本课程专为希望转入数据领域、但编程背景各异的学习者设计,旨在快速填补Python技能缺口。课程以行业需求为导向,系统教授Python核心语法及数据分析必备库(pandas, matplotlib, scipy, scikit-learn),涵盖数据清洗、可视化、统计分析及基础机器学习等核心技能。学员将学习如何用Python实现以往在R、SPSS等工具中熟悉的分析流程,并能自信地解释各类统计方法与机器学习技术的实际应用场景。通过本速成课,学员将掌握雇主期待的核心Python数据分析能力,为胜任数据相关岗位打下坚实基础。
制作:Stat Hacks
MP4 | 视频:h264,1920×1080 | 语言:英语 | 时长:14节课(3小时8分钟) | 文件大小:1.43 GB

Python for Data Analysis in Industry: A Crash Course
Learn to use Python for Data Exploration, Visualisation, Statistical Analysis, and Machine Learning
What you’ll learn
✓ Learn the basics of Python, the most common programming language employers expect you to know
✓ Learn how to conduct data manipulation, data visualisation, and data analysis in Python
✓ Learn the famous Python Libraries used for Data Analysis (pandas, matplotlib, scipy, sckit learn)
✓ Be confident in explaining basic use cases of statistical methods and machine learning techniques
Requirements
● Basic knowledge of Data Analysis
Description
For many people trying to pivot into a Data role, Python may not be your first programming language. You might be more familiar with R, or have little programming experience at all, having worked primarily with tools like SPSS or Jamovi. However, Python is the most commonly expected language in industry, and is a core skill employers look for when hiring for Data roles.
This course is designed to bridge that gap. It teaches you how to perform the data manipulation and analysis you may already be familiar with—just now, in Python. Beyond that, it introduces a wide range of data analysis tools and concepts that employers expect candidates to understand when applying for Data positions.
You don’t need to be an expert (yet). But you do need to be able to speak confidently about fundamental visualisation techniques, statistical methods, and basic Machine Learning algorithms—and understand how and when they are used in practice.
As an industry-focused crash course, this programme equips you with essential Python-based data skills that employers expect to see in candidates. By the end of the course, you will be able to confidently discuss your analytical approach, demonstrate practical Python workflows, and show that you are ready to contribute effectively in a Data role.
Course Image by Jan from Pixabay – many thanks to you!
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