Course Summary
Learn how to use Python to process and analyze data in this comprehensive course. From data cleaning to data manipulation and visualization, you'll come away with the skills needed to tackle real-world data problems.Key Learning Points
- Learn to use Python libraries like Pandas, NumPy, and Matplotlib for data processing and visualization
- Gain practical experience working with real-world data sets
- Develop the skills needed to land a data analyst or scientist job
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453
- India: ₹5,28,000
- Spain: €29,000
- Data Scientist
- USA: $113,309
- India: ₹10,00,000
- Spain: €39,000
- Business Intelligence Analyst
- USA: $70,397
- India: ₹7,00,000
- Spain: €37,000
Related Topics for further study
Learning Outcomes
- Ability to process and analyze real-world data sets using Python
- Knowledge of popular Python libraries for data processing and visualization
- Preparation for a career in data analysis or data science
Prerequisites or good to have knowledge before taking this course
- Basic understanding of Python programming
- Familiarity with data structures like lists and dictionaries
Course Difficulty Level
IntermediateCourse Format
- Online, self-paced
- Video lectures and quizzes
- Hands-on projects
Similar Courses
- Data Science Essentials
- Python Data Structures
- Applied Data Science with Python
Related Education Paths
Related Books
Description
This course (The English copy of "用Python玩转数据" <https://www.coursera.org/learn/hipython/home/welcome>) 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.
Outline
- Welcome to learn Data Processing Using Python!
- Promotion Video
- Teaching Methods
- FAQ
- Basics of Python
- 1 Introduction to Python
- 2 The First Python Program
- 3 Basics of Python Syntax
- 4 Data Types of Python
- 5 Basic Operations of Python
- 6 Functions, Modules and Packages of Python
- 1.1 Extension: Build a Python Environment
- 1 Conditions
- 2 range
- 3 Loops
- 4 break, continue and else in Loops
- 5 Self-defined Functions
- 6 Recursion
- 7 Scope of Variable
- A1: Standard Library Functions
- A2: Exceptions
- 1.1 Walk into Python slides
- 1.1 References
- 1.1 Programming exercises(Not Graded)
- 1.2 Multi-dimensional View of Python slides
- 1.2 Control structure & function exercises(9 questions)
- Walk into Python quiz
- More About Python quiz
- Data Acquisition and Presentation
- 1 Local Data Acquisition
- 2 Network Data Retrieval
- 2.1 Extension: RE introduction
- 2.1 Extension: Dynamic web crawling example
- 1 Sequence
- 2 String
- 3 List
- 4 Tuple
- 2.2 Extension: IO&functional programming
- 2.2 Extension: Mutable objects modify issue
- 2 Data Retrieval and Represent slides
- 2.1 Internet Data Retrival Programming exercise(Not Graded, update on Oct 17, 2020))
- 2.1 code snippets for reference only
- Sequence fuctions practice
- Sequences and Files Programming Exercise(8 questions)
- Data Acquisition and Presentation quiz
- Powerful Data Structures and Python Extension Libraries
- 1 Why Are Dictionaries Needed
- 2 Dictionary Use
- 3 Set
- 3.1 Extension: dict and set programming examples
- 1 Extension Library SciPy
- 2 ndarray
- 3 Series
- 4 DataFrame
- 3.2 Extension: Common numpy applications
- Data Structure Selection
- 3 Powerful Data Structure and Software Ecosystem slides
- 3.1 Programming exercise(Not Graded)
- 3.1 Classic dict programming(1 question)
- 3.2 Programming exercise for DataFrame(Not Graded)
- 3.2 Modify the DataFrames
- Powerful Data Structures and Python Extension Libraries quiz
- Python Data Statistics and Mining
- 1 Convenient and Fast Data Acquisition
- 2 Fundamentals of Python Plotting
- 3 Data Clean of Data Exploration and Preprocessing
- 4 Data Transformation of Data Precessing
- 5 Data Reduction of Data Preproccessing
- Copy of 1 Convenient and Fast Data Acquisition
- 1 Basic Data Characteristics Analysis of Data Exploration
- 2 Data Statistics and Analysis Based on pandas
- 3 Cluster Analysis
- 4 Aplications of Python into Science and Engineering Fields
- 5 Applications into Humanities and Social Sciences Fields
- 4.2 Extension: An Analysis of the Differences between Males and Females on Film Ratings
- 4.2 Extension: Classification of Red Wine Data Based on Random Forest Model
- 4.1 Data retrieval and preprocessing of Python Slides
- 4.1 References
- 4.1.1 code snippets for reference only
- 4.1.3: Analyze test results using Box-plot
- Web API - TuShare and Data Analysis task
- 4.1 Titanic Data Set Acquisition
- 4.2 Data Statistics, Mining and Application Slides
- 4.2 code snippets for reference only
- 4.2.1 K-means algorithm an discussion on K value
- 4.2.1 Extension: Scikit-learn Machine Learning Basics
- 4.2.6 Project- —Linear Regression for Boston houses price prediction
- 4.2.6 Extension: Introduction to WAV audio processing
- 4.2.7 Learn More about NLTK
- Data retrieval and preprocessing of Python quiz
- Data Statistics, Mining and Application quiz
- Object Orientation and Graphical User Interface
- 1 GUI and Object Orientation
- 2 Abstraction
- 3 Inheritance
- 1 Basic Framework of GUI
- 2 Common Components of GUI
- 3 Layout Management
- 4 Other GUI Libraries
- 5 Comprehensive Applications
- Object Orientation and Graphical User Interface Slides
- code snippets for reference(BMI calculation)
- Crazy players(1 program, Not Graded)
- 5.2 Comprehensive practice project
- 5 code snippets for reference only
- Object Orientation and Graphical User Interface quiz
- Examination
Summary of User Reviews
Learn Python Data Processing with Coursera. This course has received positive reviews for its comprehensive curriculum and practical assignments. Many users enjoyed the hands-on approach to learning.Key Aspect Users Liked About This Course
Hands-on approach to learningPros from User Reviews
- Comprehensive curriculum
- Practical assignments
- Well-structured course material
- Clear explanations of complex topics
- Engaging and knowledgeable instructors
Cons from User Reviews
- Some users found the course too basic
- Lack of interaction with instructors
- Limited support for technical issues
- Some users experienced technical difficulties with the platform
- Not suitable for advanced learners
ZHANG Li
4.2 Raiting
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