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Avis et commentaires pour d'étudiants pour Python and Statistics for Financial Analysis par Université des sciences et technologies de Hong Kong

4.4
étoiles
3,268 évaluations

À propos du cours

Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data. By the end of the course, you can achieve the following using python: - Import, pre-process, save and visualize financial data into pandas Dataframe - Manipulate the existing financial data by generating new variables using multiple columns - Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts - Build a trading model using multiple linear regression model - Evaluate the performance of the trading model using different investment indicators Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications....

Meilleurs avis

SL

13 avr. 2021

Un curso con una perceptiva muy refrescante en cuanto a los conceptos técnico-estadísticos y sumamente prácticos. e incluso baratos, de implementar dentro del mundo de la inversión. Muy buen trabajo.

EJ

3 août 2019

Great course! Very didatic explanations about financial and statistical concepts also with some interesting practical Python for Finance! Looking forward for new courses from same Univ. and prof.!

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701 - 725 sur 741 Avis pour Python and Statistics for Financial Analysis

par Volodymyr M

19 mai 2020

I really cannot recommend this course. Course tries to touch two major topics: Financial Risk Management and Statistics. In both disciplines course is only scratching surface of the subject without going into any details. Just for quantitive estimation, FRM is 100+ hours of material, Statistics - another 200+ hours. Whole course contains definitely less than 4 hours of lectures.

On the other hand - course is some sort of source of topics you need to find yourself over Internet and learn yourself.

par Warren V

1 avr. 2021

In evaluating this course, it took me a while to identify the target audience from the course materials. The statistical content is too fast-paced and lacking detail for newcomers, and that the Python content is de-emphasised. I suppose that this course is a good introduction for those outside finance seeking an introduction. Looking at this course as exercises to improve my Pandas knowledge, I left disappointed because there was no course Python assignment in implementing a trading strategy.

par Colin

1 févr. 2021

I have a PhD in econometrics - took this course to see if it would be useful to some of my team - gleans over complex issues way to fast but given the length that is understandable - my issues with this course are (a) the Jupiter notebooks are complete already so don't make students think and (b) there should be a great deal of included warnings with references for students to dig deeper outside of the class

par Daniel

3 juil. 2020

Part of the code doesn't work. To fix it it is needed to go to Python documentation and do research

A lot of statistics concept are not quite well explained. The course should include more detailed explanations or at lease some links where the this information can be studied

The results of the trading system implemented in the curse are not the one that you will obtain

par Nico M

25 nov. 2019

Too many mistakes in the use of English throughout the course made it hard for me to follow the course properly without getting annoyed. The instructor's English was terrible as well, just too many mistakes, which made it impossible to follow without subtitles. The content was good though which is why it deserves two points.

par Victor M

6 nov. 2019

I was hoping for a little more from this course. While it effectively combines statistics, finance and python, it does not go into great detail in any of the three. There are spelling mistakes and the audio quality is not great. For me the most useful part was learning the python functions and syntax.

par Andrey P

29 avr. 2022

Don't waste your time. The statistics part isn't explained well enough. Python code, which this course provides, is slow and uses outdated concepts. The idea of predicting stock prices using linear models is just a gem! If you are new to python, the course can show you how not to write code.

par Jonathan Z

29 sept. 2021

Course is good but (a) some Python bits are not up-to-date and (b) final week exam questions should be updated and brought in sync with either lectures or the lectures need to be updated. If Google has a different answer than the course, well, that's not a great place to be in.

par Rohit P

7 févr. 2022

Course uses <pd.DataFrame.from_csv >. This has been discarded by Python. Since I am begineer to Python and on assumtion that such discarded notations will again be used going further in course, I have no choice but leave the course in first week itself.

par Laleh N

28 mai 2020

i am happy with subject and course syllabus but if the data that the lecturer worked on them, was available the course would be much more useful,

without the data, it was just some code that we were watching.

thank you coursera :)

par Nicolas P

1 sept. 2019

I would change the title. It has little practical content on trade, and explains more statistical methods.I would call it "how to use and graph statistics in python, with some trade samples".

par Vikram N

25 févr. 2022

Does not explain technical terms well. I had to search online to understand much of this content. Some of the quiz questions related lab to are not explained in course / lab

par Mohini J

21 mars 2020

The course tried to cover a lot but wasn't really helpful for those who didn't have basic knowledge of either Python or Statistics

par Panguluri B T

10 juil. 2020

Poor Explanation of topics, was in a very hurry to complete than in explaining the concepts in depth. Did not reach expectations.

par Jacob G

23 juin 2021

More coding please. I was looking for more linear modeling examples and implementation. The rest was relatively easy.

par Andrew D

24 mai 2022

Not enough detail. Lectures have good material but a lot of information is presented too quickly. Labs are good

par HIMANSHU V

13 août 2019

Lectures are not very informative. Things are said directly and not explained well. Sadly I paid $50 for this.

par Victor H C C

17 juin 2022

weak, not really good course, just showing some basics of data analisys and concepts.

par Danny w

6 mai 2020

The teacher need to learn better pronunciation and slower pacing

par wegdan

24 févr. 2021

video lecturing lacks clarity and big picture context

par Eliad H

12 mars 2019

very basic,

not improving python skills

par Wickson H

28 févr. 2022

Too difficult to beginner

par Lubie W

9 août 2020

This course teaches statistics more than Python coding. The codes are not well explained or even not explained by the instructor. The instructor spent more time on statistics concepts than going through the Python coding. I learned very little about Python in this course.

par Liem J L

15 nov. 2019

Should be better explained. Could not get past the first few lines in the practical. Looked at the discussion board and people were saying it was because the course is outdated and the code he explained might not even work with the version we were using

par Anas A H H

16 juin 2021

1- t​he language spoken is not clear (I had t oread the subtitles more than listening which was a horrible experience)

2​- the labs are bot built in the right way, lots of errors and lots of data changes that effected the application of the commands