University of Colorado Boulder
Data Analysis with Python 专项课程
University of Colorado Boulder

Data Analysis with Python 专项课程

Launch your career in Data Science & Data Analysis. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis.

Di Wu

位教师：Di Wu

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深入学习学科知识
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2 月 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入学习学科知识
4.8

（15 条评论）

中级 等级

推荐体验

2 月 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • 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

您将获得的技能

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授课语言：英语（English）

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专业化 - 5门课程系列

Classification Analysis

Classification Analysis

第 1 门课程38小时

您将学到什么

  • Understand the concept and significance of classification as a supervised learning method.

  • Identify and describe different classifiers, apply each classifier to perform binary and multiclass classification tasks on diverse datasets.

  • Evaluate the performance of classifiers, select and fine-tune classifiers based on dataset characteristics and learning requirements.

您将获得的技能

类别：Machine Learning Algorithms
类别：Classification And Regression Tree (CART)
类别：Machine Learning
类别：Bayesian Statistics
类别：Probability & Statistics
类别：Supervised Learning
类别：Feature Engineering
类别：Predictive Modeling
类别：Data Analysis
类别：Data Science
类别：Data Mining
Regression Analysis

Regression Analysis

第 2 门课程40小时

您将学到什么

  • Understand the principles and significance of regression analysis in supervised learning.

  • Implement cross-validation methods to assess model performance and optimize hyperparameters.

  • Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.

您将获得的技能

类别：Regression Analysis
类别：Machine Learning Methods
类别：Predictive Modeling
类别：Data Analysis
类别：Feature Engineering
类别：Verification And Validation
类别：Supervised Learning
类别：Statistical Analysis
类别：Exploratory Data Analysis
类别：Statistical Modeling
Clustering Analysis

Clustering Analysis

第 3 门课程37小时

您将学到什么

  • Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.

  • Apply clustering techniques to diverse datasets for pattern discovery and data exploration.

  • Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.

您将获得的技能

类别：Unsupervised Learning
类别：Dimensionality Reduction
类别：Machine Learning
类别：Machine Learning Methods
类别：Machine Learning Algorithms
类别：Exploratory Data Analysis
类别：Applied Machine Learning
类别：Statistical Machine Learning
类别：Data Analysis
类别：Data Mining
Association Rules Analysis

Association Rules Analysis

第 4 门课程22小时

您将学到什么

  • Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection

  • Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.

  • Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

您将获得的技能

类别：Anomaly Detection
类别：Unsupervised Learning
类别：Algorithms
类别：Data Mining
类别：Data Analysis
类别：Data Manipulation
类别：Applied Machine Learning
类别：Feature Engineering
类别：Exploratory Data Analysis
Data Analysis with Python Project

Data Analysis with Python Project

第 5 门课程18小时

您将学到什么

  • 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.

您将获得的技能

类别：Anomaly Detection
类别：Scikit Learn (Machine Learning Library)
类别：Regression Analysis
类别：Supervised Learning
类别：Data Mining
类别：Unsupervised Learning
类别：Dimensionality Reduction
类别：Classification And Regression Tree (CART)
类别：Analytics
类别：Exploratory Data Analysis
类别：Machine Learning Algorithms
类别：Predictive Modeling
类别：Statistical Analysis
类别：Data Analysis
类别：Machine Learning
类别：Project Planning

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位教师

Di Wu
University of Colorado Boulder
21 门课程53,729 名学生

提供方

University of Colorado Boulder

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