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DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

Question #1

Machine learning can be categorized as:

A. Reinforcement learning
B. All of the above
C. Supervised learning
D. Unsupervised learning

Reveal Solution Discussion 0

Correct Answer: B 🗳️