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DASCA SDS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Advanced Statistical Modeling20-25%- Statistical Inference
  • 1. Bayesian Methods
  • 2. Confidence Intervals
  • 3. Hypothesis Testing
- Regression Analysis
  • 1. Regularization Techniques
  • 2. Generalized Linear Models
  • 3. Linear and Logistic Regression
Topic 2: Big Data Engineering15-20%- Cloud Computing for Data Science
  • 1. Serverless Architectures
  • 2. Containerization
  • 3. AWS/Azure/GCP Services
- Distributed Computing
  • 1. Data Pipelines
  • 2. Hadoop Ecosystem
  • 3. Apache Spark
Topic 3: Data Visualization and Communication10-15%- Tools and Technologies
  • 1. Tableau/Power BI
  • 2. Python Visualization Libraries
- Visualization Principles
  • 1. Storytelling with Data
  • 2. Dashboard Design
Topic 4: Machine Learning at Scale25-30%- Unsupervised Learning
  • 1. Dimensionality Reduction
  • 2. Clustering Algorithms
  • 3. Anomaly Detection
- Supervised Learning
  • 1. Support Vector Machines
  • 2. Ensemble Methods
  • 3. Time Series Forecasting
- Deep Learning Fundamentals
  • 1. Recurrent Networks
  • 2. Convolutional Networks
  • 3. Neural Networks
Topic 5: Foundations of Data Science15-20%- Data Science Ethics
  • 1. Privacy and Confidentiality
  • 2. Bias and Fairness
  • 3. Transparency and Explainability
- Data Science Lifecycle
  • 1. Data Collection and Preparation
  • 2. Problem Formulation
  • 3. Deployment and Monitoring
  • 4. Model Building and Evaluation

DASCA Senior Data Scientist Sample Questions:

1. Which of the following errors refers to the wrong negation of a true null hypothesis?

A) Logical Error
B) None of the above
C) Type II Error
D) Type I Error
E) Hypothesis Error


2. Which of the following is TRUE for Tensor?

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


3. Which of the following is TRUE for "By" analysis?

A) All of the above
B) "By" analysis is used to create a collaborative technique to drive alignment between the business users and the data scientists to identify and brainstorm variables and metrics that might be better predictors of business performance.
C) The "By" analysis technique reinforces the process of "thinking like a data scientist."
D) "By" analysis is a technique by which business subject matter experts (SMEs) and the Data Science team could collaborate to uncover new variables and metrics that might be better predictors of business performance.
E) Both B and C


4. The Big Data Vision Workshop process is ideal for organizations who:

A) All of the above
B) Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
C) Both A and B
D) Have a wealth of data that they do not know how to monetize
E) Have a desire to leverage Big Data to transform their business but do not know where and how to start


5. Which of the following is correct about microservices?

A) All of the above
B) Each service can be developed in any language that best fits the requirement
C) Each service is a new project
D) Each service is independent


Solutions:

Question # 1
Answer: D
Question # 2
Answer: C
Question # 3
Answer: A
Question # 4
Answer: A
Question # 5
Answer: A

1166 Customer ReviewsCustomers Feedback (* Some similar or old comments have been hidden.)

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