# Data Analysis in MATLAB

### About Data Analysis in MATLAB

Module Structure

Data Handling in MATLAB

Learn how to Import data interactively into MATLAB using a system clipboard. Following ways, we can use to import data interactively. 1) Click on the import data tab in the variable section of the Home tab. 2) By choosing a file from the directory. 3) uiimport command will open a dialog box in the MATLAB environment.

Importing data Programmatically in MATLAB

Learn to import different files and data into MATLAB using programming.

Exporting Data from MATLAB

Learn to export different data into files from MATLAB using programming.

Organization of data in MATLAB

Standard arrays represent valuable data structures for storing objects. Arrays cannot be used to memorize both numbers and strings in the same object. We can solve this issue by using cell arrays and structure arrays.

Data Preparation & Preprocessing

Data Accessing and Removing in MATLAB

Learn how to access, replace and remove any particular attribute or any observation from the table.

Exploratory Data Analysis

Exploratory Data Analysis will provide a systematic approach to extract information or summarize critical data characteristics.

Central Tendency of Data Distribution

Learn how to use Mean, median, and mode terms for getting information about the tendency of data distribution.

Measure of Dispersion

Dispersion measures provide information on the spread of data around the middle. In this section, we can evaluate dispersion measurements with the aid of variance and standard deviation terminology.

Skewness and Kurtosis

Skewness tells us the skewed magnitude and direction of the data set. Compared to a regular bell curve, Kurtosis tells us how high and sharp the central peak is.

Graphical Representation of the Data

Learn the graphical way to get insight from the data. A graph is intended for the rapid visualization of a data set.

Operations on Outliers

An outlier is a piece of data that is abnormally distant from other points. In other words, this is the data that lies outside the other values in the data set. So, making a machine learning model without removing the outlier value makes the model a bias. Essential steps are needed to make the model unbiased to remove outliers from the data set. By deleting or replacing the outlier's value, we can vanish outliers from the data. However, it is not recommended to delete any information from the data method.

Operations on Categorical Data

Most of the practical data sets are categorical. We cannot directly apply categorical data to modeling. Most machine learning algorithms allow only numeric data to be used while modeling. So, to make the categorical data acceptable to ML, we have to perform some operations on the categorical data.

Feature Scaling

Feature scaling is one of the steps we have to follow before starting the actual machine learning model to make data well. With the help of feature scaling methods, we scale input data to the same level. Thus, different ranges and units of measurement problems of input features are avoided.

Transformation Techniques & Feature Selection

Transformation Techniques

Transformation and Feature Selection Techniques play an essential role in improving the accuracy of the model. Both techniques are not necessary, and our model could still run without them. But the use of these techniques may make it possible for the model to produce better results. In this lesson, we focus on when and how to use transformation techniques.

Feature Selection Techniques

Machine learning feature selection aims to find the best collection of features to create an efficient model from the data collected. It is almost rare that all the variables or features in the dataset are helpful when creating a machine learning model in real life. Adding unnecessary variables decreases the model's potential and may also decrease the model's overall precision. Adding more and more variables or attributes to the model often increases the model's overall complexity.

### Data Analysis in MATLAB

April 28, 2021

Data Handling in MATLAB

4 Lessons

#### Importing Data Interactively into MATLAB

Learn how to Import data interactively into MATLAB using a system clipboard. Following ways, we can use to import data interactively. 1) Click on the import data tab in the variable section of the Home tab. 2) By choosing a file from the directory. 3) uiimport command will open a dialog box in the MATLAB environment.

Paid Access

#### Importing data Programmatically in MATLAB

Learn to import different files and data into MATLAB using programming.

Paid Access

#### Exporting Data from MATLAB

Learn to export different data into files from MATLAB using programming.

Paid Access

#### Organization of data in MATLAB

Standard arrays represent valuable data structures for storing objects. Arrays cannot be used to memorize both numbers and strings in the same object. We can solve this issue by using cell arrays and structure arrays.

Data Preparation & Preprocessing

9 Lessons

Paid Access

#### Data Accessing and Removing in MATLAB

Learn how to access, replace and remove any particular attribute or any observation from the table.

Paid Access

#### Exploratory Data Analysis

Exploratory Data Analysis will provide a systematic approach to extract information or summarize critical data characteristics.

Paid Access

#### Central Tendency of Data Distribution

Learn how to use Mean, median, and mode terms for getting information about the tendency of data distribution.

Paid Access

#### Measure of Dispersion

Dispersion measures provide information on the spread of data around the middle. In this section, we can evaluate dispersion measurements with the aid of variance and standard deviation terminology.

Paid Access

#### Skewness and Kurtosis

Skewness tells us the skewed magnitude and direction of the data set. Compared to a regular bell curve, Kurtosis tells us how high and sharp the central peak is.

Paid Access

#### Graphical Representation of the Data

Learn the graphical way to get insight from the data. A graph is intended for the rapid visualization of a data set.

Paid Access

#### Operations on Outliers

An outlier is a piece of data that is abnormally distant from other points. In other words, this is the data that lies outside the other values in the data set. So, making a machine learning model without removing the outlier value makes the model a bias. Essential steps are needed to make the model unbiased to remove outliers from the data set. By deleting or replacing the outlier's value, we can vanish outliers from the data. However, it is not recommended to delete any information from the data method.

Paid Access

#### Operations on Categorical Data

Most of the practical data sets are categorical. We cannot directly apply categorical data to modeling. Most machine learning algorithms allow only numeric data to be used while modeling. So, to make the categorical data acceptable to ML, we have to perform some operations on the categorical data.

Paid Access

#### Feature Scaling

Feature scaling is one of the steps we have to follow before starting the actual machine learning model to make data well. With the help of feature scaling methods, we scale input data to the same level. Thus, different ranges and units of measurement problems of input features are avoided.

Transformation Techniques & Feature Selection

2 Lessons

Paid Access

#### Transformation Techniques

Transformation and Feature Selection Techniques play an essential role in improving the accuracy of the model. Both techniques are not necessary, and our model could still run without them. But the use of these techniques may make it possible for the model to produce better results. In this lesson, we focus on when and how to use transformation techniques.

Paid Access

#### Feature Selection Techniques

Machine learning feature selection aims to find the best collection of features to create an efficient model from the data collected. It is almost rare that all the variables or features in the dataset are helpful when creating a machine learning model in real life. Adding unnecessary variables decreases the model's potential and may also decrease the model's overall precision. Adding more and more variables or attributes to the model often increases the model's overall complexity.