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Last updated

27 July 2026

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Data Handling with NumPy and Pandas – Complete AI & Machine Learning Teacher Resource Pack is a classroom-ready resource from Fatih ARICA AI Learning, designed to help students understand how data must be inspected, structured, cleaned and checked before it can be used in AI or machine learning.

This unit introduces the essential foundations of practical data work. Instead of rushing directly from dataset to model, students learn why poor data quality can damage AI results and how NumPy and Pandas support safer, clearer and more reliable data preparation.

The core message of the unit is:

Most AI projects fail in the data before they fail in the model.

Across four structured lessons, students explore common data quality problems, NumPy arrays, vectorised operations, Pandas DataFrames, DataFrame inspection, missing values and clean data pipelines.

Lesson 1 – Why Data Handling Comes Before Modelling
Students investigate missing values, duplicates, inconsistent labels, outliers and biased data.

Lesson 2 – NumPy Arrays and Vectorised Operations
Students explore array-style thinking, repeated numerical operations, comparisons and shape.

Lesson 3 – Pandas DataFrames and Data Inspection
Students learn how DataFrames organise tabular data into rows and columns, and how inspection can reveal missing values, inconsistent categories, data type issues and possible outliers.

Lesson 4 – Missing Data and Clean Data Pipelines
Students learn that cleaning data is not simply deleting messy rows. They practise making careful, checked and documented cleaning decisions.

This pack includes:

Course Promo Page
Teacher Guide
Teacher Handbook
Student Summary Notes
Student Worksheet
Answer Key
Printable Activity Cards
Exit Tickets
Classroom Slides
Editable DOCX files
Read Me First guide
TES cover image

Students will learn to explain why data handling comes before modelling, identify common data quality problems, describe NumPy arrays, explain vectorised operations, inspect Pandas DataFrames, describe missing data issues and build a simple clean data pipeline.

This resource is suitable for introductory AI lessons, machine learning foundations, Python data science starters, secondary computing, digital technology and beginner data literacy lessons.

It can be taught with live coding, teacher demonstration, printed datasets or no-code classroom discussion.

Suggested duration: 4 × 40-minute lessons.

Part of the Fatih ARICA AI Learning classroom resource series. A related AI & Machine Learning book series will also be available separately.

Get this resource as part of a bundle and save up to 33%

A bundle is a package of resources grouped together to teach a particular topic, or a series of lessons, in one place.

Bundle

AI & Machine Learning Complete No-Prep Bundle | Volume 1: Foundations & Data

Teach Artificial Intelligence, Python, data science and introductory Machine Learning with one complete, ready-to-use bundle. This collection brings together the full set of AI & Machine Learning No-Prep Lessons created for Volume 1: Foundations & Data, alongside the complete 192-page course book. The resources follow a clear learning journey from the foundations of Artificial Intelligence to data handling, visualisation and the development of students’ first Machine Learning models. Each lesson is designed to reduce preparation time while providing structured explanations, practical activities and classroom-ready materials. The bundle covers all seven units: • Introduction to Artificial Intelligence • AI history, branches and real-world applications • Artificial Intelligence in Europe and the EU AI Act • Python, Anaconda and development environments • Jupyter Notebook, Google Colab, Kaggle and VS Code • NumPy and Pandas for data handling • Missing data and real European datasets • Data visualisation with Matplotlib and Seaborn • Supervised, unsupervised and reinforcement learning • The Machine Learning workflow • Overfitting, underfitting and evaluation metrics • Linear regression and feature engineering • Scikit-learn workflows and model development • Hyperparameter tuning and ensemble methods • K-Nearest Neighbours, Decision Trees, Support Vector Machines and Random Forests The included No-Prep Lessons provide ready-to-teach classroom support through presentations, student worksheets, practical activities, answer materials, assessment opportunities and lesson review tasks. The full course book adds detailed explanations, diagrams, code examples, comparison tables, key concepts, worked examples and practice exercises. Students also complete practical tasks using Python, Jupyter, NumPy, Pandas, Matplotlib and Scikit-learn. This bundle is suitable for secondary Computer Science, vocational education, introductory AI courses, coding clubs, homeschooling and independent learning. No previous Artificial Intelligence or Machine Learning experience is required. All resources in this bundle are also available separately. Purchasing the complete bundle provides the full Volume 1 learning sequence in one organised collection and offers better value than purchasing each No-Prep Lesson individually. What’s Included This bundle includes: Full 192-page AI & Machine Learning: Volume 1 — Foundations & Data course book Complete collection of Volume 1 No-Prep Lesson packs Teaching presentations Student worksheets Practical coding and data activities Answer materials Review and assessment tasks Classroom activities and exit-ticket style checks Resources covering all seven chapters of Book 1

£15.00

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