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DASCA SDS Exam Syllabus Topics:

Section	Weight	Objectives
Data Visualization and Communication	10-15%	- Visualization Principles 1. Storytelling with Data 2. Dashboard Design - Tools and Technologies 1. Python Visualization Libraries 2. Tableau/Power BI
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. Deployment and Monitoring 2. Data Collection and Preparation 3. Model Building and Evaluation 4. Problem Formulation
Advanced Statistical Modeling	20-25%	- Regression Analysis 1. Regularization Techniques 2. Linear and Logistic Regression 3. Generalized Linear Models - Statistical Inference 1. Hypothesis Testing 2. Confidence Intervals 3. Bayesian Methods
Big Data Engineering	15-20%	- Distributed Computing 1. Hadoop Ecosystem 2. Data Pipelines 3. Apache Spark - Cloud Computing for Data Science 1. AWS/Azure/GCP Services 2. Containerization 3. Serverless Architectures
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

DASCA Senior Data Scientist Sample Questions:

1. Which classification steps are performed in inductive techniques?
i. Training Step
ii. Test Step
iii. Validation Step
iv. Application Step

A) i, ii
B) i, ii, iii, iv
C) i, ii, iv
D) ii, iii

2. Which of the following is FALSE for Social Network Analysis (SNA)?

A) SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
B) None of the above
C) Social Network Analysis (SNA) is an example of trend analysis
D) Social Network Analysis (SNA) is an example of graph analysis
E) SNA is used to investigate social structures and relationships across social networks

3. Which of the following is NOT an example of graphical model?

A) Electrical circuits
B) Flow charts
C) Computer networks
D) Road maps
E) Geographical networks

4. Which of the following is NOT used to implement Agile?

A) Scrum
B) Extreme Programming (XP)
C) Six Sigma
D) Kanban

5. Which of the following can be classified as factor analysis in machine learning?

A) None of the above
B) Exploratory factor analysis
C) Both A and B
D) Confirmatory factor analysis

Solutions:

Question # 1
Answer: B

Question # 2
Answer: C

Question # 3
Answer: B

Question # 4
Answer: C

Question # 5
Answer: C