Data Processing and Feature Engineering with MATLAB
This intermediate-level course combines data from multiple sources and times to lay the foundation for predictive modeling. MATLAB is used to process and engineer features, providing a useful tool for anyone interested in data analysis. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
15th May, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
Data Processing and Feature Engineering with MATLAB is an intermediate-level course designed to help those with domain knowledge and some exposure to computational tools, but no programming background, build on the skills learned in Exploratory Data Analysis with MATLAB. Through this course, participants will learn how to merge data from different data sets, handle common scenarios such as missing data, and explore special techniques for handling textual, audio, and image data. By the end of the course, participants will be able to visualize their data, clean it up and arrange it for analysis, and identify the qualities necessary to answer their questions.
[Applications]
Upon completion of this course, participants will be able to apply the skills learned to combine data from multiple sources, handle missing data, and explore special techniques for handling textual, audio, and image data. They will be able to visualize their data, clean it up and arrange it for analysis, and identify the qualities necessary to answer their questions. Participants will also be able to visualize the distribution of their data and use visual inspection to address artifacts that affect accurate modeling.
[Career Paths]
1. Data Scientist: Data Scientists are responsible for analyzing large amounts of data and developing predictive models to help organizations make better decisions. They use a variety of tools and techniques, including MATLAB, to explore and analyze data, identify patterns, and develop models. Data Scientists are in high demand as organizations increasingly rely on data-driven decision making.
2. Data Analyst: Data Analysts are responsible for collecting, organizing, and analyzing data to help organizations make informed decisions. They use MATLAB to explore and analyze data, identify patterns, and develop insights. Data Analysts are in high demand as organizations increasingly rely on data-driven decision making.
3. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models to solve real-world problems. They use MATLAB to explore and analyze data, identify patterns, and develop models. Machine Learning Engineers are in high demand as organizations increasingly rely on data-driven decision making.
4. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines and data warehouses. They use MATLAB to explore and analyze data, identify patterns, and develop models. Data Engineers are in high demand as organizations increasingly rely on data-driven decision making.
[Education Paths]
1. Bachelor of Science in Data Science: This degree program provides students with the skills and knowledge to analyze and interpret data, develop predictive models, and create data-driven solutions. Students will learn the fundamentals of data science, including data mining, machine learning, and artificial intelligence. They will also gain experience in programming languages such as Python, R, and MATLAB. This degree is becoming increasingly popular as businesses and organizations rely more heavily on data-driven decision making.
2. Master of Science in Data Science: This degree program builds on the skills and knowledge acquired in a Bachelor of Science in Data Science. Students will learn more advanced techniques for data analysis, such as natural language processing, deep learning, and big data analytics. They will also gain experience in more advanced programming languages such as Java and Scala. This degree is ideal for those who want to pursue a career in data science or research.
3. Doctor of Philosophy in Data Science: This degree program is designed for those who want to pursue a career in research or academia. Students will learn advanced techniques for data analysis, such as Bayesian inference, statistical modeling, and machine learning. They will also gain experience in programming languages such as Python, R, and MATLAB. This degree is ideal for those who want to pursue a career in data science research or teaching.
4. Certificate in Data Science: This certificate program provides students with the skills and knowledge to analyze and interpret data, develop predictive models, and create data-driven solutions. Students will learn the fundamentals of data science, including data mining, machine learning, and artificial intelligence. They will also gain experience in programming languages such as Python, R, and MATLAB. This certificate is ideal for those who want to gain a basic understanding of data science and its applications.
Pros & Cons
Topnotch content and delivery
Practicals, quizzes and exams
Variety of data type
Unique programming mindset and skills
Advanced uses of Live Editor & Machine Learning apps
Clear and concise manner
Hands on experience
Week 5 was too rushed
Fast pace
Not enough interactive assignments
Difficult to follow
Video lectures not in depth
Course Provider
Provider Coursera's Stats at AZClass
This intermediate course combines data from multiple sources and time to provide a foundation for predictive modeling. MATLAB is used to process and design features, providing a useful tool for anyone interested in data analysis. In this course, you'll build on the skills you learn in MATLAB Exploratory Data Analysis to build on the foundation for predictive modeling. This intermediate course is useful for anyone who needs to combine data from multiple sources or time and is interested in modeling. These skills are valuable for those who have domain knowledge and exposure to some computing tools but no programming background.
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