Very good

Perfect!

NICE ONE

good !

v.good

Ótimo.

great

great

good

good

Using short videos was a good way to keep things interesting. The course was broken up into very manageable sections so I never felt I had too much work to complete in order to progress to the next section (especially since I work long hours and do not have much free time). The videos, along with the subtitles at the bottom of the page, were clear and easy to understand. The exercises were a little disappointing in my opinion. I believe the best way to learn most programming language is to type out the code from scratch and test at every step as you go along. I understand that some sections of the code we used to the analysis were complex, so my suggestion is to only include those parts of the code in the exercises, and have the student type out the easy parts repeatedly. For example the from excel, print, head, tail and other easy code can be filled out by the students instead of already having it in place. This will really help nail down the syntax and nuances of the language. You can include a help button that shows the correct code if the students can't figure it out themselves. Overall I'd give this course a 8.5/10 since I was able to apply this knowledge easily to my work. Thank you, Coursera & Xuhu Wan!

Jitendra De Silva

A fine introduction to the use of statistical models for finance (stock trading), showing its implementation in Python. It is NOT a course in either Python or Statistics but shows what one should learn. Alas, it does not give any pointers as to where to go to delve deeper into the needed statistics (nor trading, for that matter). It contains a fair summary explanation of linear regression models, but the recipes for their evaluation are discussed way too briefly.As for Python, it uses 4 common important libraries and directs the student to the corresponding sites. It gives no explanations as to the kind of structures being manipulated. The Jupyter notebooks are well set-up for practice.

The course provides an overview of how to build a quantitative trading model. However, the instructor does not go into details while either introducing python functions to someone unfamiliar with the language or talking about statistical concepts. I could follow the code based on my background in other programming languages.I will be following up this course with other courses that go in depth on both the programming and statistics front.The Jupyter notebooks are quite helpful and I will be using them for future reference.3.5 would probably be a more honest rating of the course but I don't think the course could have taught the learner more given its length.

This course shares a lot of info on how to use statistical analysis formulas like RMS, p-value, std. deviation, etc., and how to apply this knowledge using data modeling in a really easy way. There are some small hurdles to get over when taking the quizzes as some of the answers can be interpreted in multiple ways. out of the 4 quizzes I took, i attempted at least all of them 2 to 3 times. Not sure if I failed to absorb the knowledge well or if the goal was to go back and review the course material with a finer comb, either way, I found the course helpful and useful. I'd recommend it to friends and colleagues.

Good for what it intends to provide, namely a quick introduction to the topic, but it doesn't go very deep.

It is slightly annoying that there are plenty of typos and grammatical mistakes all over the Python code and the quizzes, which could easily have been avoided if either the author had somebody proofread everything quickly, or if Coursera had any type of quality control.

Nevertheless, coming from another programming language, I did get out of this course what I wanted, namely a collection of all the basic Python commands for this kind of analysis. So thank you for providing this course!

In general a satisfactory course and not too to follow through. It is focused more on the stat side than finance which I kinda have a mixed feeling toward. Professor could probably have done a little better job on explaining the meanings behind the formula but for the most part it is not hard to figure it out yourself by searching or reviewing the materials a few times by oneself. I also feel this course is a bit short, and if in the future it can try to cover more topics that will be awesome.

But hey I did learn stuff and am happy to have taken this.

The course was indeed helpful for my main goal to improve my skills using Python libraries to carry out mathematical / statistical caclulations.

One minor issue:

As I downloaded the notebooks for replaying them in my local Jupyter installation which is based on Python >= 3.6, I had to manually correct some statements due to changes in pandas, e.g.

pd.DataFrame.from_csv -> pd.read:csv or

pandas.tools.plotting -> pandas.plotting

mho it would be good to check for such issues

This course is more suitable for someone who has basic python knowledge. understand that there is a challenge with teaching programming languages via online platforms. It is quite difficult for the instructor to shorten the whole course into 4weeks material. Appreciate that the instructor and TA do spend time to answer student’s questions in the coursera forum. Candidate needs to spend extra time to view other sources to better understand the course material.

A wonderful course to get an introduction into financial statistics and a few python basics. This helped me understand many things about prediction and trading strategies. However to truly understand how to code a financial trading strategy you will need a lot more practice than you get in this course.

I really liked the course and would recommend it to anyone who wants to learn more about financial trading and python!

-: some little mystakes, exercice level very low

+: large présentation that provide both python and core financial statistics skill within high level

Might need more time than expected, maybe twice, in order to code the exercice meanwhile watching the video. Cause the final exercice for each week consists just in changing some value. Not enough to know about coding. Except if you already properly know Python

This course is a good starter for you to apply financial analysis by using Statistics models with Python programming. If you have experiments in either programming or statistics, you will find lessons are quite easy to understand. I recommend classmates could take a look at some python plugins such as flask, yfinance. They are wonderful tools for further study.

The course was helpful and definitely interesting. The only problem I found was that a lot of pre-existing knowledge was required and I had luckily studied some of it but the course did not cover it, It would also be helpful to add more indicators to show what each variable stands for in the formula since I found myself forgetting and had to rewind.

The course provides a very good learning experience. The course explains the various statistics that go into evaluation of stock data and further its execution using Python. The explanations could be bettered as there were many instances where pronunciations could not be comprehended. Overall the course provides a good learning experience!

전공이 금웅공학이나 금융분야는 아니지만 관심이 많아 찾아보던중 이 강의를 들어보았습니다. 결과적으로 말씀드리면 이 강의는 대학교의 명성만큼 어느정도 수준이 높은 강의이며, 기초지식으로 파이썬과 통계학을 요구합니다. 저같은 경우, 전공이 IT여서 파이썬과 통계학을 배웠음에도 불구하고 금융적인 해석능력이 부족하여 많이 고생하였습니다. 만약 이 강의를 듣기를 고민하고 있다면, 자신이 통계학과 파이썬을 어느정도 할 수 있는지 자체 레벨테스트를 할 필요가 있습니다. 강의의 구성과 교수님의 설명은 전체적으로 만족스럽습니다. 이 교수님이 조금 더 낮은 레벨의 강의를 개설하여 입문자를 더 많이 늘렸으면 좋겠네요.

Great Intro. course to Python application in the Financial domain. It will be beneficial to have some Python and Pandas background. Good examples, very practical.

It's a great course - with many practical examples. But this course needs some basic Statistics and Python knowledge to really follow along with some "deep concepts".