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INTERMEDIATE

Data Science

From data analysis to machine learning and production AI. Master the full data science stack.

6 modules60 lessons

Course Curriculum

Module 1

Data Visualization

Create compelling charts, dashboards, and visual stories with Matplotlib, Seaborn, and Plotly.

10 hoursStart Module

Matplotlib Advanced Techniques45 min 3D Visualizations and Advanced Plots50 min Plotly Fundamentals50 min Advanced Plotly Techniques55 min Bokeh for Web Visualizations50 min D3.js Fundamentals for Data Scientists60 min +7 more lessons →

Module 2

Advanced Machine Learning

Ensemble methods, feature engineering, hyperparameter tuning, and model evaluation techniques.

12 hoursStart Module

Advanced Bagging and Random Forests50-60 min Gradient Boosting Mastery: XGBoost, LightGBM, and CatBoost60 min Stacking and Blending Techniques55 min Feature Engineering Fundamentals55 min

Module 3

Deep Learning

Neural networks, CNNs, RNNs, transformers, and training deep models with PyTorch and TensorFlow.

12 hoursStart Module

Neural Network Fundamentals60 min Backpropagation and Optimization65 min Activation Functions and Initialization55 min PyTorch Fundamentals60 min TensorFlow and Keras60 min Framework Comparison and Selection50 min +10 more lessons →

Module 4

NLP & Text Analytics

Text preprocessing, sentiment analysis, named entity recognition, and large language model APIs.

10 hoursStart Module

Text Preprocessing Fundamentals50 min Word Embeddings: Word2Vec & GloVe55 min Text Representation: TF-IDF & Bag of Words45 min Text Classification Techniques50 min Named Entity Recognition45 min Sentiment Analysis50 min +8 more lessons →

Module 5

Computer Vision

Image classification, object detection, segmentation, and real-world computer vision applications.

9 hoursStart Module

Introduction to Computer Vision45 min Image Processing Fundamentals50 min Feature Detection and Extraction55 min Convolutional Neural Networks60 min Image Classification55 min Object Detection65 min +6 more lessons →

Module 6

Data Science in Production

MLOps, model serving, monitoring, A/B testing, and deploying machine learning systems at scale.

2 hoursStart Module

Introduction to MLOps50 min