Data Processing Using Python from Coursera

This course (The English copy of "用Python玩转数据" ) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level.

This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance, and robustness of Python. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields.

The course has been updated. Updates in the new version are :

1) the whole course has moved from Python 2.x to Python 3.x

2) Added manual webpage fetching and parsing. Web API is also added.

3) Improve the content order and enrich details of some content especially for some practice projects.

Note: videos are in Chinese (Simplified) with English subtitles. All other materials are in English.

What's inside

Syllabus

Welcome to learn Data Processing Using Python!

Hi, guys, welcome to learn “Data Processing Using Python”(The English version of "用Python玩转数据", url is https://www.coursera.org/learn/hipython/home/welcome)!In this course, I tell in a manner that enables non-computer majors to understand how to utilize this simple and easy programming language – Python to rapidly acquire, express, analyze and present data based on SciPy, Requests, Beautiful Soup libraries etc. Many cases are provided to enable you to easily and happily learn how to use Python to process data in many fields.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Helps non-computer majors process data using Python

Covers data acquisition, presentation, analysis, and visualization

Uses Python libraries such as SciPy, Requests, and Beautiful Soup

Applicable to various fields, including business, humanities, and science

Instructors have experience in data processing

Reviews summary

Comprehensive python data processing for beginners

According to learners, this course provides a solid foundation for individuals, particularly non-computer majors, seeking to learn data processing using Python. Many highlight the practical value and hands-on projects, finding them instrumental in applying concepts. The course is praised for starting with Python basics and gradually building up to data acquisition, analysis, and visualization. However, a notable point is that the video lectures are in Chinese with English subtitles, while other materials are in English, which some learners found challenging to navigate, though others adapted successfully. The updated content covering Python 3 and web scraping is seen as relevant and modern, contributing to the course's utility.

Good pace for beginners, some want more depth.

"The pace was just right for me as someone completely new to Python."

"Could use more in-depth coverage on complex topics or optimization techniques after the basics."

"While great for beginners, it might be too slow for those with prior programming experience."

"It gives a broad overview but doesn't go very deep into specific libraries like Pandas or NumPy."

Videos in Chinese, materials English.

"Note that videos are in Chinese (Simplified) with English subtitles. All other materials are in English."

"Following the Chinese lectures with English subtitles sometimes required extra focus and pausing."

"The mix of languages wasn't ideal, but the English subtitles and materials were sufficient to learn."

"The language format is clearly stated upfront, so learners can prepare."

Includes relevant Python 3 topics.

"Appreciate that the course has been updated to Python 3, making it relevant today."

"The addition of manual webpage fetching and parsing was a very useful update."

"Learning web scraping and APIs was a valuable part of the updated curriculum."

"It's good that the course content stays current with Python versions and methods."

Hands-on coding and projects are helpful.

"The hands-on coding and projects are the strongest part of the course for me, making theory concrete."

"I learned how to use practical tools and strategies that I could apply immediately to my work"

"The assignments helped solidify my understanding and apply the concepts taught."

"Doing the coding exercises was crucial to really learning the material."

Solid base for those new to Python/data.

"This course provided me with a strong foundation in using Python for common tasks"

"I gained a solid foundation from completing this course, particularly useful as a non-computer major."

"It's very good for absolute beginners, starting from scratch and building up."

"The course provides a good entry point for learning Python for data science."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Processing Using Python with these activities:

Review Python libraries for data analysis

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Ensure you are familiar with the key Python libraries used for data analysis, such as NumPy, Pandas, and Scikit-learn.

Browse courses on NumPy

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Review documentation or tutorials on these libraries.
Complete a few practice exercises using these libraries.

Create a data visualization dashboard

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Demonstrate your understanding of data visualization techniques by creating a dashboard that presents insights from a given dataset.

Browse courses on Data Visualization

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Choose a dataset that interests you.
Explore the data and identify key insights.
Design and create a dashboard using Python libraries like Matplotlib or Seaborn.
Present your dashboard to the class or a group of peers.

Create a Python data analysis tutorial

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Reinforce your understanding of Python data analysis techniques by creating a tutorial that explains a specific topic.

