Intro to Data & AI - Course Curriculum

Hexadigitall Academy (Hexadigitall Technologies)

www.hexadigitall.com

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Course Snapshot

Discover the world of data analysis, machine learning, and artificial intelligence basics.

Welcome to Intro to Data & AI! 🎓

This curriculum is designed to take you from core understanding to confident delivery through weekly applied practice, measurable outcomes, and portfolio evidence.

Each week builds progressively with practical tasks, implementation checkpoints, and reflection points so you can convert knowledge into repeatable professional performance.

Your success is our priority. Stay consistent with weekly execution, document your work, and use feedback loops to continuously improve your delivery quality.

Prerequisites

Python programming proficiency: libraries (NumPy, Pandas, scikit-learn), data structures, and API usage
Statistics and probability fundamentals: distributions, hypothesis testing, and experimental design
Machine learning basics: supervised learning, hyperparameter tuning, and model evaluation metrics
Hands-on experience with notebooks (Jupyter), experiment tracking, and model versioning systems

Learning Outcomes

What is data and why it matters
Basic statistics and data analysis
Introduction to machine learning
AI concepts and real-world applications
Data visualization basics

Recommended Complementary Courses

Pair this curriculum with a related foundation or advanced specialization to strengthen adjacent skill areas.
Select one systems-focused and one delivery-focused course to improve both implementation depth and execution speed.
Use complementary study tracks to broaden portfolio evidence and improve interview and project readiness.

Essential Learning Resources

Model development workflow guides, hyperparameter tuning references, and experiment tracking templates
Feature engineering playbooks, model evaluation metrics library, and production deployment checklists
Research paper repository, implementation examples, and performance benchmarking tools

Your Learning Roadmap

Foundation

Weeks 1-3

Early Weeks: ML fundamentals, data preparation, and baseline models
Middle Weeks: Advanced model techniques, experimentation, and tuning
Late Weeks: Production deployment, monitoring, and continuous improvement

Build

Weeks 4-6

Charts, Dashboards, and Data Visualization Basics
Asking Better Questions with Data
Introduction to Machine Learning Concepts

Integration

Weeks 7-9

Supervised versus Unsupervised Learning
AI Use Cases, Limitations, and Ethics
Model Inputs, Outputs, and Evaluation Basics

Capstone

Weeks 10-12

Data Cleaning and Basic Feature Thinking
Telling Stories with Data and AI Insights
Capstone: Simple Data-to-Insight Project

Detailed Weekly Curriculum

Week 1 2 hours/week + labs

Data Types, Sources, and Data Quality Basics

Identify the principles of Data Types, Sources, and Data Quality Basics and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Data Types, Sources, and Data Quality Basics in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Data Types, Sources, and Data Quality Basics, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Data Types, Sources, and Data Quality Basics with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 2 2 hours/week + labs

Spreadsheets, Tables, and Simple Analysis Workflows

Identify the principles of Spreadsheets, Tables, and Simple Analysis Workflows and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Spreadsheets, Tables, and Simple Analysis Workflows in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Spreadsheets, Tables, and Simple Analysis Workflows, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Spreadsheets, Tables, and Simple Analysis Workflows with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 3 2 hours/week + labs

Descriptive Statistics and Summary Measures

Identify the principles of Descriptive Statistics and Summary Measures and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Descriptive Statistics and Summary Measures in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Descriptive Statistics and Summary Measures, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Descriptive Statistics and Summary Measures with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 4 2 hours/week + labs

Charts, Dashboards, and Data Visualization Basics

Identify the principles of Charts, Dashboards, and Data Visualization Basics and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Explain Charts, Dashboards, and Data Visualization Basics in a guided scenario using realistic tools, constraints, and quality gates.
Apply trade-offs, risks, and decision points for Charts, Dashboards, and Data Visualization Basics, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Charts, Dashboards, and Data Visualization Basics with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 5 2 hours/week + labs

Asking Better Questions with Data

Apply the principles of Asking Better Questions with Data and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Asking Better Questions with Data in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Asking Better Questions with Data, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Asking Better Questions with Data with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 6 2 hours/week + labs

Introduction to Machine Learning Concepts

Apply the principles of Introduction to Machine Learning Concepts and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Introduction to Machine Learning Concepts in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Introduction to Machine Learning Concepts, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Introduction to Machine Learning Concepts with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 7 2 hours/week + labs

