In Big Data processing, ________ is a scripting language used with Hadoop to simplify MapReduce programming.
- Pig
- Python
- R
- Scala
Pig is a scripting language used in Big Data processing with Hadoop to simplify MapReduce programming. It provides a high-level platform for creating MapReduce programs without the need for complex Java coding. Python, R, and Scala are also used in the context of Big Data but serve different purposes.
How does A/B testing contribute to data-driven decision making?
- It analyzes historical data to make predictions about future trends.
- It focuses on creating visual representations of data for better understanding.
- It helps in comparing two versions of a webpage or app to determine which performs better.
- It involves analyzing data in real-time.
A/B testing is a method for comparing two versions of a webpage or app to determine which performs better. It contributes to data-driven decision making by providing empirical evidence on the effectiveness of changes, enabling informed decisions based on actual user responses.
What is the output of print({i: i * i for i in range(3)})?
- {0: 0, 1: 1, 2: 16}
- {0: 0, 1: 1, 2: 2}
- {0: 0, 1: 1, 2: 4}
- {0: 0, 1: 1, 2: 8}
The output is a dictionary comprehension where each key-value pair is the square of the corresponding value from the range(3). Therefore, the correct output is {0: 0, 1: 1, 2: 4}.
How should a data analyst approach the task of convincing stakeholders about a data-driven decision that goes against conventional wisdom?
- Aligning with conventional wisdom to maintain stakeholder trust.
- Avoiding discussions about the decision's data-driven nature to prevent resistance.
- Ignoring conventional wisdom and implementing the decision without stakeholder buy-in.
- Presenting a compelling narrative backed by data, highlighting the evidence supporting the decision.
Convincing stakeholders requires presenting a compelling narrative supported by data. Emphasizing the evidence and reasoning behind the decision helps build confidence and trust in the data-driven approach, even if it challenges conventional wisdom.
In managing a data project, what is a 'data roadmap' and why is it important?
- It focuses on data storage infrastructure
- It is a strategy for data security implementation
- It is a visual representation of data flows within the organization
- It outlines the project timeline and milestones related to data initiatives
A data roadmap in data project management outlines the project timeline, milestones, and key activities related to data initiatives. It provides a strategic view, helping teams understand the sequence of tasks and dependencies. It is not specifically about data security or storage infrastructure.
If x = [10, 20, 30, 40, 50], what is the output of print(x[-2])?
- 20
- 30
- 40
- 50
The output is the element at the index -2 in the list, which is 40. Negative indexing counts elements from the end of the list.
The function ________ is used in R to create user-defined functions.
- create_function()
- define_function()
- function()
- user_function()
In R, the function() keyword is used to create user-defined functions. It is followed by a set of parentheses that can contain function arguments, and then the function body is enclosed in curly braces.
To synchronize a local repository with a remote repository in Git, the command is 'git _______.'
- fetch
- merge
- pull
- push
The 'git pull' command is used to synchronize a local repository with a remote repository in Git. It fetches changes from the remote repository and merges them into the current branch. 'Push' is used to upload local changes to the remote repository, 'fetch' retrieves changes without merging, and 'merge' combines branches.
What role does predictive analytics play in data-driven decision making?
- It analyzes current data to identify patterns and trends.
- It focuses on creating data visualizations to communicate insights.
- It involves testing hypotheses and drawing conclusions from data samples.
- It uses historical data and statistical algorithms to make predictions about future outcomes.
Predictive analytics plays a crucial role in data-driven decision making by utilizing historical data and statistical algorithms to make predictions about future outcomes. It enables organizations to anticipate trends, make proactive decisions, and optimize processes based on expected future scenarios.
_______ is a technique used to handle imbalanced datasets in predictive model training.
- K-Means Clustering
- Mean Imputation
- Principal Component Analysis
- SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is a technique used to handle imbalanced datasets in predictive model training. It generates synthetic samples for the minority class to balance the dataset and improve the model's performance on minority class instances.