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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
1,466 évaluations
328 avis

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

LH

Mar 24, 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.

TT

Apr 23, 2020

Generally, the course offer many approach with financial data but not very easy to understand for beginner such as myself. I hope there will be more course like this in the future !!!

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151 - 175 sur 327 Avis pour Python and Statistics for Financial Analysis

par Graham T

Feb 22, 2020

Great! Thank you :)

par Brian P

Jun 03, 2020

Power of knowledge

par SHARIQUE A K

May 20, 2020

AWESOME EXPERIENCE

par Feiting X

Feb 27, 2019

Nice Introduction!

par Santiago L C

Jan 30, 2019

I enjoy very much

par Jonathan M

Jun 28, 2020

very informative

par Artem C

Feb 23, 2020

Great course !

par ARAVINDH D

May 26, 2020

Thank you sir

par Bhavya S

Jan 27, 2020

whatta wow :D

par 裴品傑

Jun 05, 2019

很不錯,但最後的回歸有點難

par Tsoi K M E

Jun 15, 2020

Great course

par Heiner A M V

Apr 29, 2020

Very usseful

par Henrique G

Sep 24, 2019

It's great!

par Ruikun D

Jan 29, 2019

very

useful

par Md K I

Jul 04, 2020

Awesome!!

par Joydeep p

May 08, 2020

Very good

par PRINCY X

May 24, 2020

NICE ONE

par Kleber L d S

Jun 20, 2020

Ótimo.

par John W

Oct 09, 2019

great

par Zhu, T

Jun 07, 2020

good

par Xiaobing C

Dec 22, 2019

good

par Claudio H

Apr 21, 2020

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.

par Kushagra S

May 22, 2020

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.