What are some common descriptive analytics techniques and when to use them?

When analyzing frequency tables, it's important to consider conditional frequencies. This involves examining the occurrence of values or categories within specific subsets or conditions of the data. By doing so, you can gain insight into how certain factors influence the distribution of the data. For example, imagine you have sales data for a retail store that includes information about customers' age groups and their purchase amounts. Creating a frequency table that shows the distribution of purchase amounts for each age group can help identify which age group contributes the most to the store's revenue. You can also calculate conditional frequencies to compare the buying behaviors of different age groups for specific product categories.

For example, this helps us know the customer segments and their contribution to sales figures. Once we know that we can Pareto principle to focus on the most paying customers

???? Measures of central tendency help me by revealing the "typical" value in data. They summarize complex datasets into simple values, aiding quick understanding. These measures assist in data exploration, comparison, and outlier detection. They're like a compass in decision-making, estimating expected outcomes. ???? Also, they validate data quality and identify skewed distributions. Use them to communicate, model, and interpret data variability. ???? #CentralTendency #DataInsights #DecisionMaking

Here’s how a manager can use the measures of CT Mean: This helps in giving one single average value to the entire data set and helps the manager to make decisions without getting lost in details. For ex: Knowing the average will help you make assumptions of what products will sell in a particular geography. Median: This is the central value of the dataset. Once you know this you know who is falling short or exceedingly this value. For ex: with 8hr as median you know who is putting extra hours or who is spending less time in office. Mode: this is the most frequently occurring value in the data set. You may have products of different pricing but ppl buying Rs 250 tea coasters means that’s the most loved product

For example once you know how homogeneous the data is you can take general measures for good of all. But if your data is heterogeneous one size won’t fit all. Similarly you will know if things are too diverse it can be risky like stock market, while investing in other instruments may be more predictable.

Some useful Charts & Graphs: 1) Bar Chart: ?? Compares categorical data and changes over time. 2) Line Chart: ?? Shows trends in time series data. 3) Pie Chart: ?? Displays proportions or percentages 4) Scatter Plot: ?? Reveals correlations and relationships. 5) Histogram: ?? Visualizes data distribution and patterns. 6) Box Plot: ?? Highlights data spread and outliers. Heatmap: ??? Shows relationships in a matrix format. 7) Bubble Chart: ?? Displays three-variable relationships. 8) Gantt Chart: ?? Illustrates project timelines and tasks. 9) Radar Chart: ?? Compares multiple variables across categories. 10) Waterfall Chart: ?? Visualizes incremental changes. The best chart or graph depends on the story you want to tell. Use wisely !!

Summary statistics are the key for understanding geo coded air pollution, weather and cancer registry data altogether. It helps understand underlying phenomenon between study variables before predictive modeling. In my experience MEAN, MEDIAN, & SD helps understand general population characteristics while the 25th, 50th, and 75th percentile helps understand pollution and weather exposure levels. Mean, 25th, and 75th percentile also helps in understanding distance of monitoring stations to gauge the reliability of measured exposure values. E.g., I utilized these data from 1988-2015 and the summary stats helped me identify a racial group residence in non-metropolitan areas, compare survival based on summary percentile, combine var categories.

Some cases where I personally use Pivot Tables: 1) Summarizing Data: Pivot tables efficiently aggregate and summarize large datasets. ?? 2) Cross-Tabulation: They help in cross-tabulating data for comparisons across dimensions. ?? 3) Data Exploration: Pivot tables reveal trends, patterns, and correlations in data. ???? 4) Data Cleaning: They aid in identifying missing values and data quality issues. ???? 5) Report Generation: Pivot tables simplify report creation with visual summaries. ???? 6)Segmentation Analysis: Useful for analyzing data by different criteria, like demographics. ???? 7) Data Comparison: Pivot tables help compare data from various sources. ????

Based on my experience descriptive analytics uses techniques like mean, median, and mode ?? to find typical values, while measures like range and standard deviation show data spread. Histograms ?? display data distribution, percentiles address skewed data, and correlations ???? reveal relationships. Cross-tabulation ??, time series analysis ?, and scatter plots ?? offer insights into associations and trends. Visualizations like bar charts ??, pie charts ????, and heatmaps ??? enhance data understanding, and tools aid data profiling ???. Employ these techniques for informed decisions! ??????