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Data Science with Python: Analyze & Visualize

本课程是 Python for Data Science: Real Projects & Analytics 专项课程的一部分

位教师：EDUCBA

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

15 条评论

9 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

15 条评论

9 小时完成

灵活的计划

自行安排学习进度

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

您将学习的工具

Python Programming

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

14 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

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。

向行业专家学习新概念
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
获得可共享的职业证书

该课程共有4个模块

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.

This course benefits anyone aiming to advance a career in data science, analytics, or business intelligence, providing practical, project-based learning experiences. Unlike generic tutorials, this program integrates Python foundations with real-world statistical methods, Bayesian inference, and applied machine learning workflows. The structured approach—spanning Python basics to advanced analysis—ensures learners can confidently interpret data, validate assumptions, and present findings with clarity.

单元详情

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

7个视频总计37分钟

Introduction to Data Visualization4分钟
Understanding Data Science5分钟
Python Environment Framework4分钟
Various Python Scripts7分钟
Concept of Advanced Python7分钟
Creating Functions for Python5分钟
Creating a New Library5分钟

3个作业总计50分钟

Getting Started with Data Science10分钟
Mastering Python Essentials10分钟
Graded-Python Foundations for Data Science30分钟

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

11个视频总计71分钟

Understanding Central Tendencies11分钟
Dispersion for Data6分钟
Probability in Discreet Mathematics3分钟
Normal Distribution Curve7分钟
Example for Normal Distribution Curve5分钟
Central Limit Theorem7分钟
Concept of Hypothesis3分钟
Example on Hypothesis Testing8分钟
Defining the Next Value9分钟
Principle of P Hacking5分钟
Understanding Bayesian Inference6分钟

4个作业总计60分钟

Exploring Statistical Foundations10分钟
Distribution and Hypothesis Testing10分钟
Advanced Statistical Insights10分钟
Graded-Statistics and Probability in Action30分钟

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.