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DASCA SDS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Advanced Statistical Modeling	20-25%	- Regression Analysis 1. Linear and Logistic Regression 2. Regularization Techniques 3. Generalized Linear Models - Statistical Inference 1. Confidence Intervals 2. Bayesian Methods 3. Hypothesis Testing
Topic 2: Data Visualization and Communication	10-15%	- Tools and Technologies 1. Python Visualization Libraries 2. Tableau/Power BI - Visualization Principles 1. Dashboard Design 2. Storytelling with Data
Topic 3: Machine Learning at Scale	25-30%	- Unsupervised Learning 1. Anomaly Detection 2. Dimensionality Reduction 3. Clustering Algorithms - Deep Learning Fundamentals 1. Recurrent Networks 2. Convolutional Networks 3. Neural Networks - Supervised Learning 1. Ensemble Methods 2. Time Series Forecasting 3. Support Vector Machines
Topic 4: Foundations of Data Science	15-20%	- Data Science Ethics 1. Transparency and Explainability 2. Privacy and Confidentiality 3. Bias and Fairness - Data Science Lifecycle 1. Deployment and Monitoring 2. Data Collection and Preparation 3. Problem Formulation 4. Model Building and Evaluation
Topic 5: Big Data Engineering	15-20%	- Distributed Computing 1. Hadoop Ecosystem 2. Data Pipelines 3. Apache Spark - Cloud Computing for Data Science 1. Serverless Architectures 2. AWS/Azure/GCP Services 3. Containerization