By completing this course, learners will be able to apply Python programming to analyze datasets, construct compelling visualizations, evaluate statistical measures, and implement machine learning techniques to generate actionable insights. You will develop hands-on skills in Python scripting, create reusable libraries, build functions, and preprocess data for accurate analysis. Learners will also construct charts, scatter plots, histograms, and box plots, evaluate probabilities and hypotheses, and implement regression and optimization models using gradient descent.
Data Science with Python: Analyze & Visualize
您将学到什么
Analyze datasets with Python scripting, functions, and libraries.
Visualize data using charts, scatter plots, histograms, and box plots.
Apply ML techniques like regression and gradient descent models.
您将获得的技能
- Regression Analysis
- Statistical Methods
- Histogram
- Statistical Analysis
- Data Visualization Software
- Box Plots
- Data Preprocessing
- Scripting
- Probability & Statistics
- Machine Learning Algorithms
- Data Manipulation
- Data Analysis
- Scatter Plots
- Statistical Inference
- Data Science
- Python Programming
- Bayesian Statistics
- 技能部分已折叠。显示 9 项技能，共 17 项。
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作业
14 项作业
授课语言：英语（English）
最近已更新！
October 2025
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本课程是 Python for Data Science: Real Projects & Analytics 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
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该课程共有4个模块
This module introduces learners to the core principles of Python programming and its application in data science. Students will explore the Python environment, understand essential coding structures, and build reusable functions and libraries. By the end of this module, learners will have the programming foundation necessary to analyze, process, and manipulate data effectively.
涵盖的内容
7个视频3个作业
This module focuses on data visualization methods for effective data storytelling. Learners will develop skills in creating charts, graphs, and scatter plots while exploring the mathematical foundations of vector spaces and matrices. By mastering these visualization tools, students will be able to present data insights clearly and persuasively.
涵盖的内容
6个视频3个作业
This module provides a deep dive into the statistical foundations of data science. Learners will explore measures of central tendency, variability, probability, and hypothesis testing while addressing advanced concepts such as the Central Limit Theorem, Bayesian inference, and p-hacking. These skills prepare students to evaluate datasets critically and draw reliable conclusions.
涵盖的内容
11个视频4个作业
This module introduces learners to regression, optimization, and applied data analysis techniques. Students will implement gradient descent, preprocess datasets, and apply visual tools such as histograms, scatter plots, and box plots to extract insights. The module concludes with practical applications and a summary of the entire learning journey.
涵盖的内容
12个视频4个作业
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自 2018开始学习的学生
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自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
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学生评论
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显示 3/14 个
AR
已于 Jan 9, 2026审阅
SS
已于 Jan 7, 2026审阅
This course offers an excellent balance between Python programming, statistics, and machine learning. The real-world examples make the learning experience highly practical.
SS
已于 Jan 11, 2026审阅
This course offers an excellent balance between Python programming statistics, and machine learning. The real world examples make the learning experiences highly practical.
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