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 is part of the Applied Data Science with Python Specialization
590,526 already enrolled
Offered By
Applied Data Science with Python SpecializationUniversity of MichiganAbout this Course
683,363 recent views
Learner Career Outcomes
27%
started a new career after completing these courses
30%
got a tangible career benefit from this course
13%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 30 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
What you will 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 will gain
Python ProgrammingNumpyPandasData Cleansing
Learner Career Outcomes
27%
started a new career after completing these courses
30%
got a tangible career benefit from this course
13%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 1 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 30 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
University of Michigan
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.
Syllabus - What you will learn from this course
12 hours to complete
Fundamentals of Data Manipulation with Python
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.
12 hours to complete
12 videos (Total 111 min), 7 readings, 2 quizzes
6 hours to complete
Basic Data Processing with Pandas
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.
6 hours to complete
9 videos (Total 89 min), 1 reading, 2 quizzes
7 hours to complete
More Data Processing with Pandas
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.
7 hours to complete
6 videos (Total 84 min), 1 reading, 2 quizzes
6 hours to complete
Answering Questions with Messy Data
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.
6 hours to complete
2 videos (Total 20 min), 5 readings, 2 quizzes
Reviews
TOP REVIEWS FROM INTRODUCTION TO DATA SCIENCE IN PYTHON
by PSAug 11, 2020
Assignments are way tougher than what is taught in the class, but they are challenging and the help in discussion forums is speechless. Without that, completion of assignments will take too much time.
by PKMay 9, 2020
The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans
by BZMay 17, 2020
super hard course I enjoyed it but the thing that I didn't like specially when this is supposed to be the learning phase the assignments were hard and it sometimes pushes you to look for the solution
by ANAug 15, 2020
I found this course appealing because it was more practical based.it helped me alot in getting hands on experience and most of all I have learned how to solve real world problem with python libraries
About the Applied Data Science with Python Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.
