Course Overview
The Complete Data Intelligence Stack
Data Science Foundation
NumPy, Pandas, Matplotlib — collect, clean, analyze, and visualize data to drive business decisions.
Machine Learning
Supervised, unsupervised, and reinforcement learning. Build models that predict, classify, and cluster.
Deep Learning & Neural Nets
TensorFlow and Keras — build CNNs, RNNs, and transformers for images, text, and sequential data.
Real Industry Projects
Work on capstone projects: churn prediction, image classification, NLP sentiment analysis, and recommendation systems.
Tech Stack
Tools & Technologies
Python
Jupyter Notebook
NumPy
Pandas
Matplotlib
Scikit-Learn
TensorFlow
Keras
MongoDB
Power BI
Curriculum
Module Breakdown
1
Python for Data Science
7 Topics
- Python Refresher & Environment Setup
- NumPy — Arrays, Broadcasting, Linear Algebra
- Pandas — DataFrames, Merging, GroupBy
- Data Cleaning & Preprocessing
- Matplotlib & Seaborn Visualizations
- Exploratory Data Analysis (EDA)
- Mini Project: Sales Data EDA
2
Machine Learning
9 Topics
- ML Workflow & Model Lifecycle
- Regression: Linear, Polynomial, Ridge
- Classification: Logistic, KNN, SVM, Decision Trees
- Ensemble Methods: Random Forest, XGBoost
- Clustering: K-Means, DBSCAN
- Dimensionality Reduction: PCA
- Model Evaluation & Cross-Validation
- Hyperparameter Tuning
- Project: Customer Churn Prediction
3
Artificial Intelligence & Deep Learning
8 Topics
- Neural Network Architecture & Backpropagation
- TensorFlow & Keras Foundations
- CNNs — Image Classification
- Transfer Learning (VGG, ResNet)
- RNNs & LSTMs — Sequential Data
- NLP: Tokenization, Word2Vec, Transformers
- Introduction to Generative AI
- Project: Image Classifier & Sentiment Analyzer
4
Capstone & Deployment
5 Topics
- Model Deployment with Flask/FastAPI
- Power BI & Tableau Dashboards
- End-to-End ML Pipeline
- Portfolio Building & GitHub
- Capstone Project: Full DS/ML Solution
Learning Outcomes
Skills You'll Master
Perform end-to-end data analysis and build dashboards
Train, evaluate, and deploy ML models to production
Build deep learning models for vision and language tasks
Work fluently with Pandas, Scikit-Learn, and TensorFlow
Solve real business problems with data-driven solutions
Present findings through compelling visualizations