Combo Program: Advanced Data Science + Robotics with AI (Kids)

Combo Program: Advanced Data Science + Robotics with AI (Kids)

An all-in-one program combining advanced data science and AI-powered robotics for kids.

Category: Tags: , , , ,

This 24-month combo program is designed for young learners to gain deep knowledge in both Data Science and AI Robotics. It provides a perfect balance of coding, analytics, and hands-on robotics.
What they’ll learn:

Python & Programming Foundations

  • Introduction to Python

  • Exploring Anaconda and other IDEs

  • Identifiers, Comments, Keywords

  • Datatypes & Operations

    • Strings, Lists, Tuples, Sets, Dictionaries

  • Operators & Control Statements

    • Types of Operators, Binary Numbers

    • Decision Control Statements, Nested If

    • Loops, Nested Loops, Jump Control Statements

  • Functions

    • Types, Arguments, Built-in Functions, Recursion, Lambda Functions

  • Modules & Exception Handling

    • Creating & Importing Modules

    • Errors vs Exceptions, Raising & Handling Exceptions, Nested Try

  • File Handling

    • File Types, Modes, Paths, Methods

  • OS Module

    • Functions and Methods

  • OOPS Concepts

    • Classes & Objects, Methods

    • Inheritance, Encapsulation, Polymorphism, Abstraction

    • Getters, Setters, Deleters

  • Multithreading

  • Python Libraries

    • NumPy, Pandas, Matplotlib, Tkinter

Robotics Fundamentals

  • What is Robotics? Definition & Key Concepts

  • Types of Robots: Industrial, Service, Autonomous Vehicles, Humanoid, Swarm Robotics

  • Tools & Frameworks: Arduino, Raspberry Pi

  • Sensors: Ultrasonic, IR, Temperature, Humidity, Accelerometer, Gyroscope

  • Actuators: DC Motors, Servo Motors, Stepper Motors

  • Simulation Tools: TinkerCAD

  • Hands-On Projects

    • Simple LED Circuit

    • Traffic Light System

    • Smart Home Automation Prototype

Arduino & Raspberry Pi

  • Introduction to Arduino & Raspberry Pi

  • Setting Up Arduino IDE and Raspberry Pi OS

  • GPIO Pin Configuration

  • Reading Sensors (Temperature, Light, Soil Moisture)

  • Controlling LEDs, Buzzers, Motors, and Relays

  • Projects

    • Bluetooth-Controlled Car

    • Smart Irrigation System

    • Temperature-Controlled Fan System

SQL & Database Management

  • Understanding Databases, RDBMS vs NoSQL

  • SQL Basics: Syntax, CRUD Operations, Datatypes

  • Advanced SQL: Functions, Joins, Grouping, Filtering, Subqueries, Set Operations, Constraints, Procedures

Statistics

  • Introduction, Importance, Applications

  • Types of Statistics: Qualitative vs Quantitative

  • Data Collection, Sampling, Organization, Graphical Representation

  • Measures of Central Tendency: Mean, Median, Mode

  • Measures of Dispersion: Variance, Standard Deviation, Skewness, Kurtosis

  • Correlation, Regression, Probability

  • Hypothesis Testing: Null & Alternate, Type I & II Errors, Z-test, T-test, Chi-Square, ANOVA, F-test

  • Simple Linear Regression

Data Visualization & Business Intelligence

  • Excel: Formulas, Functions, Pivot Tables, Charts, Conditional Formatting, Macros

  • Power BI: Data Cleaning, Transformation, Modeling, DAX, Visualizations, Dashboards, RLS, Publishing

  • Tableau: Connecting Data, Calculated Fields, Charts, Dashboards, Story Points, Publishing

Machine Learning & AI

  • Introduction: AI, ML, DL, Data Science

  • Types of ML: Supervised, Unsupervised, Reinforcement

  • Applications of ML

  • Environment Setup: Anaconda, Jupyter Notebook, Python Libraries

  • Data Preprocessing & EDA: Missing Data, Encoding, Feature Scaling, Feature Engineering, Visualization

  • Supervised Learning: Linear, Multiple, Polynomial Regression, Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Naive Bayes

  • Unsupervised Learning: K-Means, Hierarchical, DBSCAN, PCA

  • Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, ROC-AUC, MSE, RMSE, MAE

  • Advanced ML: Ensemble Learning, Hyperparameter Tuning, Time Series Forecasting, NLP Basics

