Course Summary
Learn how to use Python for data analysis, manipulation, visualization, and more with this comprehensive course. Gain practical skills in data cleaning, exploring, and modeling to solve real-world problems.Key Learning Points
- Learn powerful Python libraries for data analysis, including NumPy, Pandas, and Matplotlib
- Explore real-world datasets and use Python to extract valuable insights
- Master data cleaning techniques to ensure accurate and reliable results
Related Topics for further study
Learning Outcomes
- Understand how to use Python for data analysis and manipulation
- Learn how to leverage powerful Python libraries for data exploration and visualization
- Gain practical skills in data cleaning and modeling to solve real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with basic statistical concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Hands-on exercises
Similar Courses
- Applied Data Science with Python
- Python for Everybody
Related Education Paths
Related Books
Description
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Knowledge
- Understand techniques such as lambdas and manipulating csv files
- Describe common Python functionality and features used for data science
- Query DataFrame structures for cleaning and processing
- Explain distributions, sampling, and t-tests
Outline
- Fundamentals of Data Manipulation with Python
- Introduction to Specialization
- Introduction to the Course
- The Coursera Jupyter Notebook System
- Python Functions
- Python Types and Sequences
- Python More on Strings
- Python Demonstration: Reading and Writing CSV files
- Python Dates and Times
- Advanced Python Objects, map()
- Advanced Python Lambda and List Comprehensions
- Numerical Python Library (NumPy)
- Manipulating Text with Regular Expression
- Syllabus
- Notice for Auditing Learners: Assignment Submission
- Help Us Learn More About You!
- Week 1 Textbook Reading Assignment (Optional)
- 50 years of Data Science, David Donoho (Optional)
- Regular Expression Operations documentation
- Quiz 1
- Basic Data Processing with Pandas
- Introduction to Pandas
- The Series Data Structure
- Querying a Series
- DataFrame Data Structure
- DataFrame Indexing and Loading
- Querying a DataFrame
- Indexing Dataframes
- Missing Values
- Example: Manipulating DataFrame
- Week 2 Reading Assignments (Optional)
- Quiz 2
- More Data Processing with Pandas
- Merging Dataframes
- Pandas Idioms
- Group by
- Scales
- Pivot Table
- Date/Time Functionality
- Week 3 Reading Assignments (Optional)
- Quiz 3
- Answering Questions with Messy Data
- Basic Statistical Testing
- Other Forms of Structured Data
- Science Isn't Broken: p-hacking
- Goodhart's Law (Optional)
- The 5 Graph Algorithms that you should know
- Post-course Survey
- Keep Learning with Michigan Online!
- Final Quiz
Summary of User Reviews
Discover the power of Python for data analysis and gain the skills you need to excel in this growing field. This course has received high praise from students for its comprehensive content, engaging instructors and practical applications. One key aspect that many users found helpful is the hands-on approach to learning, with plenty of opportunities to practice what you've learned in real-world scenarios.Pros from User Reviews
- Comprehensive content that covers all the important topics in data analysis
- Engaging and knowledgeable instructors who provide clear explanations and helpful feedback
- Hands-on approach to learning with opportunities to practice what you've learned in real-world scenarios
- Practical applications that help you develop real-world skills
- Flexible schedule that allows you to learn at your own pace
Cons from User Reviews
- Some users found the course to be too basic and not challenging enough
- A few users had technical difficulties with the online platform
- The course can be time-consuming, especially if you want to complete all the assignments
- Some users felt that the course could benefit from more interactive elements
- The course may not be suitable for those who are already familiar with Python and data analysis
Christopher Brooks
4.5 Raiting
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