Combo Offer: Full Stack Data Science with Generative AI & Robotics

Combo Offer: Full Stack Data Science with Generative AI & Robotics

This comprehensive program is designed for learners who want to master Data Science, Generative AI, and Robotics in one powerful journey. The course covers the entire Data Science stack—from Python, SQL, Statistics, Machine Learning, Deep Learning, NLP, and Big Data to Cloud Deployment—while also diving deep into Robotics fundamentals, Arduino, Raspberry Pi, Sensors, Actuators, and IoT.

Learners will explore Generative AI applications for text, vision, and automation, and apply these skills to real-world robotics systems such as autonomous vehicles, smart automation, and AI-powered robots. With hands-on projects, simulations, and capstone development, this course ensures both theoretical knowledge and practical expertise.

By the end of the program, learners will be able to analyze data, build AI models, design robotic systems, and integrate them into intelligent, real-world solutions—making them industry-ready for the future of AI-driven innovation.

Category: Tags: , ,

1. Python & Programming Foundations

  • Python basics: Anaconda, IDEs, Identifiers, Comments, Keywords

  • Data types & structures: Strings, Lists, Tuples, Sets, Dictionaries

  • Operators & Control Flow: Loops, If-Else, Nested structures, Jump statements

  • Functions: Arguments, Recursion, Lambda, Built-ins

  • Modules & Exception Handling: Custom modules, Try-Except

  • File Handling: File types, File modes, OS module

  • OOP: Classes, Objects, Inheritance, Encapsulation, Polymorphism, Abstraction

  • Multithreading

2. Python Libraries for Data & Visualization

  • NumPy: Arrays, Operations, Functions

  • Pandas: Series, DataFrames, Cleaning & Transformation

  • Matplotlib & Seaborn: Charts, Graphs, Visualizations

  • Tkinter: GUI Apps

  • Multiple mini-projects

3. SQL & Databases

  • RDBMS vs NoSQL

  • Tables, CRUD, Functions, Joins, Subqueries

  • Grouping, Filtering, Constraints, Procedures, Set operations

4. Statistics for Data Science

  • Data collection, Sampling, Representation

  • Central Tendency, Dispersion, Skewness & Kurtosis

  • Correlation, Regression

  • Probability & Hypothesis Testing

  • Z-Test, T-Test, Chi-Square, ANOVA, F-Test

  • Linear Regression

5. Advanced Excel

  • Data Formatting, Transformation

  • Functions: Math, Logical, Lookup

  • Pivot Tables, Pivot Charts, Conditional Formatting

  • Flash Fill, Macros, Worksheet Protection

6. Power BI

  • Power BI Desktop vs Service

  • Data import, transformation, modeling

  • DAX, Visualizations, KPIs, Filters, Drill Downs

  • Dashboards, Q&A Visuals, Smart Narratives

7. Machine Learning

  • AI vs ML vs DL vs DS

  • Data preprocessing, EDA

  • Regression, Classification (Logistic, KNN, SVM, Trees, Random Forest, Naive Bayes)

  • Evaluation (Confusion Matrix, Precision, Recall, F1, ROC-AUC)

  • Unsupervised: Clustering, PCA

  • Advanced: Ensemble, Boosting, Hyperparameter Tuning

  • Time Series Forecasting

  • NLP Basics

  • Ethics & Bias

  • Real-world ML projects

8. Big Data

  • Hadoop Ecosystem (HDFS, YARN, Hive, Pig, HBase)

  • Sqoop, Flume

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

  • Cloud Big Data (AWS, Azure, GCP)

9. Cloud Computing

  • Service Models (IaaS, PaaS, SaaS)

  • Deployment Models (Public, Private, Hybrid)

  • Virtualization & Hypervisors

  • Cloud Services (EC2, S3, IAM, Networking)

  • Serverless (AWS Lambda, GCP Functions)

