Data Processing and Feature Engineering with MATLAB

Go to Course: https://www.coursera.org/learn/feature-engineering-matlab

Introduction

## Course Review: Data Processing and Feature Engineering with MATLAB If you're looking to bolster your data science skills, Coursera's **Data Processing and Feature Engineering with MATLAB** is an excellent choice. This intermediate-level course is expertly tailored for individuals who have domain knowledge and some familiarity with computational tools but may lack formal programming experience. By the end of the course, you’ll build a strong foundation in predictive modeling and data preprocessing, critical skills in today’s data-driven world. ### Course Overview The course builds upon skills acquired in the **Exploratory Data Analysis (EDA) with MATLAB** course, allowing participants to effectively handle the complexities of data processing and feature engineering. The course is particularly appealing to those who work with diverse datasets and are tasked with extracting meaningful insights through predictive analytics. ### Why Take This Course? - **Practical Focus**: The course emphasizes hands-on experience with MATLAB, a powerful platform for data analysis. You'll work with real datasets, which enables you to apply the concepts learned in a practical context. - **Comprehensive Syllabus**: The course is structured into several key modules, each focusing on different aspects of data processing, from surveying data distributions to domain-specific feature engineering. - **No Programming Required**: For those without a background in programming, this course is perfect. It breaks down complex processes into manageable steps, making it accessible while still providing substantial content of value. ### Module Breakdown 1. **Surveying Your Data**: - You’ll revisit the data exploration techniques from the previous course, but with a fresh dataset. This module includes various types of distributions and calculations of statistical measures like skewness and interquartile range. The introduction of plotting multi-dimensional data expands visualization capabilities. 2. **Organizing Your Data**: - Data isn't always tidy, and understanding how to manipulate it is crucial. Here, you will learn to clean and combine data from multiple sources, a skill that is routinely required in real-world data projects. 3. **Cleaning Your Data**: - Messy data can hide insights, and this module teaches you how to identify and rectify issues such as missing values and outliers. Normalizing variables to handle discrepancies in scale is also covered, providing you with tools to analyze data effectively. 4. **Finding Features that Matter**: - The art and science of feature engineering are explored in detail. You’ll learn to create new features that enhance your dataset’s predictive power, ensuring you can scrutinize which features hold significant value for your modeling needs. 5. **Domain-Specific Feature Engineering**: - The final module is particularly exciting as it applies the techniques you’ve learned in varied contexts. You’ll use accelerometer data, conduct image processing, and delve into text analysis—demonstrating the versatility of MATLAB across different domains. ### Recommendation I highly recommend **Data Processing and Feature Engineering with MATLAB** for anyone looking to deepen their understanding of data preparation and feature engineering. The course is well-structured, informative, and practical, providing participants with the essential skills to excel in predictive modeling. ### Conclusion In a world increasingly dominated by data, the ability to preprocess and engineer features is invaluable. This course not only equips you with these crucial skills but does so in an approachable manner that doesn’t require programming expertise. Whether you're a data analyst, a business professional, or someone simply looking to transition into the field of data science, this course will serve as an excellent stepping stone in your journey. Visit Coursera to enroll and take the first step toward transforming the way you work with data!

Syllabus

Surveying Your Data

In this module you'll apply the skills gained in Exploratory Data Analysis with MATLAB on a new dataset. You'll explore different types of distributions and calculate quantities like the skewness and interquartile range. You'll also learn about more types of plots for visualizing multi-dimensional data.

In this module you'll learn to prepare data for analysis. Often data is not recorded as required. You'll learn to manipulate string variables to extract key information. You'll create a single datetime variable from date and time information spread across multiple columns in a table. You'll efficiently load and combine data from multiple files to create a final table for analysis.

In this module you'll clean messy data. Missing data, outliers, and variables with very different scales can obscure trends in the data. You'll find and address missing data and outliers in a data set. You'll compare variables with different scales by normalizing variables.

In this module you'll create new features to better understand your data. You'll evaluate features to determine if a feature is potentially useful for making predictions.

In this module you'll apply the concepts from Modules 1 through 4 to different domains. You'll create and evaluate features using time-based signals such as accelerometer data from a cell phone. You'll use Apps in MATLAB to perform image processing and create features based on segmented images. You'll also use text processing techniques to find features in unstructured text.