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Choose a specific topic within Python data analysis that you want to cover.
Research the topic thoroughly and gather resources.
Organize your content into a logical flow.
Create the tutorial using a platform like Medium or YouTube.
Share your tutorial with others and gather feedback.

Show all three activities

Career center

Learners who complete Data Processing Using Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are in charge of collecting, interpreting, and presenting data to help businesses make better decisions. This course will be beneficial to your pursuit of this role by providing you with a strong understanding of the Python programming language and essential libraries like SciPy, Requests, and Beautiful Soup. You will learn to gather and organize data, explore and analyze it, and share your findings.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use their technical prowess in data science to analyze data to discern patterns and relations. This course provides a strong foundational understanding of the syntax of Python, which is one of the most commonly used languages in data science. With course topics covering the acquisition, expression, analysis, and presentation of data, you will be well-equipped to handle the beginning phases of many data analysis tasks.

See salaries and explore the career path for Data Analyst

Data Science Manager

Data Science Managers oversee the development and execution of data science projects. This course can help you learn the basics of Python and essential libraries like SciPy and Pandas, which are widely used in data science. The course covers data acquisition, analysis, and visualization, all of which are important for managing data science projects.

See salaries and explore the career path for Data Science Manager

Statistician

Statisticians collect, analyze, interpret, and present data. This course can help you learn Python, which is frequently used in the field of statistics. The course covers data acquisition, analysis, and visualization, all of which are important for statistical analysis.

See salaries and explore the career path for Statistician

Data Visualization Specialist

Data Visualization Specialists create visual representations of data to help businesses understand and communicate their data. This course will teach you how to use Python to acquire and analyze data, and how to create clear and effective data visualizations.

See salaries and explore the career path for Data Visualization Specialist

Market Researcher

Market Researchers study consumer behavior and market trends to help businesses make better decisions. This course will provide you with a solid foundation in Python, a commonly used programming language in market research. You will learn how to acquire, analyze, and present data, and how to build and use data-driven models.

See salaries and explore the career path for Market Researcher

Data Engineer

Data Engineers design, build, and maintain the infrastructure that stores and processes data. This course provides a good introduction to Python, a popular programming language used in data engineering. The course covers data acquisition and processing techniques, which are essential for data engineering.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine Learning Engineers are specialists who design, develop, and deploy machine learning models. This course will teach you the basics of Python, a popular programming language used in the field of machine learning. You will also learn how to acquire, prepare, and analyze data, and how to build and evaluate machine learning models using Python.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make better decisions. This course will provide you with a solid foundation in Python, a popular programming language used in business analysis. You will learn how to acquire, analyze, and present data, as well as how to build and use data-driven models.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. This course will provide you with a good foundation in Python, a programming language frequently used in financial analysis. You will learn how to acquire, analyze, and present financial data, and how to build and use financial models.

See salaries and explore the career path for Financial Analyst

Insurance Analyst

Insurance Analysts use data to analyze risks and determine insurance premiums. This course will provide you with a good foundation in Python, a programming language often used in insurance analysis. You will learn how to acquire, analyze, and present insurance data, and how to build and use insurance models.

See salaries and explore the career path for Insurance Analyst

Database Administrator

Database Administrators are responsible for managing and maintaining databases. This course provides a good foundation in Python, a programming language frequently used in database administration. The course covers data acquisition and processing techniques, which are essential for managing databases.

See salaries and explore the career path for Database Administrator

Software Engineer

Software Engineers design, develop, and maintain software applications. This course is a good start to learning Python, a commonly used programming language in software engineering. It provides a foundation in Python syntax, data acquisition, presentation, and debugging, which are important for software development.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can serve as an introduction to Python, which is widely used in the financial industry. The course covers data acquisition and presentation techniques that are essential for financial modeling.

See salaries and explore the career path for Quantitative Analyst

Epidemiologist

Epidemiologists study the causes and distribution of diseases in populations. This course can help you learn Python, which is sometimes used in epidemiology for data acquisition, analysis, and visualization. The course covers data acquisition techniques and basic data analysis methods, both of which are helpful for epidemiology research.

See salaries and explore the career path for Epidemiologist