Course Summary
Learn how to use Python to analyze and visualize data with this comprehensive course. Gain skills in data manipulation, data visualization, and data analysis that will help you stand out in today's job market.Key Learning Points
- Learn how to use Python libraries like Pandas, Numpy, and Matplotlib for data analysis and visualization
- Practice manipulating, cleaning, and transforming data to prepare it for analysis
- Explore statistical analysis and machine learning techniques to gain insights from data
Related Topics for further study
- Python libraries for data analysis
- Data manipulation and transformation
- Data visualization
- Statistical analysis
- Machine learning
Learning Outcomes
- Gain proficiency in using Python libraries for data analysis and visualization
- Learn how to manipulate, clean, and transform data to prepare it for analysis
- Develop skills in statistical analysis and machine learning techniques
Prerequisites or good to have knowledge before taking this course
- Basic familiarity with Python programming
- Basic understanding of statistics
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Quizzes and assignments
Similar Courses
- Data Analysis with Python
- Applied Data Science with Python
Related Education Paths
Related Books
Description
This course will continue the introduction to Python programming that started with Python Programming Essentials and Python Data Representations. We'll learn about reading, storing, and processing tabular data, which are common tasks. We will also teach you about CSV files and Python's support for reading and writing them. CSV files are a generic, plain text file format that allows you to exchange tabular data between different programs. These concepts and skills will help you to further extend your Python programming knowledge and allow you to process more complex data.
Outline
- Dictionaries
- Welcome!
- Class Structure
- Python Dictionaries
- Defining a Dictionary
- Dictionary Lookup and Update
- Checking Keys
- Handling Dictionary Errors
- Dictionaries - Example
- Practice Exercises for Dictionaries
- Dictionaries
- Tabular Data and Nested Data Structures
- Iteration over Dictionaries
- Tabular Data as a Nested List
- Tabular Data as a Nested Dictionary
- Displaying Dictionaries
- Tabular Data
- Practice Exercises for Nested Data Structures
- Nested Representations for Tabular Data
- Tabular Data and CSV Files
- Tables and CSV Files
- Parsing CSV Files
- Python's CSV Module
- CSV DictReader
- CSV Reader Options
- Experimenting with CSV Methods - Part 1
- Experimenting with CSV Methods - Part 2
- Project Video for Part 1
- CSV Files
- Practice Project: Loading Cancer-Risk Data
- Project Description: Reading and Writing CSV Files
- OwlTest: Automated Feedback and Assessment
- Organizing Data
- Sorting
- Lambda
- Advanced Sorting
- Refactoring Your Code - Part 1
- Refactoring Your Code - Part 2
- Project Video for Part 2
- Dictionaries vs. Lists for storing data
- Practice Project: Processing Cancer-Risk Data
- Project Description: Analyzing Baseball Data
Summary of User Reviews
Learn Python for data analysis with Coursera's course. Users have praised the depth of the course and the clear explanations given by the instructor. Overall, users have found the course to be informative and a great way to learn Python.Key Aspect Users Liked About This Course
The course is well-structured and covers a lot of groundPros from User Reviews
- The course covers a wide range of topics related to Python for data analysis
- The instructor is knowledgeable and explains concepts clearly
- The course is interactive and includes quizzes and assignments to reinforce learning
Cons from User Reviews
- Some users have found the pace of the course to be too slow or too fast
- The course may not be suitable for those with no prior programming experience
- Some users have experienced technical difficulties with the platform
Scott Rixner
4.7 Raiting
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