Вернуться к Introduction to Portfolio Construction and Analysis with Python

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Оценки: 1,198

The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language.
This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way. We'll start with the very basics of risk and return and quickly progress to cover a range of topics including several Nobel Prize winning concepts. We'll cover some of the most popular practical techniques in modern, state of the art investment management and portfolio construction.
As we cover the theory and math in lecture videos, we'll also implement the concepts in Python, and you'll be able to code along with us so that you have a deep and practical understanding of how those methods work. By the time you are done, not only will you have a foundational understanding of modern computational methods in investment management, you'll have practical mastery in the implementation of those methods....

MA

29 нояб. 2020 г.

Awesome Course, Great instructors that give a nice balance of theory and practice, the practice is very hands-on and will help you understand the theory and give you useful tools from the first week.

SW

5 авг. 2020 г.

I am self taught in Python and have an industry background in Finance. This course was a good connector/provided additional insight on using Python to process portfolio performance and data analysis.

Фильтр по:

автор: Eduardo d M R

•17 нояб. 2019 г.

Risk Kit code needs revision from instructors! Also, it would be nice to have some feedback regarding the graded quizzes and a bit more presence on the forum. Overall, a very nice course to be introduced into Portfolio Management: would recommend with caution, nonetheless.

автор: Yana S

•10 окт. 2020 г.

The course definitely needs additional background in statistics and finance before start. For me it was difficult to understand the formulas and I had to read additional information to keep up. There is too mush learning for each week for me. 6-8 hours for a week are either the whole working day or 1 hour per day. Usually I was able to finish everything for a course during weekend but not for this course.

автор: Slake D M

•17 июня 2020 г.

Very good curriculum but you need to be comfortable with investment management concepts and even have an understanding of how Python works otherwise you can find that the lab sessions are a bit challenging. I will definitely recommend it to anyone interested in using Python for financial analysis

автор: Ernesto M

•14 апр. 2021 г.

Excellent course. Amazing and concise theory explanations and extended, full guided Lab sessions.

At the beginning I did not notice that the Phyton resources were available so I typed all the first 3 Lab's sessions. However it was a very good experience for me to starting from scratch diving into Python ( a challenge for someone coming from Assembler, Basic, Fortran, Pascal...). I discovered later on the forum, that the lab's resources were also available as well so in the 4th part/week I utilized them.

Very good and clear pronunciation for both who are not english language natives, that helps a lot to easy understand the finance concepts and how to code them into Python.

The LAB professor Vijay Vaidyanathan, is incredible good teacher, with patience and timing starting Python from "0" to reach little by little a good level.

Highly recommended course, I will continue with the specialization next week!

автор: carlos j u

•6 сент. 2020 г.

Great course!

The structure is of (1): compact, rigurous and intuitive explanations of theoretical concepts, and (2) extensive lab sessions where proffessinal Python code is developed on JupyterLab (and explained on-the-fly) that implements the concepts explained in the "lectures". Often extending them.

Emphasis is made in understanding well-written code, not in developing the math behind. The student gradually builds up their own Python module so that the code can be reused many times along the course, where needed. Moreover, the ipywidgets library is explained and used to facilitate the understanding, through interactive visualizations, of the parameters involved in many simulations.

автор: SMRUTI R D

•9 мая 2021 г.

This is one of the finest courses on Portfolio Management I have ever undertaken under Coursera. Starting from basic concepts, calculation of different portfolio statistics it took us to fairly advanced topics like construction of duration matched portfolio, Monte Carlo Simulation, Risk Budgeting etc in a well defined learning curve which was not very difficult to negotiate. It also imparted instructions on coding in Python to achieve all these objectives. In fact, LAB sessions were extremely interesting, helpful in understanding concepts and honed our coding skills in Python and Pandas. My sincere thanks to the faculties and all those in background for such a productive program.

автор: Yoann C

•24 нояб. 2020 г.

Very high quality! Thank you very much for sharing such an insightful content! Both teachers are very good. The labs are awesome, it helps a lot to understand the formulas and to apply to real data! The first two weeks are easy to follow, while the third is a bit harder and the the last definitely very dense. Those two last weeks require a lot more time investment than the two others. At the end of the lecture, I still have some code that does not work consistently, but hey, it's a good debugging exercise isn't it? :) Thanks for all.

автор: Henry

•9 февр. 2020 г.

Both two instructors are really good, patient, detail-oriented, and knowledgeable. This course is quite useful for you to gain skills in Python and its associated popular packages such as Numpy, Pandas, and Seaborn. Portfolio strategies such as CPPI and LDI are quite important and are introduced fully here. The course however takes a bit long time to finish, if you really want to make the most of it, because you must follow the long-hours labs. But I think it is worthy to spend such great amount of time.

автор: Hubert K

•9 апр. 2021 г.

Very quick and well-structured. The course does say beginner I believe, but I recommend to do some background reading beforehand perhaps. This could be on the monte carlo simulation or the statistics involved, not only is it interesting in other scenarios, but allows you to be familiar and more cognisant of the behaviour of the monte carlo simulation and brownian motion for example. Overall, I would recommend this course for anyone who wishes to find out more about portfolio construction and analysis!

автор: Chan C M

•27 июня 2020 г.

I like the layout of this course and the way VJ laying out his mistakes. Really help a lot for my programming and I am no longer afraid of errors. I also like how the instructor taught about liability-driven investing and how they combine the theory with the lab sessions.

One suggestion is that the course materials can be designed to teach students how they can come up with the functions. I perfectly understand how the function works but I could never come up with the functions myself.

