Python Data Analysis

Python Data Analysis

位教师：Packt - Course Instructors

访问权限由 Coursera Learning Team 提供

12个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

3 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。

初级等级

推荐体验

3 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

Install and use open source Python modules across different platforms
Master the fundamentals of NumPy for efficient data manipulation
Analyze and process data using pandas and other key Python libraries

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您将学习的工具

要了解的详细信息

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作业

12 项作业

授课语言：英语（English）

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该课程共有12个模块

This course introduces the core concepts of data analysis using Python, equipping learners with essential skills to perform data manipulation and analysis. Data analysis is a crucial skill in today's tech-driven world, and this course empowers professionals with the tools they need to extract insights from data.

Throughout this course, you will gain hands-on experience using key Python libraries such as NumPy and pandas, making complex data analysis tasks accessible. You’ll learn how to clean, process, and analyze data sets, setting you up for success in real-world data projects. What sets this course apart is its combination of theory and practice, using real-world examples to guide you. You'll not only learn the fundamental concepts but also how to apply them to solve actual data problems. This course is perfect for programmers, scientists, and engineers with a basic understanding of Python and data science concepts. If you're looking to expand your data analysis skills with Python, this course is designed for you.

In this section, we install Python and core libraries on multiple OSs, implement NumPy arrays for numerical tasks, and use IPython for interactive sessions.

涵盖的内容

2个视频4篇阅读材料1个作业

In this section, we explore NumPy arrays, their data types, and operations like indexing and shaping. Key concepts include array creation, manipulation, and efficient numerical computing techniques.

涵盖的内容

1个视频6篇阅读材料1个作业

In this section, we explore descriptive statistics, linear algebra operations, and random number generation using NumPy and SciPy for data analysis and modeling.

涵盖的内容

1个视频6篇阅读材料1个作业

1个视频总计1分钟

Statistics and Linear Algebra - Overview Video1分钟

6篇阅读材料总计120分钟

Introduction20分钟
Basic Descriptive Statistics with NumPy30分钟
Solving Linear Systems with NumPy20分钟
NumPy Random Numbers20分钟
Performing a Normality Test with SciPy10分钟
Creating a NumPy-Masked Array20分钟

1个作业总计10分钟

Foundations of Statistics and Linear Algebra10分钟

In this section, we explore pandas installation, DataFrame data structures, and perform data querying, aggregation, and analysis.

涵盖的内容

1个视频8篇阅读材料1个作业

1个视频总计1分钟

Pandas Primer - Overview Video1分钟

8篇阅读材料总计210分钟

Introduction30分钟
Pandas Series20分钟
Querying Data in Pandas30分钟
Statistics with pandas DataFrames30分钟
Concatenating and Appending DataFrames30分钟
Handling Missing Values20分钟
Dealing with Dates30分钟
Pivot Tables20分钟

1个作业总计10分钟

Pandas Fundamentals and Data Manipulation10分钟

In this section, we explore writing CSV files with NumPy and pandas, analyzing binary formats like .npy and pickle, and storing data using PyTables and HDF5 for efficient data management.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore data visualization techniques using matplotlib and pandas, focusing on basic plots and subpackage functionality.

涵盖的内容

1个视频4篇阅读材料1个作业

In this section, we explore time series analysis techniques including moving averages, autocorrelation, and Fourier analysis. These methods enable modeling and forecasting of sequential data using statistical and signal processing tools.

涵盖的内容

1个视频5篇阅读材料1个作业

In this section, we explore relational and NoSQL databases, focusing on SQLite3, SQLAlchemy, and PyMongo/MongoDB.

涵盖的内容

1个视频5篇阅读材料1个作业

In this section, we explore text analysis using NLTK, covering word frequency, sentiment analysis, classification, and visualization techniques for unstructured data.

涵盖的内容

1个视频5篇阅读材料1个作业

In this section, we explore scikit-learn for predictive analytics, focusing on logistic regression, preprocessing, and classification techniques to enable data-driven decision-making.