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DASCA SDS Exam Syllabus Topics:

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

DASCA Senior Data Scientist Sample Questions:

1. Which of the following is TRUE for Tensor?

A) All of the above
B) Tensor is an array of floating-point numbers
C) In Tensor, there can be arbitrarily many dimensions to the array
D) Both B and C
E) Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations

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

3. Semi-structured data does NOT include:

A) Database system
B) Schema-full data
C) Scientific data
D) File systems

4. Which of the following is a Python library for fitting Bayesian networks to real data?

A) MCMC
B) SCIMC
C) MyLib
D) SciLib
E) PyMC

5. Self-driving car is an example of:

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

Solutions:

Question # 1
Answer: A

Question # 2
Answer: B

Question # 3
Answer: B

Question # 4
Answer: E