Retour à Python and Statistics for Financial Analysis

étoiles

2,800 évaluations

•

620 avis

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....

GZ

25 mars 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.

TT

22 avr. 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 !!!

Filtrer par :

par Zeyu H

•20 janv. 2019

【Now you know Prof. Xuhu Wan, please avoid his course in HKUST】

0. Course Equivalence😐

This course basically covers 50% content of MATH2411 Applied Statistics (I heard there is ISOM2500 that is similar to MATH2411?). Accidentally I took 2411 right before this winter when this course is out, so I found this course quite disappointing because I expect some practical manipulation of Python is covered while it doesn't. More is discussed in #3.

1. Teaching ☹

If you have the experience of recording a video presentation eight hours before the deadline, with scripts written three days before and you hadn't recited or even gone through it in these three days, you will find the professor the same unpassionate. You will find his tone flat enough and gestures unnatural enough as if he is not emphasizing on anything but focusing to recite his scripts. You will find him lag a lot at strange and unnatural spots as if his brain goes blank and he quickly reads the copy of scripts next to the camera.

I thought business people cares a lot about presentation, but I was wrong.

2. Subtitle 😡

There are tons of me steaks in the subtitles, not only tipos but also worlds of cellar pronunciation.

(There are tons of mistakes in the subtitle, not only typos but also words of similar pronunciation.)

I enable subtitle because I sometimes can‘t understand the professor's perfect Mainland accent, but it turns out the subtitle is on his side but not my side.

I thought business people are very strict about the material that comes along with their presentation, that they always carefully spellcheck every sentence. But I was wrong.

3. Content 😐

3.1 Overall:

Please rename this course "Python and applied statistics". The professor spends sooooo much time talking about the statistics concepts and spends soooo little time applying the knowledge to financial analysis. It is not about "Statistics for Financial Analysis". Replace the data he uses for demonstration with GPA of every student and it becomes "Statistics for Being HKUST President" or "Statistics for Anything". I feel I am taking an introduction course to statistics and financial analysis is just an excuse the teacher use to show us the content he teaches is somewhat useful.

3.2 Pace:

You MAY find the pace quite fast because:

The teacher throws many statistics concepts

The teacher cannot fully explain the concepts (or it is not a 4 week course) so he moves on before you ever (perhaps never will) digest the previous concepts

This is extremely annoying in week 4, e.g. Multiple Linear Regression is taught without introducing a single formula, merely Python codes and black boxes behind them. (Actually this is the way I originally expect the professor to do, but it is quite inconsistent with the style in week 1-3)

You MAY find the pace quite slow because:

After all this course introduces formulas and codes and let you to use them without knowing why.

So I would say this is a 4-day course if you can spare 1 hour each day. After all you are not asked "why" but only "how". If you haven't taken MATH2411 or ISOM, you can spend more time on week 2 & 3 to understand the underlying knowledge. Week 1 is simple and week 4 is needless to comprehend.

4. Jupyter Notebook (JN for short) 😡

4.1 Poor Exercise

Almost useless. Just a copy of the codes appeared in the video, with some variables assigned None instead of the correct expression. Your job is to change the lines of variable assignment (usually one or two lines), and the rest is done for you. Some notebooks are even 100% done for you, and all you need to do is look at it and appreciate. Even if you are fiddling with provided exercises, you don't know how to use JN, because...

4.2 Irresponsible adoption of JN

If you want to do some real exercise, you may want to append empty cells below the given content and type codes from scratch. But oh, this course does not teach you how to use JN! It just throw you a tutorial link of how to INSTALL JN ON YOUR COMPUTER{https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook}. What a shame!

Quickly gone through the linked tutorial, it assumes you have installed multiple instance of Python on your desktop, and know basics of pip, conda, docker, and virtual env, and teaches you how to install and configure JN in various dev. environments. But you just mentioned we can use Coursera's pre-installed JN out-of-the-box, why you want us to learn that huh? And to create cells, run cells, run several cells in order, run all, and other basic operations, is hidden in the last seconds of GIFs, not explicitly explained.

I guess the professor is TOO UNRESPONSIBLE to not only teach students how to use JN himself, but also SPEND AT LEAST SOME TIME to check if the external tutorial really "explains how to use Jupyter Notebooks". Please, not every one taking this course is CS student like me, SBM students they may not know how to use Python stuff.

5. Coursera Technical 😐

Quizzes do not provide correct answer. So it is not that helpful. But getting 80% is not that hard either. But given the assumption that you can't use JN (explained in #4.), you lose at least 10% in Quiz 3 and 20% in Quiz 4. Oh that hurts! (Since Notebook 4.4 is done for you, another 20% in Quiz 4 related to JN is okay.)

par Helena K

•8 févr. 2019

this course is very practical! it explains how statistic concepts can be applied into financial-related examples using python.

some argue the course do not cover enough of python nor financial, nor statistics concepts. hey man !!! this course is not a baby intro course!!! it assumes you are either strong in one/some of the aspects (either you are strong in computer, or stats, or finance), and you want to see how the other aspects can be combined to work out something valuable. do you need to learn everything about a car before driving it? you just learn what you need to get the car moving man!!

