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.
Introduction to Data Science in Python
This course is part of Applied Data Science with Python Specialization
Instructor: Christopher Brooks
1,089,030 already enrolled
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(27,093 reviews)
What you'll learn
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
Skills you'll gain
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There are 4 modules in this course
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.
What's included
12 videos6 readings1 assignment1 programming assignment2 ungraded labs1 plugin
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.
What's included
9 videos1 reading1 assignment1 programming assignment1 ungraded lab
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.
What's included
6 videos1 reading1 assignment1 programming assignment1 ungraded lab
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
What's included
2 videos6 readings1 assignment1 programming assignment1 ungraded lab
Instructor
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Taking this course by University of Michigan may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.
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Reviewed on Mar 1, 2017
Enjoyed every bit of it. The timings for the assignment could be a little bit more though. Also the expected output could be provided for validation, rather than the grader printing cryptic messages.
Reviewed on May 3, 2018
It's very useful specially for new learner because it only dives into the part of python that data science need. I strongly recommend to anyone even if you don't have experience in programming before.
Reviewed on Dec 31, 2016
To be an introductory course I struggled a lot, is a very practical course, and the assignements encourage you to learn more. This is the best technical course I have taken. Lo recomiendo ampliamente
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