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.
Offered By
Applied Data Science with Python SpecializationUniversity of MichiganAbout this Course
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Learner Career Outcomes
33%
started a new career after completing these courses
35%
got a tangible career benefit from this course
10%
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. 31 hours to complete
English
Subtitles: English
Skills you will gain
Python ProgrammingNumpyPandasData Cleansing
Learner Career Outcomes
33%
started a new career after completing these courses
35%
got a tangible career benefit from this course
10%
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. 31 hours to complete
English
Subtitles: English
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
12 videos
Introduction to the Course4m
The Coursera Jupyter Notebook System8m
Python Functions8m
Python Types and Sequences8m
Python More on Strings3m
Python Demonstration: Reading and Writing CSV files3m
Python Dates and Times2m
Advanced Python Objects, map()5m
Advanced Python Lambda and List Comprehensions2m
Numerical Python Library (NumPy)32m
Manipulating Text with Regular Expression27m
7 readings
Syllabus10m
Notice for Auditing Learners: Assignment Submission10m
Help Us Learn More About You!10m
Week 1 Textbook Reading Assignment2h
50 years of Data Science, David Donoho (Optional)1h 30m
Regular Expression Operations documentation1h
Regular Expressions Debugging10m
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
9 videos
The Series Data Structure10m
Querying a Series15m
DataFrame Data Structure12m
DataFrame Indexing and Loading8m
Querying a DataFrame9m
Indexing Dataframes8m
Missing Values11m
Example: Manipulating DataFrame8m
1 reading
Week 2 Reading Assignments1h
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 videos
Pandas Idioms15m
Group by19m
Scales10m
Pivot Table9m
Date/Time Functionality12m
1 reading
Week 3 Reading Assignments2h
5 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.
5 hours to complete
2 videos (Total 20 min), 4 readings, 2 quizzes
4 readings
Science Isn't Broken: p-hacking45m
Goodhart's Law (Optional)30m
The 5 Graph Algorithms that you should know10m
Post-course Survey10m
1 practice exercise
Final Quiz30m
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 BNMar 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.
by AUDec 9, 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!
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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
More questions? Visit the Learner Help Center.