This course is not spoon-feeding like your elementary school teachers!!! Professor taught you something, and you are expected to study further on your own. i am not good at stat, but I know programming reasonably well, I know where i should pick up some statistics to understand the materials.

you will be able to find tons of courses that introduces programming language/statistics, but they never tell you how useful the programming language/statistics is in real life. But this course is so practical that I can pick up the knowledge and use immediately.

Highly appreciate professor xu's effort in creating this valuable course!

par Cheuk W K

•28 févr. 2019

It is a good course overall, combining the basics of statistics, Python and finance. I've learned a lot from it. I think the students can benefit more if additional suggested reading materials can be provided, so that if one lacks a strong background in a particular discipline, one can find out more outside the course. Also will be helpful if slides can be downloaded.

par sabarinathan r

•6 févr. 2019

This gives a application of all the three famous sectors viz, finance, python and statistics. Actually speaking i am searching for these kind of courses and did not get one. Atlast got this one for my solace. This suited my need. This course cannot be easily designed as other courses . This really needs one time . Thanks to the person who devised the course and also to the instructor Mr. Xuhu Wan for his meticulous time to provide the information in a precise way.

Infact the while explaining errors actually in a very short time he explained the unexplained, explained and total error in a concise and apt way. Really this a wonderful course.

Thanks

Sabarinathan alias Cheryn

par Kevin L

•27 juil. 2019

I have no finance background. But i have some extent of programming knowledge. I learn a lot from this course not only finance terms, meaning behind them and how we apply statistic using python to analyze, evaluate and predict market. This course is very pratical thank you Professor Wan!

par Tony T

•23 avr. 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 !!!

par Krzysztof P

•29 juin 2019

I have mixed feelings about the course. It shows very practical aspects of building trading stategy in Python, which is still quite unique topic here. It also offers a lot of practice and ready to use and modify solutions delivered as Jupyter notebooks. This course definitely expect you to know a bit about statistics and also to know Python programming, on basic level at least. On the other hand I think the course does not cover the topic deep enough, we've got only some simple linear regression model based on some not-so-creative feature engineering. It does not cover such aspects as HFT vs swing trading strategies, using slipage and transaction costs to evaluate strategy, managing invested capital and many more. I've expected a bit more, to be honest. The course is well done as ready-to-use implementation of very simple concept - but there's nothing more to expect here.

par Satish N

•27 févr. 2019

I had only basic knowledge of python and very basic knowledge of statistic - most of which I had not put to use, since leaving school. This course was a helped me to get more confidence with using python in a practical way. In the process I also brushed up my statistical skills - there is no better way to understand statistics then to apply in real-life scenarios as explained in this course. And python packages makes learning fun, by taking off the difficult computation tasks. Overall I would recommend this course to anyone who has interest in learning how to apply statistics and python to analysing data.

par Nipun A

•10 mai 2020

This course takes you from the basics to the more advanced stages of statistics, while teaching you python (even if you have never used it before) and how to build your own financial strategy. It is well rounded and pushes you to learn.

par Ernani H M J

•4 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.!

par Leung P H

•24 mars 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.

par ANAND M I

•10 mai 2020

This is a good course. I did not learned or gone through any of the Python module before joining this course, but the training was good. Thank you Xuhu Wan for your training.

par Ezekiel J T

•5 févr. 2019

The lecture videos were very helpful to my studies. The teacher was able to explain the materials very clearly. However, I this course doesn't fit my expectations. The reason why is because I wanted to learn how to code in Python. This course emphasizes more on the business side and it doesn't provide an opportunity for us to actually learning the basics of coding in Python. I only learned a few useful terms in Python.

par Chutian Z

•10 févr. 2020

The topics are interesting and some basic statistics/finance knowledge are required for the learner. The cons of this course is there are no coding practices or homework. The Jupyter Notebook included after each lesson basically asks you to copy and paste the syntax provided. The student's knowledge base is evaluated only through simple quizzes, which I found absolutely insufficient. Only when the hands get dirty can a student actually master the coding skills. I won't recommend this course.

需要一些基本的统计学和金融知识，但没有作业自己不用输代码，coding skill得不到训练，不推荐。

par Rhoit A

•7 juin 2020

Only Suitable if you Have already learned Finance and Statistics, more focus is towards Statistics. What I don't like unnecessary and misuse use of Pandas and statsmodels which could have kept the exercise simpler.

par Abu S M A

•12 août 2020

Worst Course, This course makes my interest of learning down when I start to continue it. The language is wired and boring. Topics seem not effective and interesting. I unenroll it in after 2 weeks.

par Nitish S

•17 mai 2020

For an Intermediate level course, it was not at all challenging. The course was too short, and I felt a lot more content could have been added. Also, need challenging assignments!

par NG W c

•23 avr. 2020

I write to disagree with what Zeyu H posted on 20.1.2019. The comments by him or her were unfair.

0. The trend is to use the toolkits developed. e.g. Pandas and

Numpy introduced in the cost. Come one...no one is going to programme another least square regression function in Python or any language. Grab a library and go. And read the (API) documentations yourselves.

