Back to Understanding and Visualizing Data with Python

4.6

182 ratings

•

48 reviews

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.
At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera....

Apr 04, 2019

Excellent introductory course to statistics. Great use of NHANES dataset to demonstrate techniques on real dataset. I would appreciate a more demanding project at the course end.

Jan 24, 2019

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory.

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By Daniel R

•Feb 22, 2019

Lectures are great but there's little practice material and the quizzes are terrible. The quizzes are actually super easy but they don't cover much material from the course and sometimes introduce concepts and terms that were nowhere in the course materials.

If you want a good intro to stats without any actual testing, the lectures get pretty in-depth and the explanations are excellent! But if you're looking for lots of practice with stats in Python, you won't get much here.

By Md I

•Apr 19, 2019

This is the best course in this website in entry level

By José A G P

•Apr 16, 2019

The course contents are good to an introduction or refreshing in statistics but the assigments are not really well prepared, and contains many unrepaired errors. This drops down the level an educational potential of this course (and the entire specialization) and converts it in a poor educational resource and a waste of time, in my opinion

By Aayush G

•Apr 15, 2019

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory. The most exciting part of the course is Brenda Ma'am performing a cartwheel !! For all the ones who are enrolled, don't forget to watch it out.

By David W

•Apr 14, 2019

I love the U of M courses! I get so much out of them. Thank you again for helping me to advance my knowledge of Python and deepen my understanding of statistics.

By Kristoffer H

•Jan 10, 2019

This course still has spelling mistakes in its quizzes, which in a programming focused course are big, and the instructors don't seem interested in fixing them. The result is you have to guess through their mistakes if code is suppose to not work in a quiz because of the error or the error is not supposed to be there in the first place and the code is valid.

By Christine B

•Jul 19, 2019

I feel 100% more confident in my job now. We just started using Python for analysis and I am probably now ahead of many of my coworkers in a super short amount of time. The class got me over the hump in the learning curve so I can progress much faster than trying to learn on my own.

By Jafed E

•Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

By HUNG H L

•Jun 16, 2019

Sometimes, the lines in Jupyter notebooks are kinda hard to understand. Yet, there are a lot of materials out there online for us to explore; for this, I also learn how to solve programming problems by myself. In general, I like the courses and the instructors a lot.

By Girish C

•Jun 15, 2019

nice learnig experience

By JIANG X

•Jun 11, 2019

I love the depth and breadth of the content. It provides in-depth knowledge of statistics and wide range of context information and supplementary reference learning materials. I also appreciate that each lesson is accompanied by hands-on activities using Jupyter notebook which definitely has helped me gain a deeper and clearer understanding of the content.

By Don J

•Jun 04, 2019

Wonderful introduction to Basic Statistics. Loved the python notebooks as well.

By Brett S

•May 29, 2019

Great class for beginners. Would have loved to learn the material in smaller python notebook snippets throughout. Instruction was excellent!

By Joffre L V

•May 26, 2019

Great course, excellent!!!

By Hamza a

•May 13, 2019

excellent course having comprehensive lectures

By Partha S

•May 13, 2019

It's a wonderful course. Each and every part is well designed and very well explained. i

By Vinícius G d O

•May 12, 2019

If you are searching for a course who could either teach you all about the world of statistics - ranging from statistical analysis with awsome examples and explanation with demosntrations of statistical methods - and at the same time force you trough programming, this is the right course.

I'm very grateful by the efforts of course's team in undertaken such work! I'm now more prepared to advance in my carrer, thanks to it!

By CHITRESH K

•May 12, 2019

Coursework is great and so are the teachers , concepts are taught in a easy to understand way.

By Hugo I V R

•May 09, 2019

This can be a quite helpful course for beginners. I really liked the course because it thoroughly introduced me into Seaborn (visualization library) which I was unaware of. Also, some of the practical exercises truly help you develop your pandas skills. I really enjoyed week 1-3, which truly challenged me and introduced me to new concepts with a good balance between practical and theoretical. However, week 4 felt a bit off. The contents could've been split into two weeks. The practical tasks are minimal compared to readings and videos. And the final quiz covers like 15% of all that was taught in the week. Concepts like CDF were never taught but employed at the end when talking about the empirical rule.

By Purushottam P

•Apr 26, 2019

Thanks for everyone involved in preparing the course content. Gained a lot of insights from this course. Really opened my eyes on the basics of statistics. Will definitely complete all the courses of this specialization!

By Yaroslav B

•Apr 24, 2019

There is incorrect course title for this course as in reality it’s Statistics AND partial illustration of it using Python. There is no consіstent exposition on Python libraries and frameworks.

By Nirmal M

•Apr 19, 2019

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory

By Rashbir S

•Apr 16, 2019

This is a must. If you want to be a data scientist then this course is like a stepping stone. Trust me its totally worth it

By VIVEK N

•Apr 15, 2019

Very helpful in understanding sampling stats...using python is like a cherry on top :)

By Richard R

•Apr 15, 2019

A well paced stats refresher which covered the core material well and skillfully introduced current research. The fourth week was a solid introduction to sampling methodologies and inference. Looking forward to the next course in the sequence.

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