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Introduction to Data Science in Python

 with  Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran
This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,  manipulate it, and run basic inferential statistical analyses.

This course is number 1 in the Applied Data Science with Python specialization and should be taken before any other courses in the specialization.

Syllabus

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.

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.

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.

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.

16 Student
reviews
Cost Free Online Course
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 4 weeks long
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What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

16 reviews

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5 out of 6 people found the following review useful
3 months ago
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Graham C partially completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
Really excellent course. Fast paced so be prepared to 'pause' to research or think about things. Doesn't spoon feed you so a bit of googling required now and again. Challenging assignments really make you think. Auto-grader for assignments has been buggy but is being fixed. Suggest you know Python a bit before starting Read More
Really excellent course. Fast paced so be prepared to 'pause' to research or think about things. Doesn't spoon feed you so a bit of googling required now and again. Challenging assignments really make you think. Auto-grader for assignments has been buggy but is being fixed. Suggest you know Python a bit before starting.

The course assignment can be graded without paying for the course - very generous functionality compared to most other courses where this is locked down.

Great first session, cant wait for the next!
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8 out of 10 people found the following review useful
3 months ago
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Anonymous is taking this course right now.
Lectures are too fast. They don't explain anything, just running through examples. For example they use a function but they don't explain what arguments it takes so you have to read about it elsewhere.
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4 out of 7 people found the following review useful
3 months ago
D C is taking this course right now.
This course is fast, but it's not the good kind of challenging. The instructor sounds like he's reading from a script, and there's almost no explanation of anything, even basic pandas syntax. "Here's a function you can use," and then just types it out without any explanation of, e.g., what parameters are mandatory, wha Read More
This course is fast, but it's not the good kind of challenging. The instructor sounds like he's reading from a script, and there's almost no explanation of anything, even basic pandas syntax. "Here's a function you can use," and then just types it out without any explanation of, e.g., what parameters are mandatory, what options there are, and what they mean.

The result is that each 7-min video takes me hours to work through and think about, and I'm still left with many questions. And no, I'm not a beginner to python. I'm honestly not sure if I'll finish the course at this point, though I'm halfway through.
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2 out of 3 people found the following review useful
3 months ago
Julián Urrea completed this course.
The course is definitely NOT for beginers in python. It's more than just challenging, sometimes, you don't know how to continue!!!, so you feel you want to quit at some point. What I loved the most, was the collaboration between students in the forum. A lot of students with great experience always ready to help. Sadly, Read More
The course is definitely NOT for beginers in python. It's more than just challenging, sometimes, you don't know how to continue!!!, so you feel you want to quit at some point. What I loved the most, was the collaboration between students in the forum. A lot of students with great experience always ready to help. Sadly, I never saw a mentors reply. But, I think, once you complete, you can say that you lerned very interesting thing to do with pandas...
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3 out of 3 people found the following review useful
3 months ago
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Anonymous is taking this course right now.
The lecturer puts minimal effort to the videos, information are scarce and difficult to understand. The assignments have a really steep learning curve, and are too difficult to complete, provided the topics covered by the lecturer. Help from the teaching staff is kept to a minimum, and most students don't actually m Read More
The lecturer puts minimal effort to the videos, information are scarce and difficult to understand.

The assignments have a really steep learning curve, and are too difficult to complete, provided the topics covered by the lecturer.

Help from the teaching staff is kept to a minimum, and most students don't actually manage to complete the assignments

In conclusion, the worst course i've ever taken in my academic life.
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4 out of 6 people found the following review useful
4 months ago
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Anonymous dropped this course.
Disconnect with the word "Introduction"... lecture goes from basic to quiz that assumes advanced knowledge. Think: Chem 101 to build a rocket engine the next day.

Stick with Dr Chuck's python course if you want to learn at the Intro level.
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3 out of 4 people found the following review useful
3 months ago
Alexander Partin is taking this course right now and found the course difficulty to be medium.
I am not a beginner to Python or data science but I find this course very helpful because it covers many useful topics in a concise and methodical way.
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3 out of 4 people found the following review useful
4 months ago
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Anonymous completed this course.
Excellent material and challenging problem sets. Course covered a lot of real world challenges. The notebooks were a great learning tool!
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0 out of 1 people found the following review useful
3 months ago
Y. Nicodeme completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
A great course focusing on the use of Pandas and Numpy libraries in the iPython environment. Assignments may be challenging for a complete beginner in python, and certainly requires some true dedication from part of the students.
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1 out of 1 people found the following review useful
2 months ago
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Anonymous partially completed this course.
Too fast and just talking through the typing of syntax is just not the way I learn. Nothing like the courses Charles Severance teaches. This is NOT teaching but rather talking quickly through syntax. NOT HELPFUL!
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3 weeks ago
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Anonymous is taking this course right now.
For sure is a challenging course, but I miss more efforts when it comes to explain "Lambda" or "List Comprehension" . Actually, I had to google a lot of times just to understand basic concepts of those functions -I'm not a Python noob though. The "tasks" during the videos are a bit frustrating, it feels like "here's a Read More
For sure is a challenging course, but I miss more efforts when it comes to explain "Lambda" or "List Comprehension" . Actually, I had to google a lot of times just to understand basic concepts of those functions -I'm not a Python noob though.

The "tasks" during the videos are a bit frustrating, it feels like "here's a formal definition of what Lambda is, now manage to solve something you probably won't understand because I didn't tell you how it works".
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3 months ago
Ilya Rusin completed this course, spending 15 hours a week on it and found the course difficulty to be medium.
It was wonderful experience and the best MOOC I've taken. Now I'm ready to study Python data science more with basic skills of pandas and data transformations.
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0 out of 2 people found the following review useful
3 months ago
Vladimir Shargin completed this course.
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0 out of 2 people found the following review useful
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2 months ago
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Mikael completed this course.
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0 out of 9 people found the following review useful
4 months ago
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Alex Ivanov audited this course.
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