The Pandas function _______ is essential for reshaping data from wide format to long format.
- melt()
- pivot_long()
- reshape()
- wide_to_long()
The melt() function in Pandas is essential for reshaping data from wide format to long format. It unpivots the data, making it suitable for various analyses and visualizations.
For a company observing seasonal sales patterns, which time series model would be best suited to forecast future sales?
- Autoregressive Integrated Moving Average (ARIMA)
- Exponential Smoothing State Space Models (ETS)
- Long Short-Term Memory (LSTM) Networks
- Seasonal Decomposition of Time Series (STL)
Seasonal Decomposition of Time Series (STL) is well-suited for capturing and forecasting seasonal patterns in time series data. It decomposes the time series into components such as trend, seasonality, and remainder. Other models like ARIMA, ETS, and LSTMs may be used for different patterns but STL is specifically designed for seasonality.
For a sales dashboard, what type of visualization is typically used to represent sales trends over time?
- Line Chart
- Pie Chart
- Radar Chart
- Scatter Plot
A line chart is typically used in a sales dashboard to represent sales trends over time. Line charts are effective for showing the progression of sales data, making it easy to identify patterns, fluctuations, and overall trends.
How is 'Cost Per Acquisition' (CPA) significant in marketing performance analysis?
- CPA measures the cost of acquiring new customers through marketing efforts.
- CPA determines the overall marketing budget for a campaign.
- CPA assesses the total revenue generated by a marketing campaign.
- CPA evaluates the brand awareness created by marketing activities.
Cost Per Acquisition (CPA) is significant in marketing performance analysis as it measures the cost incurred to acquire a new customer through marketing efforts. It helps assess the efficiency and effectiveness of marketing campaigns in acquiring valuable customers. The other options are not accurate descriptions of CPA.
To extract data from a website, a scraper typically parses the website's ________ structure.
- CSS
- Database
- HTML
- JavaScript
A scraper typically parses the website's HTML structure to extract data. HTML (Hypertext Markup Language) defines the structure of web pages, and parsing it allows the scraper to locate and extract the relevant information.
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.