Mastering Python for Data Science

Mastering Python for Data Science

位教师：Packt - Course Instructors

访问权限由 New York State Department of Labor 提供

12个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

12个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 周完成

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

Manage data and perform linear algebra in Python
Derive inferences using inferential statistics
Create data visualizations and mine for patterns

您将获得的技能

您将学习的工具

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

12 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

该课程共有12个模块

Data Science has become one of the most sought-after fields in today’s data-driven world, and Python stands at its core. This course empowers learners to master the art of data science using Python—one of the most versatile programming languages for analyzing, visualizing, and interpreting data.

Through hands-on learning and guided projects, learners will gain the practical skills to clean, manipulate, and analyze data efficiently. The course also explores essential machine learning techniques and visualization tools, enabling professionals to uncover hidden patterns and derive actionable insights. Unlike typical theory-heavy programs, this course bridges the gap between academic knowledge and industry application. It combines conceptual clarity with real-world case studies that reflect how data science operates in business and technology environments. This course is ideal for Python developers, data analysts, and aspiring data scientists seeking to enhance their analytical capabilities. A basic understanding of Python and foundational data science concepts will help learners maximize the course benefits.

In this section, we explore parsing raw data from multiple sources, cleaning datasets, and manipulating data using NumPy and pandas for effective analysis.

涵盖的内容

2个视频5篇阅读材料1个作业

In this section, we explore probability distributions, hypothesis testing, confidence intervals, and errors to make population inferences from sample data using statistical methods.

涵盖的内容

1个视频6篇阅读材料1个作业

In this section, we explore structured data mining techniques, domain-driven analysis, and pattern discovery to uncover actionable insights for informed decision-making in real-world scenarios.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore techniques for controlling plot properties, combining multiple visualizations, and creating advanced data displays using Python. These methods enhance data communication and insight extraction.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore supervised, unsupervised, and reinforcement learning, focusing on their applications, key concepts like feature vectors, and practical problem-solving in data-driven systems.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore simple and multiple linear regression models, focusing on variable relationships, correlation coefficients, and model training for predictive analysis.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we build and evaluate logistic regression models using statsmodels and SciKit, focusing on predicting event likelihood with the Titanic dataset and assessing performance via ROC curves.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore user-based and item-based collaborative filtering techniques, focusing on calculating similarity using Euclidean distance and generating recommendations through weighted averages.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore random forest models for classification, analyze census data to predict income levels, and evaluate model performance using accuracy metrics.

涵盖的内容

1个视频2篇阅读材料1个作业

In this section, we explore k-means clustering for customer segmentation, focusing on determining optimal clusters and interpreting results for business insights.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we preprocess text data using NLTK, generate wordclouds, and apply tokenization, POS tagging, and named entity recognition to extract insights from unstructured data.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore Python's role in big data processing, focusing on Hadoop, MapReduce, and distributed computing techniques for efficient data analysis.