University of Colorado Boulder
Data Analysis with Python Project
University of Colorado Boulder

Data Analysis with Python Project

This course is part of Data Analysis with Python Specialization

Taught in English

Di Wu

Instructor: Di Wu

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

18 hours (approximately)
Flexible schedule
Learn at your own pace

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.

Details to know

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Assessments

1 assignment

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

18 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Analysis with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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

Di Wu
University of Colorado Boulder
15 Courses29,139 learners

Offered by

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