Back to Python and Statistics for Financial Analysis

4.6

234 ratings

•

40 reviews

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....

Jan 21, 2019

Perfect for the beginning to intermediate python programmer who wants to utilize finance data to make decisions (i.e. trading).

Jun 09, 2019

An interactive and succinct course to get an insight into the statistical analysis used in the finance domain on daily basis.

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By Cheuk W K

•Mar 01, 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.

By Helena K

•Feb 08, 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!

By carlo

•Mar 23, 2019

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By Satish N

•Feb 28, 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.

By sabarinathan r

•Feb 07, 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

By Ezekiel J T

•Feb 05, 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.

By Zeyu H

•Jan 20, 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.)

By SIDIBE A B

•Jul 21, 2019

very interested but the exercice are little easy and does not help to look for at home

By Teren D

•Jul 12, 2019

This is a good start to introducing python in a stock market context. Hopefully there can be a continuation of it.

By Chan W W

•Jul 07, 2019

Great fundamental course provided by Prof Xuhu WAN. After finishing the course, I am appreciated that he put lots of good efforts in the training materials. All concepts are delivered with clear examples! Highly recommend to take this course. Thank you very much.

By TJ D

•Jul 05, 2019

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

By Đan T L

•Jul 04, 2019

Interesting and easy to understand for people with basic background or have basic knowledge about finance or statistic. However, I wish some of the videos may have explained more about how to use the data to solve real life issues. Even though some of the practices may explore it, it appears not deep enough for me

By PUREUM W

•Jun 30, 2019

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.

By Krzysztof P

•Jun 29, 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.

By Tushar G

•Jun 09, 2019

An interactive and succinct course to get an insight into the statistical analysis used in the finance domain on daily basis.

By 裴品傑

•Jun 05, 2019

很不錯，但最後的回歸有點難

By DONG C

•May 09, 2019

well illustrated and practical skills

By Steve R

•May 06, 2019

Associate Professor Xuhu Wan of HKUST ensures that a student learns both the python programming to build predictive models and the concepts of the models. To build your applied financial analysis skill set, this high caliber course ties together python programming practice with statistics.

By Torres M

•May 06, 2019

Me gusto

Es un curso muy completo

By Boudokhane M

•May 01, 2019

This course was really enjoyable : well structured, a likeable professor and very useful and illustrative exercises. the use of Jupyter notebooks was also a very good idea.

By Steven D

•May 01, 2019

I learned a lot about how to implement financial statistics in Python and some added knowledge on statistics. Great Course.

By Aria Z

•Apr 23, 2019

pros

(1) h

By Jing H

•Apr 16, 2019

Clear instruction and very useful python code for applied case study.

By Ren J

•Apr 11, 2019

Clear explanation of the statistics and python, well-prepared exercise in notebook and basic bags in python are recommended to use in data processing.

By ducvannguyen

•Apr 01, 2019

Thank you very much for this useful course. I hope to join many course from you

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