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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
| Machine Learning at Scale | 25-30% | - Unsupervised Learning
- 1. Anomaly Detection
- 2. Dimensionality Reduction
- 3. Clustering Algorithms
- Supervised Learning
- 1. Ensemble Methods
- 2. Support Vector Machines
- 3. Time Series Forecasting
- Deep Learning Fundamentals
- 1. Recurrent Networks
- 2. Neural Networks
- 3. Convolutional Networks
|
| 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
|
| Foundations of Data Science | 15-20% | - Data Science Ethics
- 1. Privacy and Confidentiality
- 2. Transparency and Explainability
- 3. Bias and Fairness
- Data Science Lifecycle
- 1. Model Building and Evaluation
- 2. Data Collection and Preparation
- 3. Problem Formulation
- 4. Deployment and Monitoring
|
| Advanced Statistical Modeling | 20-25% | - Regression Analysis
- 1. Generalized Linear Models
- 2. Linear and Logistic Regression
- 3. Regularization Techniques
- Statistical Inference
- 1. Bayesian Methods
- 2. Confidence Intervals
- 3. Hypothesis Testing
|
| Big Data Engineering | 15-20% | - Cloud Computing for Data Science
- 1. Containerization
- 2. AWS/Azure/GCP Services
- 3. Serverless Architectures
- Distributed Computing
- 1. Apache Spark
- 2. Hadoop Ecosystem
- 3. Data Pipelines
|
DASCA Senior Data Scientist Sample Questions:
1. Which of the following is a DevOps Practice?
A) Continuous delivery
B) Continuous integration
C) All of the above
D) Continuous build
2. Machine learning can be used in:
A) Pattern and image recognition
B) Fraud detection
C) Web search results
D) Real-time ads on web pages and mobile devices
E) All of the above
3. Exploratory analytic algorithms help the Data Science team to better:
A) Understand patterns in the data
B) Gain a high-level understanding of relationships
C) Both A and B
D) Understand the data content
E) All of the above
4. Semi-structured data does NOT include:
A) Database system
B) File systems
C) Schema-full data
D) Scientific data
5. Which of the following is TRUE for data lake?
A) None of the above
B) The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized
C) The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
D) The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
E) The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive
Solutions:
Question # 1 Answer: C | Question # 2 Answer: E | Question # 3 Answer: E | Question # 4 Answer: C | Question # 5 Answer: B,C,E |