Об этом курсе: 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. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.
Для кого этот курс: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. The class is taught in a tutorial format using the pandas library, and only a minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.
Преподаватели: Christopher Brooks
| Основные сведения | Курс 1 из 5 — Applied Data Science with Python Specialization |
| Уровень | Учащийся среднего уровня |
| Язык | English, Субтитры: Korean, Vietnamese, Chinese (Traditional) |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Reading: Syllabus
- Reading: Help us learn more about you!
- Видео: Data Science
- Reading: 50 years of Data Science, David Donoho (optional)
- Видео: The Coursera Jupyter Notebook System
- Reading: Notice for Auditing Learners: Assignment Submission
- Notebook: Week 1 Lectures Jupyter Notebook
- Видео: Python Functions
- Видео: Python Types and Sequences
- Видео: Python More on Strings
- Видео: Python Demonstration: Reading and Writing CSV files
- Видео: Python Dates and Times
- Видео: Advanced Python Objects, map()
- Видео: Advanced Python Lambda and List Comprehensions
- Видео: Advanced Python Demonstration: The Numerical Python Library (NumPy)
- Notebook: Week 2 Lectures Jupyter Notebook
- Видео: The Series Data Structure
- Видео: Querying a Series
- Видео: The DataFrame Data Structure
- Видео: DataFrame Indexing and Loading
- Видео: Querying a DataFrame
- Видео: Indexing Dataframes
- Видео: Missing Values
- LTI Item: Hacked Data
- Notebook: Assignment 2
- Notebook: Week 3 Lectures Jupyter Notebook
- Видео: Pandas Idioms
- Видео: Group by
- Видео: Scales
- Видео: Pivot Tables
- Видео: Date Functionality
- Discussion Prompt: Goodhart's Law
- Notebook: Assignment 3
- Notebook: Week 4 Lectures Jupyter Notebook
- Видео: Distributions
- Видео: More Distributions
- Видео: Hypothesis Testing in Python
- LTI Item: End of Theory
- Discussion Prompt: Science Isn't Broken: p-hacking activity
- Notebook: Assignment 4 - Project
- Reading: Post-course Survey
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| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
Please,
Give some more insights of the basics.
thanks university of michigan for letting me do this course
a
SD
Awesome course!! First ever course that pushes you beyond your limits and provides a real-world data wrangling experience.