автор: ALI R

•13 мая 2021 г.

The course has a very rich context in terms of portfolio construction/analysis and python programming, combined together, they give you a very powerful tool to do what you should do in real life, in a very short amount of time. It is a completely practical course and not a bunch of useless theories! I definitely recommend it to everybody who is interested in quantitative portfolio management, either institutional or retailer. Lionel and Vijay, you are both incredible! Thank you a lot!

автор: Ye A M

•23 авг. 2020 г.

I have taken quite a number of Python courses(for the field of Finance) and none of the previous courses greatly improved my understanding of Python. This course, however, leaves me with nothing but a desire to dive deeper into the beauty of Python and Finance. A million thanks to both instructors for making this learning process enjoyable and interesting. 5 stars for today, for tomorrow, for as long as this course illuminates a clearer path for aspiring financial analysts!

автор: Rama M

•20 дек. 2020 г.

This course is well-organized by the instructors, Dr. Vaidyanathan and Dr. Martellini, for a virtual learning environment.

This course is suitable for three types of audience:

a) required prep for anyone considering a Masters in Financial Engineering or MS in Finance Programs.

b) investment professionals who currently use Excel/VBA, R, or MATLAB , but interested in learning python programming techniques.

c) finance undergraduate students who want to be quants.

автор: Runar A Ø

•11 дек. 2019 г.

This course is very well presented and gives the reader the necessary practical skills. After short theoretical videos, most of the time is spent implementing the models in Python, and gradually building a module of functions used for constructing portfolios and visualising the data. Some of the Python code is not completely elementary so I would advice you to spend a bit more time and code everything yourself line by line and understand what's going on.

автор: GAUTHIER P

•1 мая 2020 г.

Very useful course with state of the art financial theories as well as programming practical use. I learned a lot both in finance (especially Chapter 3 & 4) and in Python in an easy and agreable way. I will be implementing a CCPI strategy in my personal portfolio soon thanks to the very useful edhec risk kit developed during all the session labs. This format is well made and makes it really fun to try and test the code for ourselves, which is rare!

автор: Beau D

•9 июля 2020 г.

Very educational and challenging course, combining the development of fundamental skills in Python with Investment management practices. When you stumble upon a coding problem, you can be stuck for a while but mostly the forum provides an answer, atlhough some research on the internet is also recommended. Really looking forward to learn about the advanced courses in the specialization. Vijay and Lionel were very pleasant and interesting tutors!

автор: Arnaud R

•6 авг. 2020 г.

Extremely high quality course with two great instructors.

I would however point out that this might be (very) tough for people without any finance background. The theoretical aspects are explained on a high level only, which may make it difficult for some people to fully grasp what is being done.

I started with only basics in Python and was able to understand all the code. The Lab Sessions with Vijay are very good.

I highly recommend this course.

автор: Rajagopal S

•18 окт. 2020 г.

I am a novice, an individual investor who was intrigued by the complexities of portfolio construction and risk management, This course has helped me grasp the basics and learn the subject with ease. The main highlight is an excellent combination of theoretical concepts and practical lab sessions, which made the learning and the overall program very enjoyable - looking forward to starting the next course on Advance portfolio construction.

автор: Vadim T

•11 мая 2020 г.

Great course!

Vijay is one of the best professor that I have had pleasure to listen to. He does not only explain coding, but also unwraps the ideas which make financial predictions and simulations possible.

Quizzes are very easy, though, and are based on the code developed in class. I would appreciate more challenging assignments.

Nonetheless, I learned a lot and I am very excited about the next class in this specialization.

автор: Francisco S

•9 февр. 2020 г.

The course is a great balance between theory and application. The content overall is great. I really enjoyed the emphasis on python implementations of the concepts taught. It is rare to find a course that has good theory AND good coding practices for data analysis. The instructor makes use of the libraries commonly used in industry (I work as a data scientist in a US startup). Highly recommended!

автор: Rehan I

•30 мар. 2020 г.

This was an amazing course! The lab sessions are the key - I feel I will have a hard skill to take away from this course and code I can re-use in my career. Vijay in particular is an excellent teacher and makes sure you understand every step of the code. Where I didn't understand something, I posted in the discussion forums and got a really prompt reply that cleared things up. Thank you.

автор: Alfredo H

•12 апр. 2020 г.

Excellent Course! At the same time, I have a Masters in Finance there where additional topics that I had not covered in such detail in the past while introducing fundamental skills for python in finance in detail. I would suggest to anyone taking this course to have a strong fundamental in finance already as some of the topics can be confusing if you have not seen it in the past.

автор: Daniel T O

•27 янв. 2020 г.

There is such an incredible amount of useful information in the course. The information is paired with exceptional professors who communicate it extremely effectively. The lab sessions in this course are incredible and serve as a guide to implement techniques learned in the course (and to build on them) on new datasets. Thank you so much for putting this course on Coursera!

автор: Vittorio P

•19 окт. 2020 г.

Very Helpful. I studied most of the course arguments during an MSc in Finance, but I had not the knowledge to apply them in practice. This course helped me reviewing the basic mechanisms of financial analysis and portfolio construction with real data and eventually gave me a framework to apply them practically with python, which is the real value-added of these lessons.

автор: BAILLY

•26 апр. 2022 г.

This course of a good mix between theory and practice in Python.

The real market data files enable to illustrate the concepts to better understand their meaning.

The Python code provided by the EDHEC to handle the data, compute key indicators, display plots are very meaning ful.

The introduction to simulation is powerful to manipulate new data and test hypothesis.

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