diff --git a/README.md b/README.md
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@@ -56,25 +56,25 @@ MATLAB®, Mapping Toolbox™ (StoringData.mlx), Symbolic Math Toolb
## [**Strings.mlx**](https://matlab.mathworks.com/open/github/v1?repo=MathWorks-Teaching-Resources/Programming-Organizing-Data&project=OrganizingData.prj&file=Strings.mlx)
| | **In this script, students will...** | **Summary** |
| :-- | :-- | :-- |
-| 
|
- investigate the relationship between character arrays and Unicode values
- try building two-dimensional character arrays and string arrays
- format strings by concatenating, modifying capitalization, reordering characters, extracting or replacing substrings, or formatting textual data from numerical data.
- extract the month from a regularly-formatted but randomly generated sentence | You can define, edit, format, and string together textual data for a computer. This script investigates strings, character arrays, and numeric data types and discusses how they interact in MATLAB. Formatting strings, modifying and extracting substrings, and building composite strings that include the results of numerical calculations are all explored. |
+| |
- investigate the relationship between character arrays and Unicode values
- try building two-dimensional character arrays and string arrays
- format strings by concatenating, modifying capitalization, reordering characters, extracting or replacing substrings, or formatting textual data from numerical data.
- extract the month from a regularly-formatted but randomly generated sentence | You can define, edit, format, and string together textual data for a computer. This script investigates strings, character arrays, and numeric data types and discusses how they interact in MATLAB. Formatting strings, modifying and extracting substrings, and building composite strings that include the results of numerical calculations are all explored. |
## [**NumericDataTypes.mlx**](https://matlab.mathworks.com/open/github/v1?repo=MathWorks-Teaching-Resources/Programming-Organizing-Data&project=OrganizingData.prj&file=NumericDataTypes.mlx)
| | **In this script, students will...** | **Summary** |
| :-- | :-- | :-- |
-| 
|
- create an example illustrating the differences between floating point arithmetic and symbolic arithmetic.
- explore the results of mathematical operations involving mixed numeric data types.
- identify the possible values of a 32-bit unsigned integer.
- use documentation to discover how to perform mathematical computations in MATLAB. | Different numeric data types are optimized for different operations. This script investigates the binary number system and how it leads to floating point, integer, and boolean data types. |
+| |
- create an example illustrating the differences between floating point arithmetic and symbolic arithmetic.
- explore the results of mathematical operations involving mixed numeric data types.
- identify the possible values of a 32-bit unsigned integer.
- use documentation to discover how to perform mathematical computations in MATLAB. | Different numeric data types are optimized for different operations. This script investigates the binary number system and how it leads to floating point, integer, and boolean data types. |
## [**Memory.mlx**](https://matlab.mathworks.com/open/github/v1?repo=MathWorks-Teaching-Resources/Programming-Organizing-Data&project=OrganizingData.prj&file=Memory.mlx)
| | **In this script, students will...** | **Summary** |
| :-- | :-- | :-- |
-| 
|
- compare the results of computation organized in memory-efficient and memory-inefficient ways.
- apply understanding of memory to recognize efficient computational patterns such as pre-allocation. | Understanding a little bit about how programs interact with memory can help you to write efficient programs or to understand why an algorithm might be much faster in some implementations than others. This script investigates high-level memory management and demonstrates how to write more memory-efficient code. |
+| |
- compare the results of computation organized in memory-efficient and memory-inefficient ways.
- apply understanding of memory to recognize efficient computational patterns such as pre-allocation. | Understanding a little bit about how programs interact with memory can help you to write efficient programs or to understand why an algorithm might be much faster in some implementations than others. This script investigates high-level memory management and demonstrates how to write more memory-efficient code. |
## [**StoringData.mlx**](https://matlab.mathworks.com/open/github/v1?repo=MathWorks-Teaching-Resources/Programming-Organizing-Data&project=OrganizingData.prj&file=StoringData.mlx)
| | **In this script, students will...** | **Summary** |
| :-- | :-- | :-- |
-| 
|
- read metadata to understand a dataset.
- use arrays to store multidimensional data of the same type and access individual elements or sub-arrays using dimensional, logical or linear indexing.
- use tables to store linked sets of different types of data such as key/value sets and extract information as tables, single variable arrays, or concatenated variable arrays.
- use structure arrays to store linked data of different types and sizes and use dot notation to access fields within a structure array or variables within a table.
- recognize cell arrays and extract the data to other data structures.
- clean data by interpolating missing values.
- answer questions by analyzing data.
- convert data between types and structures. | Different data storage structures are optimized for different types of data and access. This script facilitates the exploration of arrays, tables, structure arrays, and cell arrays as structures for storing data. Examples include sea surface temperature data and solar power generation data. |
+| |
- read metadata to understand a dataset.
- use arrays to store multidimensional data of the same type and access individual elements or sub-arrays using dimensional, logical or linear indexing.
- use tables to store linked sets of different types of data such as key/value sets and extract information as tables, single variable arrays, or concatenated variable arrays.
- use structure arrays to store linked data of different types and sizes and use dot notation to access fields within a structure array or variables within a table.
- recognize cell arrays and extract the data to other data structures.
- clean data by interpolating missing values.
- answer questions by analyzing data.
- convert data between types and structures. | Different data storage structures are optimized for different types of data and access. This script facilitates the exploration of arrays, tables, structure arrays, and cell arrays as structures for storing data. Examples include sea surface temperature data and solar power generation data. |
# License