In a healthcare analytics dashboard, a Choropleth map can be used to visualize the geographical distribution of patient data. Choropleth maps use color variations to represent values across geographic regions, making them ideal for displaying spatial patterns in data.

Key Performance Indicators (KPIs) are crucial in dashboard design for enabling users to focus on key metrics at a glance. KPIs provide a quick overview of important measures, allowing users to assess performance without delving into detailed reports.

Time Series Analysis is commonly used in data mining to predict future trends based on historical data. It involves analyzing and modeling data points over time to identify patterns and make predictions. Dimensionality Reduction, Association Rule Mining, and Support Vector Machines serve different purposes in data mining.

Time series analysis would be a suitable approach for predicting stock market trends. Stock prices exhibit temporal patterns, and time series models, such as ARIMA or LSTM, can capture these patterns effectively. K-Nearest Neighbors, principal component analysis, and random forest are not specifically designed for time-dependent data like stock prices.

The correct option is a) Create a new branch for each feature. This allows you to isolate and track changes related to experimental features without affecting the main codebase. Options b, c, and d are not recommended for managing experimental features in Git.

A Stacked Area chart is often used to display changes over time for two or more related groups that make up one whole category. It allows for easy comparison of the overall trend as well as the contribution of each group to the whole.

The Quick Sort algorithm is used for sorting elements. It is highly efficient for large datasets due to its divide-and-conquer approach, which minimizes the number of comparisons needed. Merge Sort also uses a divide-and-conquer approach, but Quick Sort is known for its efficiency in practice.

In Excel, Pivot tables are used to analyze and summarize data sets. They provide a dynamic way to organize and present information, making it easier to draw insights from large datasets.

Heatmaps are effective for presenting complex relationships, making them suitable for displaying patient demographic data alongside treatment outcomes. Stacked Bar Charts and Scatter Plots may not provide the same level of clarity in this scenario, and Dual-Axis Charts are generally used for comparing two different scales.

For C-level executives, dashboards should focus on presenting data at an appropriate level of granularity, ensuring relevance to strategic decisions. While aesthetics are important, the granularity and relevance of data contribute significantly to effective decision-making.