Full Stack AI Driven Data Analytics

Transfotech Academy provides well-organized online Full Stack AI Driven Data Analytics training. This training will help data professionals at all stages of their careers. Our training program will allow beginners to increase their skills and confidently pursue their careers in data analytics. Enroll and catch USA’s best demanding job.

Course Structure Overview

Student Dashboard

We provide an individual dashboard for each and every student of ours. It will help you track down all of your classes, materials and progress.

Online Live Classes

You can attend our class online in your own comfort. The classes will be live. You can engage efficiently during online classes.

Recording of the Classes

If you miss out on anything during our online training, you can easily access our recorded classes and keep up with the rest.

Resume Building

An organized resume can easily get you your targeted job. Our training online classes will assist you to build a standard job resume.

Interview Preparation

An interview is a gateway to getting a job. It is very crucial for landing a good job. Transfotech will also help you prepare for your interview.

Easy Enrollment

This online program of ours is very easy to register for. Your desired job is just a few clicks away.

What you will learn by doing the course

Data Analytics is transforming every industry with AI-driven insights. This course equips you with the essential tools, techniques, and workflows to thrive in the high-demand world of data analytics. From hands-on labs to career-ready projects, you’ll be prepared to analyze, automate, and innovate in today’s data-powered landscape.

Understand how data flows through ecosystems, apply analytical thinking, and learn core principles like pipelines, storytelling, and business insight generation.
Use tools like ChatGPT, Claude, and LLM workflows to automate reporting, enhance analysis, build prompts, and accelerate data tasks with AI-driven precision.
Gain hands-on experience with Excel functions, relational databases, analytical SQL queries, and Python programming for data cleaning, wrangling, and ETL automation.
Learn data visualization with Matplotlib, Seaborn, Plotly, and build interactive dashboards using Tableau and Power BI for real-world business intelligence.
Learn regression, classification, clustering, neural networks, CNNs, and modern transformer-based architectures to solve real predictive problems.
Build professional portfolios, prepare for interviews, refine your GitHub and LinkedIn, and start applying confidently for Data Analyst, BI, ML, and AI roles.

Work on real datasets, solve analytics problems, build ML pipelines, and complete hands-on projects showcasing your skills to employers. Here is the same section rewritten for Full Stack Data Analyst, keeping the same style, tone, line-length, and structure as your Cybersecurity version.
Analyze complex datasets, build automated workflows, perform ETL processes, and use tools like Python, SQL, Pandas, and NumPy to extract powerful business insights.
Use Generative AI, LangChain, and LLM tools to enhance analytics, generate reports, evaluate prompts, build RAG systems, and automate end-to-end data processes.
Build predictive models using regression, classification, clustering, neural networks, and transformer architectures. Apply ML workflows to real business problems.

Details about the course

Module 1: Data Foundations & Analytics Ecosystem

Understanding the Data Ecosystem & Pipelines
Data Types, Formats, and Business Storytelling
Advanced Excel: XLOOKUP, INDEX/MATCH, SUMIFS
Pivot Tables, Exploratory Charts & Insights
Data Cleaning Fundamentals & Quality Checks
Introduction to Data Lifecycle & Governance
Connecting Business Problems to Data Solutions
Fundamentals of AI & Its Role in Analytics
Lab: Preprocess a Real Dataset
Assignment: Clean & Wrangle a Second Dataset

Module 2: Database Management & SQL for Analytics

Understanding RDBMS, PostgreSQL & MySQL
ER Diagrams, Data Modeling & Schema Design
SQL Joins, CTEs, Subqueries & Window Functions
Aggregations, Ranking & Analytical Queries
Creating Relational Tables & Database Structures
Building SQL Pipelines for Real Scenarios
Indexing, Keys & Query Optimization Basics
Connecting Python to SQL Databases
Lab: Build & Query a Sales Database
Assignment: Analytical SQL Reporting

Module 3: Python Programming (Beginner → Intermediate)

Introduction to Python for Data Analytics
Syntax, Variables, Strings & Operators
Loops, Conditionals & Core Logic
Functions, Arguments & Modular Coding
Lists, Tuples, Sets & Dictionaries
Working with JSON & External Data
Writing Clean & Readable Analytical Code
Lab: Hello AI World + Python Calculator
Assignment: Budget Calculator & Quiz Builder