  • AI in Robotics

    • Computer Vision: OpenCV, Object Detection, Face Recognition, Gesture Recognition

    • AI Automation Systems

  • Model Deployment: Pickle, Joblib, Streamlit, Flask, Cloud Overview

  • Projects: Face Detection Security System, AI-based Robotics Automation

Deep Learning

  • Introduction: AI vs ML vs DL, Applications, Neural Networks History

  • Math Foundations: Linear Algebra, Calculus, Probability, Gradient Descent, Optimization

  • ANN: Perceptrons, MLP, Activation Functions, Feedforward & Backpropagation, Loss Functions, Regularization

  • CNN: Convolution, Pooling, Transfer Learning (VGG, ResNet, Inception, MobileNet), Image Classification Projects

  • RNN: Architecture, LSTM, GRU, Sequence Models, Applications (Text, Time Series, Sentiment Analysis)

Natural Language Processing (NLP)

  • Introduction, Applications, Traditional vs DL-based NLP

  • Text Preprocessing: Tokenization, Lemmatization, Stemming, Cleaning, N-grams

  • Feature Extraction: Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe, FastText)

  • Text Classification: Sentiment Analysis, Spam Detection, NER, POS Tagging

  • Sequence Models: RNN, LSTM, GRU, Seq2Seq, Attention

  • Advanced NLP: Text Generation, Summarization, Translation, QA, Zero/Few-shot

  • Model Deployment: Streamlit, Flask, Cloud (AWS, GCP, HuggingFace)

  • Ethics & Responsible NLP

Computer Vision

  • Introduction, Applications, Challenges

  • Image Fundamentals: Pixels, Channels, Formats, OpenCV

  • Image Processing: Thresholding, Blurring, Edge Detection, Morphology

  • Deep Learning for CV: CNN-based Models, Transfer Learning, Data Augmentation

  • Object Detection: R-CNN, Fast/Faster R-CNN, YOLO, SSD

  • Image Segmentation: Semantic, Instance, U-Net, Mask R-CNN

  • Applications: Face & Gesture Recognition, OCR, Video Processing & Tracking

  • Deployment: Web, Cloud, Mobile

Big Data

  • Big Data Concepts: 5Vs, Architecture, Batch vs Stream

  • Hadoop Ecosystem: HDFS, YARN, Hive, Pig, HBase

  • Data Transfer: Sqoop, Flume

  • Apache Spark: RDDs, DataFrames, Spark SQL, PySpark, Spark Streaming

  • Big Data on Cloud: AWS, Azure, GCP

  • Big Data Use Cases

Cloud Computing

  • Cloud Basics: IaaS, PaaS, SaaS, Deployment Models

  • Virtualization & Hypervisors

  • AWS, Azure, GCP Overview

  • Cloud Services: EC2, S3, IAM, Networking, Serverless (Lambda), Monitoring, Billing

  • Deploying Web Apps and AI Models on Cloud

  • Cloud Security & Real-World Use Cases

Robotics & AI Integration

  • Robotics Fundamentals: Sensors, Actuators, Controllers

  • Arduino & Raspberry Pi Programming: LED, Motors, Relays, Sensors

  • AI in Robotics: Gesture Recognition, Face Detection, Autonomous Robots

  • IoT & Cloud Integration for Smart Systems

  • Hands-On Projects: Smart Home Robot, Obstacle Avoidance Robot, AI-based Automation

Capstone Projects

  • End-to-End Real-World Integration of:

    • Python, SQL, Statistics, ML/DL, NLP, CV, Big Data, Cloud, Robotics, AI

  • Example Projects:

    • AI-Powered Smart Home Robot

    • Autonomous Obstacle Avoidance Robot

    • Predictive Analytics Dashboard with Robotics Control

    • Face & Gesture Recognition System

Course Outcomes

  • Apply Python, SQL, and statistics for data analysis.

  • Build and evaluate Machine Learning and Deep Learning models.

  • Implement AI in Robotics: sensors, actuators, computer vision, and gesture control.

  • Work with Arduino, Raspberry Pi, and IoT systems.

  • Visualize data using Excel, Power BI, and Tableau.

  • Process Big Data with Hadoop, Spark, Hive, and HBase.

  • Deploy AI and Robotics solutions on cloud platforms (AWS, Azure, GCP).

  • Develop capstone projects combining Data Science, AI, and Robotics.

  • Understand ethical AI and bias mitigation in applications.

Related Courses

Join Our Community

Enter your email address to register to our newsletter subscription delivered on regular basis!