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DS-U3-ARQ 1

Active recall questions are a powerful method for reinforcing learning and ensuring deep understanding. Here are some active recall questions based on Unit III - Data Analytics Lifecycle to help you prepare for the exam effectively:

Unit 3: Data Analytics Lifecycle - Active Recall Questions

1. Data Analytics Lifecycle

  1. What are the six key stages of the data analytics lifecycle?
  2. Describe the main objectives and activities involved in the Discovery phase.
  3. Why is the Model Planning phase critical, and what key activities are performed during this phase?
  4. Explain the importance of the Operationalize phase in the data analytics lifecycle.

2. Data Collection

  1. What are the different methods of data collection, and what are the pros and cons of each?
  2. How can you ensure the quality of data during the data collection process?

3. Data Cleaning

  1. What are some common data cleaning techniques, and provide examples of each.
  2. What challenges might you face during the data cleaning process, and how can you address them?

4. Data Transformation

  1. What is data transformation, and why is it significant in the data analytics process?
  2. Describe different techniques used for data transformation with examples.

5. Exploratory Data Analysis (EDA)

  1. What is the purpose of Exploratory Data Analysis (EDA)?
  2. What are some techniques used in EDA, and how do they help in understanding the data?

6. Data Integration

  1. What challenges are commonly encountered during data integration?
  2. How can you handle data from different sources during the integration process?

7. Data Reduction

  1. What is data reduction, and why is it important in the data analytics lifecycle?
  2. What are some techniques used for data reduction, and how do they impact the analysis process?

8. Data Analysis

  1. What are the different types of data analysis techniques? Provide examples of when each type is used.
  2. How do you choose the appropriate data analysis technique for a given problem?

9. Data Interpretation

  1. Explain the process of data interpretation and its significance in deriving insights.
  2. What are common pitfalls in data interpretation, and how can you avoid them?

10. Data Visualization

  1. What are the principles of effective data visualization?
  2. Describe different types of data visualization tools and their use cases.

Additional Contextual Questions

1. Application and Integration

  1. How would you apply the data analytics lifecycle to a real-world business problem?
  2. Explain how data preparation impacts the subsequent phases of the data analytics lifecycle.

2. Case Study-Based Questions

  1. Given a dataset with missing values and outliers, how would you approach cleaning and preparing this data?
  2. Describe a scenario where data integration poses significant challenges. How would you overcome these challenges?

3. Advanced Understanding

  1. What are the implications of poor data quality on the model building and operationalize phases?
  2. How can effective communication of results influence business decision-making?

These active recall questions cover the core concepts and practical applications of the Data Analytics Lifecycle, helping to ensure a robust understanding and readiness for exam questions. Regularly practicing these questions will help reinforce your knowledge and improve recall during the exam.