Module 4: Pandas, NumPy & ETL Pipelines

NumPy Arrays, Matrix Operations & Broadcasting
Pandas DataFrames, Merging & Transformations
Handling Missing, Duplicate & Messy Data
Filtering, Grouping & Aggregating with Pandas
Working with Date/Time & String Operations
Connecting Python to SQL Pipelines
Fundamentals of ETL/ELT Processes
Lab: Explore & Transform Sales Dataset
Assignment: Customer Insights Dashboard

Module 5: Data Visualization & Visual Storytelling

Introduction to Matplotlib for Analytics
Visualizing Trends, Bars, Histograms & Lines
Seaborn for Advanced Statistical Plots
Plotly for Interactive Visualizations
Data Storytelling Techniques & Best Practices
Designing Insight-Driven Visual Narratives
Lab: Product Sales Trend Visualization
Assignment: Data Story Notebook

Module 6: BI Dashboards with Tableau & Power BI

Connecting BI Tools to Real Datasets
Building Interactive Dashboards from Scratch
KPI Cards, Filters, Slicers & Drill-Downs
Parameters, Bookmarks & Actions
Designing Business-Ready Reports
Publishing & Sharing Dashboards
Lab: BI Dashboard Build (Tableau/Power BI)
Assignment: E-Commerce Performance Report

Module 7: Statistics, Math & Probability

Linear Algebra for Machine Learning
Mean, Variance, Standard Deviation
Correlation, Covariance & Real-World Examples
Probability Distributions & Random Variables
Sampling, Bias & Data Variability
Intro to Statistical Modeling
Lab: Regression Visualization & Correlation Map
Assignment: Gradient Descent Demo & Dataset Analysis

Module 8: Introduction to Machine Learning

Supervised, Unsupervised & Reinforcement Learning
ML Pipeline: Train → Validate → Test
Feature Engineering & Data Preparation
Model Evaluation Techniques
Lab: Titanic Classifier
Assignment: ML Survival Prediction

Module 9: Regression, Classification & Predictive Models

Linear & Multiple Regression
Logistic Regression & Decision Trees
Random Forest, KNN & Model Stacking
MSE, RMSE, R², Precision & Recall
Train-Test Split & Cross-Validation
Lab: Housing Price Prediction
Assignment: Regression Project

Module 10: Clustering & Unsupervised Learning

K-Means Clustering
Hierarchical Clustering & Dendrograms
PCA for Dimensionality Reduction
Business Use Cases for Clustering
Lab: Customer Segmentation
Assignment: Market Segmentation Report

Module 11: Neural Networks & Deep Learning

Neurons, Perceptrons & Activation Functions
Forward & Backpropagation
Understanding Weights & Optimization
Lab: Neural Network with NumPy
Assignment: Visualize 2-Layer Network

Module 12: Deep Learning with TensorFlow & PyTorch

Building Neural Network Models
CNNs for Image Classification
Tensors, Dataloaders & Training Loops
Converting Models Between Frameworks
Lab: MNIST CNN Classification
Assignment: PyTorch Classifier Project

Module 13: Transformer Architectures & Modern AI Models

CNN vs RNN vs Transformer Overview
Self-Attention & Multi-Head Attention
Time-Series vs NLP Applications
Lab: LSTM vs Transformer
Assignment: Architecture Comparison

Module 14: Generative AI & Prompt Engineering

Introduction to LLMs (GPT, Claude, Mistral, LLaMA)
Zero-Shot, Few-Shot & Chain-of-Thought Prompts
Persona, Context & Task-Based Prompting
Business, Coding & Analytical Prompt Patterns
Lab: Business & Coding Prompt Workshop
Assignment: Fix and Improve Prompts

Module 15: Prompt Evaluation & LLM Fine-Tuning

Accuracy, Bias & Hallucination Metrics
Tools: Promptfoo, Ragas, EvalLM
Fine-Tuning vs Prompt-Tuning
LoRA, QLoRA & PEFT Frameworks
Lab: Fine-Tune GPT-2 on Custom Dataset
Assignment: Build a Support Chatbot