1. The pace of the video could be adjusted to 1.25x, 1.5x or 2x.

2. Subtitles are computer-generated dude.

3.1 Correct. Welcome to the world.

3.2 The course expects learers to have prior basic knowledge in probability.

4. ,,,,,who would still install jupyter notebook? Visit google colab please.

5. Quiz is the least important issue in learning I guess.

My comment: this course provides good pointers for anyone to have a taste of python packages and technical analysis. Since pandas has had version updates, you do need google to help some codes to be smooth in the latest version. After completed this course, you should be able to find some data on your own and try their correlations for a model with predictability testable by you.

par Chan H H

•17 juil. 2020

The course is highly practical and teaches useful skills in programming, statistics and finance. Its relatively low workload helps me cope with my other commitments while staying on track with the course. The Professor's clear slides and Python code are also a huge plus - it doesn't matter that you haven't got a coding background beforehand! A definite 5 star course, highly recommended! :-)

par Philip L

•7 avr. 2021

An excellent beginner's guide to financial statistics using Python's Pandas module. Can be completed very quickly by those familiar with both basic Python and introductory Statistics.

par Michael K

•19 oct. 2020

Tricky accent at time but great content and absolutely super Jupyter notebooks. The presentation looks cheap but this is one of the best finance/python starter courses on coursera.

par TJ D

•5 juil. 2019

The videos in this course are exceptional and very interesting. The Jupyter notebooks provide a good template for applying the methods and techniques.

par carlo

•23 mars 2019

V

e

r

y

w

e

l

l

d

o

n

e

par Kevin B

•24 août 2021

I felt that this course started out pretty easily. It covered basic things in probability / statistics. I felt at the start of the course it was an okay fit for me since I already knew what it was talking about. But that changed pretty abruptly.

Assignments were not very useful in building a solid understanding of python using pandas. Almost all of the code was there for you. And sometimes they were just "Look at this code and see what it does". There's not very much hands on learning which is important for solid retention.

With these in mind, I really don't understand who the target audience was supposed to be here exactly. It seems like you should know both some Python and Statistics before taking this course. Probably more than the intermediate level. I think that could have been fixed if this was a Specialization instead of a single course. I think that is where the issue lies. If things were expanded into multiple courses, this concept and course could have worked better.

I ended up abandoning working on this course in Week 3 and shifted to two Specializations on Python and one on Statistics. (I already know a bit of probability and statistics, but clearly not enough for this course.)

par Jessica P

•3 août 2021

zero walkthroughs. have NO idea what to do. course is incomprehensible

- Analyste de données Google
- Gestion de projet Google
- Conception d'expérience utilisateur Google
- Google IT Support
- Science des données IBM
- Analyste de données d'IBM
- Analyse des données IBM avec Excel et R
- Analyste de cybersécurité d'IBM
- Marketing appliqué au réseau social Facebook
- Développeur(euse) Cloud Full Stack IBM
- Sales Development Representative Salesforce
- Opérations de ventes Salesforce
- Soporte de Tecnologías de la Información de Google
- Certificado profesional de Suporte em TI do Google
- Automatisation informatique Google avec Python
- DeepLearning.AI Tensorflow
- Certifications populaires en cybersécurité
- Certifications SQL populaires
- Certifications populaires en informatique
- Voir tous les certificats

- cours gratuits
- Apprendre une langue
- python
- Java
- conception web
- SQL
- Cursos Gratis
- Microsoft Excel
- Gestion de projet
- Cybersécurité
- Ressources humaines
- Cours gratuits en Science de données
- parler anglais
- Rédaction de contenu
- Développement Web Full Stack
- Intelligence artificielle
- Programmation en C
- Compétences en communication
- Blockchain
- Voir tous les cours

- Compétences pour les équipes en charge de la science de données
- Prise de décisions basées sur les données
- Compétences en génie logiciel
- Compétences personnelles pour les équipes d'ingénieurs
- Compétences en gestion
- Compétences en marketing
- Compétences pour les équipes en charge des ventes
- Compétences en gestion de produits
- Compétences en finance
- Projets de développement Android
- Projets TensorFlow et Keras
- Le Python pour tous
- Deep Learning
- Compétences Excel pour l'entreprise
- Bases de la gestion d'entreprise
- Apprentissage automatique
- Principes de base d'AWS
- Fondements de l'ingénierie des données
- Compétences d'analyste de données
- Compétences pour un concepteur UX

- Certificats MasterTrack®
- Certificats Professionnels
- Certificats d'université
- MBA & diplômes commerciaux
- Diplômes en science des données
- Diplômes en informatique
- Diplômes en analyse des données
- Diplômes de santé publique
- Diplômes en sciences sociales
- Diplômes en gestion
- Diplômes des meilleures universités européennes
- Maîtrises
- Licences
- Diplôme avec un Parcours de performance
- Cours de BSc
- Qu'est-ce qu'une licence ?
- Combien de temps dure un Master ?
- Un MBA en ligne vaut-il le coup ?
- 7 façons de payer ses études supérieures
- Voir tous les diplômes