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Data Analysis with Python

Di Wu

The Data Analysis specialization will provide a comprehensive overview of various techniques for analyzing data. The courses will cover a wide range of topics, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules. The courses will be very hands-on and will include real-life examples and case studies, which will help students develop a deeper understanding of Data Analysis concepts and techniques. The courses will culminate in a project that demonstrates the student's mastery of Data Analysis techniques.

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What's inside

Five courses

Classification Analysis

(0 hours)

The "Classification Analysis" course provides a comprehensive understanding of classification, a fundamental supervised learning method. You will explore various classifiers, including KNN, decision tree, support vector machine, naive Bayes, and logistic regression, and learn how to evaluate their performance. Through tutorials and case studies, you will gain hands-on experience in applying classification techniques to real-world data analysis tasks.

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Regression Analysis

(0 hours)

The "Regression Analysis" course covers the fundamentals of regression, a key supervised learning method. Students will learn various regression techniques, including linear, polynomial, and regularized regression. They will also explore cross-validation and ensemble methods. Through hands-on tutorials and case studies, students will gain practical experience in applying regression analysis to real-world data.

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Clustering Analysis

(0 hours)

The "Clustering Analysis" course introduces students to the fundamental concepts of unsupervised learning, focusing on clustering and dimension reduction techniques. Participants will explore various clustering methods, including partitioning, hierarchical, density-based, and grid-based clustering. Additionally, students will learn about Principal Component Analysis (PCA) for dimension reduction.

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Association Rules Analysis

(0 hours)

The "Association Rules and Outliers Analysis" course introduces students to unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining.

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Data Analysis with Python Project

(0 hours)

The "Data Analysis Project" course empowers students to apply their knowledge and skills in data analysis to conduct a real-life project. Participants will explore supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. By completing the course, students will be proficient in data analysis and capable of making data-driven decisions.

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Learning objectives

Describe and define the fundamental concepts and techniques used in data analysis. identify the appropriate techniques to apply.
Compare and contrast different data analysis techniques, including classification, regression, clustering, dimension reduction, and association rules

Design and implement effective data analysis workflows, including data preprocessing, feature selection, and model selection

Describe and define the fundamental concepts and techniques used in data analysis. identify the appropriate techniques to apply.
Compare and contrast different data analysis techniques, including classification, regression, clustering, dimension reduction, and association rules
Design and implement effective data analysis workflows, including data preprocessing, feature selection, and model selection

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Time

2 hours

Level

Intermediate

Via

Coursera

Institution

University of Colorado Boulder

Instructor

Di Wu

Level

Intermediate

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