  • Cloud Security & Monitoring

  • Deploying Web & AI Apps

10. Tableau

  • Tableau Interface & Data Sources

  • Data cleaning & preparation

  • Charts: Bar, Line, Pie, Maps, TreeMaps

  • LOD Expressions, Parameters, Calculated Fields

  • Dashboards & Story Points

  • Publishing (Public, Server)

11. Deep Learning

  • Foundations: Math, Optimization

  • Frameworks: TensorFlow, Keras, PyTorch

  • ANNs, CNNs (ResNet, VGG, Inception), RNNs, LSTMs, GRUs

  • Transfer Learning

  • Projects in Image & Text

12. NLP (Natural Language Processing)

  • Text preprocessing, Tokenization

  • Feature extraction: BoW, TF-IDF, Word2Vec, GloVe, FastText

  • Text classification: Sentiment, Spam, NER, POS

  • Sequence Models: LSTM, GRU, Attention, Seq2Seq

  • Advanced NLP: Summarization, Translation, QA

  • Deployment (Flask, Streamlit, Cloud)

13. Computer Vision

  • Image basics: Pixels, Channels

  • Processing: Thresholding, Blurring, Edges

  • CNNs, Transfer Learning, Augmentation

  • Object Detection: R-CNN, YOLO, SSD

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

  • Face/Gesture Recognition, OCR, Video Processing

  • Deployment (Web, Mobile, Cloud)

14. Generative AI

  • Foundations & Applications

  • Models: GANs, VAEs, Diffusion, LLMs

  • Tools: Hugging Face, LangChain, PyTorch, TensorFlow

  • GANs (DCGAN, CycleGAN, StyleGAN)

  • LLMs: Transformers, Fine-tuning, RAG with Vector DBs

  • Diffusion: Stable Diffusion, ControlNet

  • Multi-Modal AI: CLIP, ALIGN

  • Training Optimization (LoRA, QLoRA, PEFT)

  • Deployment: Flask, FastAPI, Gradio, LangChain Agents

  • Ethics: Bias, Privacy, Copyright

15. Robotics with AI

  • Robotics Overview: Types (Industrial, Service, Humanoid, Swarm, Autonomous)

  • Tools: Arduino, Raspberry Pi, TinkerCAD, Proteus

  • Hardware: Breadboards, Resistors, LEDs, Motors

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

  • Actuators: DC, Stepper, Servo + Motor Drivers

  • Arduino IDE: Loops, I/O, Projects (LED Blink, RGB, Traffic Light)

  • Raspberry Pi: OS Setup, Linux, GPIO, Smart Home Automation

  • IoT Integration: Bluetooth HC-05, Wi-Fi, Cloud (ThingSpeak, Blynk)

  • Projects: Temp-controlled fan, Bluetooth Car, Smart Irrigation

  • Motion Control: PWM, Robotic Arm, DC Motor Controller

  • AI in Robotics: OpenCV (Object Recognition, Gesture Recognition), Face/Person Recognition

  • ML/DL integration for autonomous decision-making

  • Projects: Face Detection Security, Gesture-controlled Devices, AI-Powered Autonomous Robot

16. Capstone Project

  • End-to-end project combining Data Science + GenAI + Robotics

  • Examples:

    • AI-powered Smart Surveillance Robot

    • Multi-modal GenAI Healthcare Assistant Robot

    • IoT + AI Smart City Automation

Course Outcomes

By completing this course, learners will be ready to:

  • Master Python, SQL, Statistics, ML/DL, NLP, Computer Vision, and Generative AI.

  • Build and deploy AI-powered robotics systems with Arduino, Raspberry Pi, sensors, and actuators.

  • Analyze and visualize data using Excel, Power BI, and Tableau.

  • Handle big data with Hadoop/Spark and deploy solutions on AWS, Azure, and GCP.

  • Understand AI ethics, bias, and security for responsible innovation.

  • Be job-ready for roles like Data Scientist, ML/AI Engineer, Robotics Engineer, and IoT Developer.

  • Showcase skills through real-world projects and capstone prototypes.

Related Courses

Join Our Community

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