Module 16: LangChain, RAG Pipelines & Vector Databases

Chains, Memory & Tools
RAG Workflow: Chunking → Embedding → Retrieval
FAISS, Chroma, Weaviate & Pinecone
Building Search-Based AI Systems
Lab: RAG Study Assistant
Assignment: Custom RAG Pipeline

Module 17: Big Data with PySpark

PySpark Basics & DataFrames
Data Lakes vs Data Warehouses
Parquet, ORC & Big Data Optimization
Distributed Data Processing
Lab: Document Q&A Bot with PySpark
Assignment: Retriever Demo

Module 18: API Development & Full-Stack AI Apps

Building APIs with Flask
Creating REST Endpoints & Services
Gradio UI Prototyping
Model-Integrated Applications
Lab: REST API + Gradio Interface
Assignment: API Documentation

Module 19: MLOps, Docker, CI/CD & Deployment

Docker Basics for ML Deployment
Containerizing AI Applications
CI/CD Best Practices
Deploying to Hugging Face Spaces & Render
Lab: Deploy Containerized App
Assignment: Deployment + README

Module 20: Capstone Project, Demo Day & Career Preparation

End-to-End AI/ML Capstone Build
Data → ML → Deployment Production Pipeline
GitHub Portfolio & Documentation
Resume, LinkedIn & Interview Practice
Career Roadmap & Role Guidance
Final Deliverable: Portfolio-Ready AI Application

Course Instructors

FAYEK CHOWDHURY

With over a decade of experience, Fayek specializes in software testing and quality assurance at top companies like Veruna Inc. and OUTFRONT Media. His expertise in QA automation and agile methodologies has delivered impactful results. Passionate about mentoring, he is committed to shaping the next generation of QA Engineers.

Mr. Fuad Bin Saif

Mr. Fuad Bin Saif is an experienced AI Expert and Data Analyst with a strong background in applying artificial intelligence to solve real-world business problems. At Transfotech Academy, he leads the Business & Data Analytics: Integrated with AI course, equipping professionals with practical, tool-based skills for immediate workplace impact. His teaching blends hands-on projects, industry insights, and strategic thinking to make AI accessible for all.

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What is in this course

80+ hours of instructor-led online classes
Detailed course notes
Weekly interactive quizzes for real-world practice
Live problem-solving and Q&A sessions with data experts
Exclusive online support community
120+ Graduates
Lifetime access to course materials

Next batch starts soon

Pricing Plan

Platinum Package

Regular full package $3000

$2499

(For First 10 Students only)

All inclusive, No hidden fees

Job Placement Support Until Hired
In-House Internship for real experience
1:1 Coaching + Mock Interview Sessions
Resume + LinkedIn + Job Portal Build-Up
Personalized Recruiter Support
Live Classes + Tools Installation Help
Free Background Job Verification
Free Full Course Retake (2 Years)
Anytime Course switching option to ANY courses
Lifetime Student portal (LMS) access of class recordings

Gold Package

Regular full package $3500

$2998

(For First 10 Students only)

$1499/month
2 monthly installments

Job Placement Support Until Hired
In-House Internship for real experience
1:1 Coaching + Mock Interview Sessions
Resume + LinkedIn + Job Portal Build-Up
Personalized Recruiter Support
Live Classes + Tools Installation Help
Free Background Job Verification
Free Full Course Retake (1 Year)
Anytime Course switching option to ANY courses for 1 year
Student portal (LMS) access of class recordings for 1 year after course completion

Silver Package

Regular full package $4500

$3995

(For First 10 Students only)

$799/month
5 monthly installments

Job Placement Support Until Hired
In-House Internship for real experience
1:1 Coaching + Mock Interview Sessions
Resume + LinkedIn + Job Portal Build-Up
Personalized Recruiter Support
Live Classes + Tools Installation Help
Free Background Job Verification
Free Full Course Retake for ONE time only
Lifetime Student portal (LMS) access of class recordings for 6 months after course completion

Cybersecurity Engineering

AI Engineering

Business & Data Analytics

Data Science & ML