Data Science

Modul 1: Programming and Data Foundations

• SQL for Data Analysis (joins, subqueries, window functions, views)

• Python for Data Science (variables, loops, functions)

• Algorithms and Problem Solving (Leetcode Practice)

• NumPy, Pandas, and Matplotlib

• Data Cleaning & Exploratory Data Analysis (EDA)

Modul 2: Machine Learning Essentials

• Scikit-learn: Pipelines, Model Training, and Evaluation Metrics

• Supervised Learning: Linear Models, Decision Trees, SVM, Ensemble Methods

• Unsupervised Learning: PCA, Clustering

• Time Series Analysis with Scikit-learn

Modul 3: Deep Learning & Neural Networks

• Introduction to TensorFlow and Neural Networks

• Training Deep Learning Models

• Transfer Learning and Model Scaling

• Error Analysis and Model Tuning

Modul 4: Computer Vision

• CNNs and Image Classification (MNIST, Fashion MNIST)

• Object Detection with YOLO

• Advanced CNN Architectures (UNET, GoogLeNet, Vision Transformers)

• Applications: Lane Detection, Face Verification with Siamese Networks
  
  

Modul 5: Natural Language Processing (NLP)

• NLP with TensorFlow

• Sentiment Analysis, SkimLit NLP, and Shakespeare NLP Project

• Time Series Forecasting with TensorFlow
  
  

Modul 6: Advanced Topics & Career Prep

• AutoEncoders & GANs

• Project: GAN Fashion MNIST

• Model Deployment and Scaling in Production

• Data Visualization with Tableau

• Interview Preparation & Case Studies