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
Ce cours fait partie de Spécialisation Data Analysis with Python
Instructeur : Di Wu
Expérience recommandée
Ce que vous apprendrez
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.
Compétences que vous acquerrez
- Catégorie : Unsupervised Learning
- Catégorie : Machine Learning
- Catégorie : Supervised Learning
- Catégorie : Project Planning
- Catégorie : Data Mining
Détails à connaître
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Il y a 7 modules dans ce cours
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.
Inclus
1 lecture
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.
Inclus
1 lecture
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.
Inclus
1 lecture
This week introduces clustering techniques, including partitioning, hierarchical, density-based, and grid-based methods, for unsupervised pattern discovery.
Inclus
1 lecture
This week will focus on dimension reduction techniques, with a particular emphasis on Principal Component Analysis (PCA).
Inclus
1 lecture
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.
Inclus
1 lecture
This final week focuses on outlier detection methods, including Zscore, IQR, OneClassSVM, Isolation Forest, DBSCAN, LOF, and contextual outliers.
Inclus
2 lectures1 devoir1 sujet de discussion
Instructeur
Offert par
Recommandé si vous êtes intéressé(e) par Data Analysis
University of Colorado Boulder
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