This course was ranked in the Top 100 Best Courses on Coursera, based on its high ratings and large number of ratings.
This course will introduce the student 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 Series and DataFrame abstraction as the core data structures for data analysis, along with tutorials on how to use features such as
This course will introduce the student 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 Series and DataFrame abstraction as the core data structures for data analysis, along with tutorials on how to use functions like groupby. , merge and pivot tables efficiently.
By the end of this course, students will be able to take tabular data, clean it, manipulate it, and perform basic inferential statistical analysis.
This course should be taken before any of the other Applied Data Science with Python courses: Applied Data Plotting, Graphing, and Representation in Python,
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In this week, you'll get an introduction to the field of data science, review common Python functions and features used by data scientists, and learn about Coursera's Jupyter notebook for lectures. All course information about grading, prerequisites, and expectations is in the course syllabus, and more information about Jupyter notebooks can be found on our Course Resources page.
In this week of the course, you'll learn the basics of one of the most important toolsets Python has for data cleaning and processing: pandas. You will learn how to read data into DataFrame structures, how to query these structures, and how to index the details about those structures.
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 update your understanding of data scales and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
In this week of the course, you will be introduced to a variety of statistical techniques such as distributions, sampling, and t-tests. The week ends with two discussions on science and the rise of the fourth paradigm: data-driven discovery.
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