AI Literacy Program
EduTechX’s AI Literacy Program empowers schools with a complete, research-aligned pathway to safely and effectively integrate artificial intelligence
into teaching and learning. Through structured courses for teachers, students, and parents, we build the skills, confidence,
and ethical foundations needed to thrive in an AI-driven world.
COURSE 1 — Cracking Data with Python (Beginner Level)
Foundational course. No prerequisites.
Skills Learned:
Python basics
Jupyter Notebooks
Pandas, NumPy foundations
Basic visualizations
Intro to analysis workflow
COURSE 2 — Data Analytics Fundamentals
Prerequisite: Course 1
Skills:
Data cleaning & transformation
Excel + SQL basics
Pandas intermediate
Exploratory Data Analysis
Data storytelling fundamentals
Tools:Python, SQL, Excel, Seaborn
COURSE 3 — SQL for Data Analysts (Hands-On)
Standalone or as follow-up to Course 2
Skills:
Writing SQL queries
Joins, aggregations, subqueries,Window functions
SQL case studies (sales, finance, ecommerce)
COURSE 4 — Data Visualization & Storytelling
Prerequisite: Course 2
Skills:
Creating dashboards
Matplotlib, Seaborn
Power BI or Tableau introduction
Visualization best practices
Communicating insights
COURSE 5 — Statistics for Data Science (Practical)
Prerequisite: Course 2
Skills:
Probability
Distributions
Hypothesis testing
t-test, chi-square, ANOVA,Statistical intuition for ML
Tools:
Python, SciPy, Statsmodels
COURSE 6 — Machine Learning with Python (Beginner → Intermediate)
Prerequisite: Courses 1, 2, 5
Skills:
Train-test split
Linear & Logistic Regression
Decision Trees
KNN, Naive Bayes
Model evaluation metrics
Tools:
scikit-learn
COURSE 7 — Deep Learning Foundations
Prerequisite: Course 6 or 7
Skills:
Neural networks
Activation functions
Loss functions
Optimization
Computer vision basics
NLP basics
Tools:
TensorFlow or PyTorch
COURSE 8 — Applied Artificial Intelligence (Practical AI)
Prerequisite: Course 8
Skills:
AI applications in business
LLMs and prompt engineering
Using AI APIs (OpenAI, Gemini, Claude)
AI automation
Building AI-powered apps
Tools:
Python, APIs, Streamlit
COURSE 9 — End-to-End Data Science & AI Real-World Projects (Capstone)
Prerequisite: Course 6 or higher
Skills:
Data collection
Cleaning & preparation
Machine learning pipeline
Model deployment
Presentation of findings
GitHub portfolio creation
Deliverables:
✔ 3 full projects (education, business, health, finance)
✔ GitHub-ready notebooks
✔ Resume-ready portfolio
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