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.
Этот курс входит в специализацию ''Специализация Applied Data Science with Python'
Introduction to Data Science in Python
от партнера
Об этом курсе
4.5
Оценки: 9,210
Чему вы научитесь
Describe common Python functionality and features used for data science
Explain distributions, sampling, and t-tests
Query DataFrame structures for cleaning and processing
Understand techniques such as lambdas and manipulating csv files
Приобретаемые навыки
Python ProgrammingNumpyPandasData Cleansing
Программа курса: что вы изучите
Неделя
13 ч. на завершение
Week 1
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.
11 видео ((всего 58 мин.)), 4 материалов для самостоятельного изучения, 1 тест
11 видео
Data Science7мин
The Coursera Jupyter Notebook System3мин
Python Functions8мин
Python Types and Sequences8мин
Python More on Strings3мин
Python Demonstration: Reading and Writing CSV files3мин
Python Dates and Times2мин
Advanced Python Objects, map()5мин
Advanced Python Lambda and List Comprehensions2мин
Advanced Python Demonstration: The Numerical Python Library (NumPy)7мин
4 материала для самостоятельного изучения
Syllabus10мин
Help us learn more about you!10мин
50 years of Data Science, David Donoho (optional)30мин
Notice for Auditing Learners: Assignment Submission10мин
1 практическое упражнение
Week One Quiz12мин
Неделя
23 ч. на завершение
Week 2
In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed. The module ends with a programming assignment and a discussion question.
8 видео ((всего 45 мин.)), 2 тестов
8 видео
Introduction2мин
The Series Data Structure4мин
Querying a Series8мин
The DataFrame Data Structure7мин
DataFrame Indexing and Loading5мин
Querying a DataFrame5мин
Indexing Dataframes5мин
Missing Values4мин
Неделя
33 ч. на завершение
Week 3
In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
6 видео ((всего 35 мин.)), 1 тест
6 видео
Pandas Idioms6мин
Group by6мин
Scales7мин
Pivot Tables2мин
Date Functionality5мин
Неделя
46 ч. на завершение
Week 4
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
4 видео ((всего 25 мин.)), 1 материал для самостоятельного изучения, 2 тестов
1 материал для самостоятельного изучения
Post-course Survey10мин
33%
начал новую карьеру, пройдя эти курсы
34%
получил значимые преимущества в карьере благодаря этому курсу
11%
стал больше зарабатывать или получил повышение
Лучшие рецензии
автор: AU•Dec 10th 2017
Wow, this was amazing. Learned a lot (mostly thanks to stack overflow) but the course also opened my eyes to all the possibilities available out there and I feel like i'm only scratching the surface!
автор: SI•Mar 16th 2018
overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .
О Мичиганский университет
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
О специализации ''Applied Data Science with Python'
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
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