- Corrected exercises on exploratory data analysis
- Normalize / Standardize / Resize your Data
- How to handle missing data
- Tutorial on best practices in exploratory data analysis
- Tutorial on Sweetviz (full data analysis)
- Tutorial on using geometric and harmonic mean
- Tutorial on semi-automatic data analysis
- Social Media Comment Analysis Tutorial

Descriptive analysis provides simple summaries about the sample and the observations that were made. These summaries can be either quantitative, i.e. summary statistics, or visual, i.e. easy-to-understand graphs. These summaries can either form the basis of the initial description of the data for further statistical analysis, or stand on their own for a particular survey.

For example, basketball shooting percentage is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots taken divided by the number of shots. For example, a player who shoots 33 % makes about one in three shots. The percentage summarizes or describes several discrete events. Also consider the cumulative grade point average. This single number describes a student's overall performance across all of their course experiences.

The use of descriptive and summary statistics has a long history, and indeed the simple tabulation of populations and economic data was the first way the subject of statistics arose. More recently, a set of synthesis techniques have been formulated under the title of exploratory data analysis: an example of such a technique is the box plot.

In the business world, descriptive statistics provide a useful summary of many types of data. For example, investors and brokers can use a historical account of performance behavior by performing empirical and analytical analysis on their investments to make better investment decisions in the future.