Supervised versus Unsupervised Learning

Apply the principles of Supervised versus Unsupervised Learning and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze Supervised versus Unsupervised Learning in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for Supervised versus Unsupervised Learning, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for Supervised versus Unsupervised Learning with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 8 2 hours/week + labs

AI Use Cases, Limitations, and Ethics

Apply the principles of AI Use Cases, Limitations, and Ethics and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Analyze AI Use Cases, Limitations, and Ethics in a guided scenario using realistic tools, constraints, and quality gates.
Evaluate trade-offs, risks, and decision points for AI Use Cases, Limitations, and Ethics, then record rationale for stakeholder review.
Document a portfolio-ready analytics quality workbook for AI Use Cases, Limitations, and Ethics with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 9 2 hours/week + labs

Model Inputs, Outputs, and Evaluation Basics

Analyze the principles of Model Inputs, Outputs, and Evaluation Basics and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Model Inputs, Outputs, and Evaluation Basics in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Model Inputs, Outputs, and Evaluation Basics, then record rationale for stakeholder review.
Defend a portfolio-ready analytics quality workbook for Model Inputs, Outputs, and Evaluation Basics with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 10 2 hours/week + labs

Data Cleaning and Basic Feature Thinking

Analyze the principles of Data Cleaning and Basic Feature Thinking and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Data Cleaning and Basic Feature Thinking in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Data Cleaning and Basic Feature Thinking, then record rationale for stakeholder review.
Defend a portfolio-ready analytics quality workbook for Data Cleaning and Basic Feature Thinking with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 11 2 hours/week + labs

Telling Stories with Data and AI Insights

Analyze the principles of Telling Stories with Data and AI Insights and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Telling Stories with Data and AI Insights in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Telling Stories with Data and AI Insights, then record rationale for stakeholder review.
Defend a portfolio-ready analytics quality workbook for Telling Stories with Data and AI Insights with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Week 12 2 hours/week + labs

Capstone: Simple Data-to-Insight Project

Analyze the principles of Capstone: Simple Data-to-Insight Project and link them to course outcomes with scaffolded guidance and beginner-safe checkpoints.
Evaluate Capstone: Simple Data-to-Insight Project in a guided scenario using realistic tools, constraints, and quality gates.
Create trade-offs, risks, and decision points for Capstone: Simple Data-to-Insight Project, then record rationale for stakeholder review.
Defend a portfolio-ready analytics quality workbook for Capstone: Simple Data-to-Insight Project with measurable success criteria and next actions.
Track measurable progress using rubric scores, defect/risk trends, and evidence completeness each week.
Run a short retrospective focused on what to retain, improve, and scale into the following week.
Incorporate peer or mentor feedback and revise the week deliverable to professional publication quality.
Publish the week output into your cumulative portfolio with concise outcome narrative and proof artifacts.

Capstone Projects

Project 1: Data Quality and Dashboard Starter

Clean a noisy dataset, establish quality checks, and build a dashboard that communicates core trends and anomalies.

Data cleaning and validation workflow
Dashboard with decision-oriented visuals
Quality and assumption documentation

Project 2: Intro ML Evaluation Exercise

Build and compare simple supervised and unsupervised workflows, then evaluate practical usefulness and limitations.

Model comparison notebook or analysis sheet
Metric interpretation and error analysis
Ethical and operational risk notes

Project 3: Capstone: Data-to-Insight Case Delivery

Deliver a complete analysis case from problem framing through recommendations, with reproducible artifacts and stakeholder-ready storytelling.

Final dataset, analysis, and evaluation package
Executive summary with recommendations
Presentation-ready narrative and next steps

Study Tips

Dedicate two weekly blocks for model experimentation, hyperparameter variation, and ablation study execution.
Maintain an experiment log tracking dataset versions, feature changes, model architectures, and performance deltas between iterations.
Conduct weekly model performance reviews, monitoring inference accuracy drift and retraining signal detection against production data.

Study Tips for Success

Protect consistent weekly practice time and complete hands-on work before moving to the next topic.
Document implementation decisions, trade-offs, and lessons learned after each weekly deliverable.
Review feedback quickly and ship an improved revision within the same week to reinforce retention.
Track measurable progress with checklists, test evidence, and milestone outcomes.

About Intro to Data & AI

This curriculum is structured to build practical capability, consistent delivery discipline, and portfolio-ready outcomes in Intro to Data & AI. It combines conceptual understanding with applied execution so learners can perform confidently in real project environments.