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Voltar para Python and Statistics for Financial Analysis

Comentários e feedback de alunos de Python and Statistics for Financial Analysis da instituição Universidade de Ciência e Tecnologia de Hong Kong

4.4
estrelas
3,165 classificações
703 avaliações

Sobre o curso

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....

Melhores avaliações

LH

23 de mar de 2020

A very good introduction course to python programming and it has a perfect combination with statistics, which makes financial analysis more interesting and refresh my mind on it, thanks.

GZ

25 de mar de 2020

Very clear explaining of the significant aspects when structuring a financial analysis, applicable in many forms of data if you don't want to make predictions only for the stock market.

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376 — 400 de 719 Avaliações para o Python and Statistics for Financial Analysis

por Md K I

4 de jul de 2020

Awesome!!

por Joydeep p

8 de mai de 2020

Very good

por Leonardo S M S

19 de set de 2020

Perfect!

por PRINCY X

24 de mai de 2020

NICE ONE

por Комаревич А П

1 de mai de 2022

Super!

por Aaron A B P

15 de ago de 2021

Thanks

por sw l

25 de ago de 2020

good !

por Kunal B D

16 de jul de 2020

v.good

por Kleber L d S

20 de jun de 2020

Ótimo.

por Melisa A

29 de dez de 2021

great

por Swagata R

11 de ago de 2021

good.

por Abhishek k G

24 de jul de 2020

great

por 王军乔

9 de out de 2019

great

por Tin C L

5 de jun de 2022

good

por SHIBAM C

4 de mai de 2022

GOOD

por Md Z

2 de set de 2021

good

por Siying C

27 de ago de 2021

nice

por 刘一洋

22 de ago de 2021

good

por Amlan B

24 de jun de 2021

Good

por Sankhadip J

5 de jun de 2021

Good

por SUKHEN D

27 de fev de 2021

good

por Zhu, T

6 de jun de 2020

good

por Xiaobing C

22 de dez de 2019

good

por Глеб В

28 de nov de 2021

The overall experience is good. Below I will describe main pros ans cons of the course.

To the benefits of this course I will attribute, firstly, its low entry threshold for beginners. Secondly, it was quite close to practice: methods of munging the data, visualizing the data, building simple trading signals with the help of Pandas library methods as well as evaluating the strategy with Sharpe Ratio and Maximum Drawdown coefficients will be very useful in real life and I hope will safe many hours of exploring technical issues in Stack Overflow and similar resources. Thirdly, the structure of the course was very intuitive: we had an overview of main statistical concepts before we needed to apply them to real data.

As to the drawbacks of the course, first of all, the overwiew of statistical concepts was too superficial. The listeners who don't have previous statistical, economic or engineering background will certainly have a lot of questions unanswered because they won't go to the proofs of the concepts and ideas presented. Secondly, though we've built trading strategies, they can not be applied in real life because we've not touched such topics as trading fees modelling and bid-ask spread modelling. Thirdly, I don't understand how the signals we have created may be technically applied in a trading environment as we didn't bother these questions.

To sum up, it was quite good introductionary course to the signal-based trading with the help of statistics. However, I think it should be further developed to cover more practical aspects and suggest different ways for listeners to improve their expertiese in the field of trading and quantitative finance, for example, by links to other courses in this sphere.

por Jitendra D S

11 de set de 2020

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