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DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

Question #1

Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?

A. Box Plots
B. Scatter Plot
C. None of the above
D. Trend Analysis
E. Histogram

Reveal Solution Discussion 0

Correct Answer: A 🗳️