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.
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.!
by Abhishek k G•
by Wang J•
by Tin C L•
by SHIBAM C•
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by Siying C•
by Amlan B•
by Sankhadip J•
by SUKHEN D•
by Zhu, T•
by Xiaobing C•
by Глеб В•
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.
by Jitendra D S•
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
Not for beginners, but very condensed and a good summary if you know these already.
The course contains very condensed information which combines: statistical inference methods, intermediate python language and evaluation methods of trading strategies.
I would not recommend it if you have not done at least two of three: a) Completed basic statistics course b) Completed a beginner to python programming course c) Understand the basics of trading, creating and evaluating trading strategies (sharpe ratios, overfitting etc).
For me it was a pleasure to see such information condensed, as I've refreshed my econometrics (ie statistical inference methods) knowledge, I can use the code to create my own variations of strategies and dig deeper to testing and training of trading models.
But overall I would struggle if I would be missing knowledge, as every single word from the professor has a very specific reason to be there. Every words matters and is used to create a solid line of logic.
English could be better, but I don't care about that. All was clear to me.
by Justin Y•
Learned a lot, however, it gets complicated really quickly. My intention for this course was to learn more about how Python is used in business analysis as this is something I am planning to do for college. I feel that it would be greatly appreciated if even basic statistics tools would be expounded more on as I did not really know these and it took a toll on my progress in this course. I would also appreciate if the labs were more hands-on, meaning that we would be the ones to build the code and applying our understanding of the videos. Although in certain labs this is done, some of the labs would just let me run the code that was already pre-typed. I think that allowing students to apply their understanding would allow them to remember more the lessons provided by the course.
I learned a lot and I greatly appreciate Mr. Wan for creating this course as it will certainly help me in the future.
by Claudio H•
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.
by Kushagra S•
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.
by Brandon B•
This course shares a lot of info on how to use statistical analysis formulas like RMS, p-value, std. deviation, etc., and how to apply this knowledge using data modeling in a really easy way. There are some small hurdles to get over when taking the quizzes as some of the answers can be interpreted in multiple ways. out of the 4 quizzes I took, i attempted at least all of them 2 to 3 times. Not sure if I failed to absorb the knowledge well or if the goal was to go back and review the course material with a finer comb, either way, I found the course helpful and useful. I'd recommend it to friends and colleagues.
by Matthias H•
Good for what it intends to provide, namely a quick introduction to the topic, but it doesn't go very deep.
It is slightly annoying that there are plenty of typos and grammatical mistakes all over the Python code and the quizzes, which could easily have been avoided if either the author had somebody proofread everything quickly, or if Coursera had any type of quality control.
Nevertheless, coming from another programming language, I did get out of this course what I wanted, namely a collection of all the basic Python commands for this kind of analysis. So thank you for providing this course!
by JY M•
In general a satisfactory course and not too to follow through. It is focused more on the stat side than finance which I kinda have a mixed feeling toward. Professor could probably have done a little better job on explaining the meanings behind the formula but for the most part it is not hard to figure it out yourself by searching or reviewing the materials a few times by oneself. I also feel this course is a bit short, and if in the future it can try to cover more topics that will be awesome.
But hey I did learn stuff and am happy to have taken this.
First of all, I want to thank professor Xuhu Wan, as i have benefited a lot from the course, which not only broadens my knowledge of statistics, but also helps me familiarize with practical application of python. Overall, this is a well-organized, inspiring course worth recommendation, although it would be better if the pace is a bit slower and the explanation of the code is more detailed and thorough. I believe this course helps build my python skills in financial analysis, and hopefully this experience can help me earn my PHD offer.
by Masaki S•
This is an awesome course which takes you through the statistics for the financial analysis. The course needs some update to correct some broken links, inconsistencies. It requires some basic knowledge of statistics and python programming beforehand or study of these topics alongside this course, which should be made obvious to some learners who may be puzzled (I see in the forum that several learners were quite upset about some difference in expectation vs the reality which I think could be narrowed down).
by Gonzalo A j•
Es interesante, muchos comandos y cuestiones teóricas de los últimos temas se explican rápido y sin profundizar. He aprendido y revisado conceptos que ya sabía. Me queda la duda de como afecta el margen de la oferta y demanda al proceso de evaluar estrategias financieras. El uso de Python simplemente es una herramienta para explicar conceptos, no se aprende realmente a programar, aun así en la mayoría de casos es fácilmente entendible ya que todo sigue un razonamiento lógico.
by camillo s•
The course was indeed helpful for my main goal to improve my skills using Python libraries to carry out mathematical / statistical caclulations.
One minor issue:
As I downloaded the notebooks for replaying them in my local Jupyter installation which is based on Python >= 3.6, I had to manually correct some statements due to changes in pandas, e.g.
pd.DataFrame.from_csv -> pd.read:csv or
pandas.tools.plotting -> pandas.plotting
mho it would be good to check for such issues