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Microsoft Azure Data Scientist DP-100 [2023/2024]
Data Scientist

What you will learn

Evaluate your ability to understand and clean diverse datasets, addressing missing values, outliers, and anomalies.

Demonstrate your proficiency in applying statistical methods to extract meaningful insights from data, including hypothesis testing and regression analysis.

Assess your skills in feature engineering to enhance model performance and interpretability.

Evaluate your capability to assess model performance, tune hyperparameters, and optimize machine learning models.

Description

The Data Scientist Practice Test is designed to assess and reinforce the skills and knowledge acquired throughout the Data Scientist training program. This comprehensive test encompasses a range of topics essential to the field of data science, providing participants with a simulated real-world experience.

Key Learning Objectives:

  1. Data Exploration and Cleaning: Evaluate your ability to understand and clean diverse datasets, addressing missing values, outliers, and anomalies.
  2. Statistical Analysis: Demonstrate your proficiency in applying statistical methods to extract meaningful insights from data, including hypothesis testing and regression analysis.
  3. Machine Learning Algorithms: Showcase your understanding of various machine learning algorithms, their applications, and the ability to select the most suitable algorithm for a given problem.
  4. Feature Engineering: Assess your skills in feature engineering to enhance model performance and interpretability.
  5. Model Evaluation and Optimization: Evaluate your capability to assess model performance, tune hyperparameters, and optimize machine learning models.
  6. Data Visualization: Demonstrate your skill in creating clear and insightful data visualizations to communicate findings effectively.
  7. Big Data Technologies: Test your knowledge of big data technologies and distributed computing frameworks for handling large-scale datasets.
  8. Ethical Considerations: Explore ethical implications related to data science, including privacy, bias, and responsible AI.

Who Should Take This Course:

This practice test is suitable for individuals who have completed foundational training in data science and want to assess their readiness for real-world challenges. It is also valuable for professionals preparing for data scientist certification exams.


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Prerequisites:

Completion of a foundational data science training program or equivalent knowledge and experience in statistics, programming (e.g., Python or R), and machine learning concepts.

Outcome:

Successful completion of the Data Scientist Practice Test indicates a strong foundation in data science concepts and readiness for real-world applications. Participants will receive detailed feedback on their performance to guide further learning and improvement.

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