The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions.
Data Analysis with Python Project
This course is part of Data Analysis with Python Specialization
Instructor: Di Wu
Included with
Recommended experience
What you'll learn
Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.
Apply various classification and regression algorithms and implement cross-validation and ensemble techniques to enhance the performance of models.
Apply various clustering, dimension reduction association rule mining, and outlier detection algorithms for unsupervised learning models.
Skills you'll gain
Details to know
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There are 7 modules in this course
In this first week, you will gain an overview of data analysis, understanding supervised and unsupervised learning directions. You will learn how to define the scope and direction of their data analysis project effectively.
What's included
1 reading
This week focuses on classification techniques, where you will explore Nearest Neighbors, Decision Trees, SVM, Naive Bayes, Logistic Regression, cross-validation, ensemble methods, and evaluation metrics.
What's included
1 reading
This week you will delve into regression techniques, including Simple Linear, Polynomial Linear, Linear with regularization, multivariate regression, cross-validation, ensemble methods, and evaluation metrics.
What's included
1 reading
This week introduces clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, for unsupervised pattern discovery.
What's included
1 reading
This week will focus on dimension reduction techniques, with a particular emphasis on Principal Component Analysis (PCA).
What's included
1 reading
This week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.
What's included
1 reading
This final week focuses on outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, LOF, and contextual outliers.
What's included
2 readings1 assignment1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
Universidad Nacional Autónoma de México
Coursera Project Network
University of California San Diego
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Frequently asked questions
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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