG, MIN, MAX)
• Simple SQL joins (INNER JOIN)
• Cleaning datasets
• Handling missing values
• Removing duplicates and outliers
• Filtering and sorting data in pandas
• Grouping and aggregating data in pandas
• Merging and joining DataFrames
• Data slicing and indexing

• Descriptive statistics (mean, median, mode, std dev)
• Correlation calculations
• Identifying trends and patterns
• Matplotlib line charts
• Matplotlib bar charts
• Matplotlib scatter plots
• Matplotlib histograms
• Chart styling and labeling
• Building multi-chart dashboards
• Writing automation scripts
• ETL-style workflows (extract, transform, load)
• Generating automated reports
• Functions for data processing
• Exception handling
• Parsing API responses
• File handling and data persistence
• Debugging data pipelines
• Documentation and commenting for data workflows

Outcomes

• Use Python libraries such as pandas, NumPy, and matplotlib to analyze and visualize data.
• Load, clean, organize, and transform real-world datasets for analysis.
• Write basic SQL queries to retrieve, filter, and sort data from relational tables.
• Perform SQL aggregations and grouping operations to summarize data.
• Join relational tables using simple SQL joins to prepare combined datasets.
• Import SQL query results into Python and integrate them into pandas workflows.
• Compute descriptive statistics and interpret their significance.
• Build clear and effective charts, graphs, and dashboards to communicate insights.
• Identify trends, patterns, correlations, and anomalies in datasets.
• Automate repetitive data-processing tasks using Python scripts.
• Create ETL-style workflows to extract, transform, and load structured data.
• Retrieve and process data from external APIs and online sources.
• Debug and validate data pipelines to ensure accuracy and reliability.
• Explain how structured data supports analytics and AI decision-making.
• Document data workflows and present insights clearly and professionally.
• Build industry-inspired data science and automation projects.
• Demonstrate competencies aligned with the PCED certification.

Course Description

Applied Machine Learning & AI Systems is the final capstone course in the Applied AI & Data Science Pathway—a rigorous, project-driven experience that teaches students how to use Python, SQL, and core data-analysis techniques to build, train, evaluate, and interpret machine learning models. Designed to prepare learners for the Python Institute’s PCAD certification, this course deepens mastery in data acquisition, relational databases, feature engineering, statistical reasoning, model evaluation, and responsible AI practices—the essential domains that define modern data analysis and machine learning workflows.

Students use industry-standard tools such as pandas, NumPy, scikit-learn, and matplotlib, while learning how to retrieve, clean, join, and transform structured datasets from SQL databases to prepare them for modeling. Projects challenge students to design end-to-end data and machine learning pipelines that reflect how organizations build predictive analytics systems today.

By the end of the course, students can acquire data from multiple sources, prepare and analyze structured datasets, train and evaluate ML models, interpret their predictions, apply statistical reasoning, communicate results to technical and non-technical audiences, and explain how data-driven AI systems support decision-making—demonstrating the full set of competencies required for the PCAD certification.

Language

Industry Certification

Coding Concepts

• Loading training data from CSV files
• Loading training data from JSON files
• Importing training data from SQL tables
• Writing SQL queries for model preparation
• Cleaning machine learning datasets
• Handling missing values
• Handling outliers
• Feature engineering
• One-hot encoding
• Scaling and normalization
• Train/test dataset splits
• Cross-validation techniques
• Descriptive statistical analysis
• Correlation analysis
• Scikit-learn classification models
• Scikit-learn regression models
• K-means clustering

• Distance metrics
• Regression evaluation metrics (MAE, MSE, RMSE, R²)
• Classification evaluation metrics (accuracy, precision, recall, F1)
• Confusion matrices
• Model prediction and inference
• Hyperparameter tuning
• Saving trained models
• Loading trained models
• Basic neural networks (TensorFlow/Keras)
• Activation functions
• Loss functions
• Vectorized computation
• Model visualization
• Model interpretation
• Responsible AI principles
• Debugging machine learning pipelines
• Documenting machine learning workflows

Outcomes

• Acquire training data from CSV, JSON, SQL, and external sources.
• Clean, organize, filter, and transform datasets for machine learning workflows.
• Write SQL queries to retrieve, join, and aggregate relational data for modeling.
• Engineer meaningful features using encoding, scaling, and transformation techniques.
• Split datasets into training and testing sets and apply cross-validation methods.
• Train supervised learning models for classification and regression tasks.
• Train unsupervised learning models such as clustering algorithms.
• Compute and interpret statistical metrics and model evaluation metrics.
• Evaluate model performance using accuracy, precision, recall, F1, and regression metrics.
• Interpret model predictions and explain how machine learning models generate outputs.
• Tune model hyperparameters to improve predictive performance.
• Build reproducible machine learning pipelines from data acquisition to evaluation.
• Visualize model behavior and communicate insights clearly to technical and non-technical audiences.
• Apply responsible AI principles related to fairness, bias, and model transparency.
• Debug and validate machine learning workflows for correctness and reliability.
• Document machine learning processes and present results professionally.
• Build industry-inspired machine learning applications using real datasets.
• Demonstrate competencies aligned with the PCAD certification.