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Вернуться к Python and Machine-Learning for Asset Management with Alternative Data Sets

Отзывы учащихся о курсе Python and Machine-Learning for Asset Management with Alternative Data Sets от партнера ????? ??????? EDHEC

4.6
звезд
Оценки: 31
Рецензии: 9

О курсе

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills....

Лучшие рецензии

AT

Mar 06, 2020

really interesting applications and good examples. More breadth than depth but a great guide as to what the state of the art is in applying machine learning to more alternative forms of data.

R

Mar 06, 2020

Excellent view into modern financial research in the use of alternative data sets including valuable demonstration in implementation.

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1–10 из 10 отзывов о курсе Python and Machine-Learning for Asset Management with Alternative Data Sets

автор: Loc N

Jan 04, 2020

Way better than the third course in the Specialization. If I have to rank the courses in terms of the organization from high to low, the ranking would be: the first course, this course, the second course, and the final course.

автор: Fabien N

Feb 06, 2020

Amazing course ! I had been a bit disappointed by Course 3 of the Specialization, but this Course 4 clearly paid back ! The 3-sections structure for each week is really great, the theory is well explained and the lab sessions are very clear, this allows us to really grasp the concepts and be able to use them in the future. In addition, the research application sections greatly open the applications to advanced studies and increase curiosity for the topic. Congrats ! It's one of the best MOOC I had to follow!

автор: Runar A Ø

Mar 06, 2020

Excellent view into modern financial research in the use of alternative data sets including valuable demonstration in implementation.

автор: Dirk W

Feb 05, 2020

Very well-constructed course, right balance between theory, lab sessions and application. Theory to the point. Lab sessions largely detailed, which is really a forte. Really interesting readings in the application section. Quizzes adapted to the theory, lab sessions and application. No technical issues.

автор: Alex T

Mar 06, 2020

really interesting applications and good examples. More breadth than depth but a great guide as to what the state of the art is in applying machine learning to more alternative forms of data.

автор: Michinori K

Feb 20, 2020

Great course! Highly relevant and including latest research topics.

Both lectures and labs are very efficient in delivering state-of-the-art contents.

автор: Konstantinos R

Dec 01, 2019

Different from the other 3 courses but extremely interesting

автор: Robert N

Dec 21, 2019

Interesting and very useful!

автор: Kevin W

Apr 02, 2020

Great material and knowledgeable lecturers.

However, the Lab sessions aren't relevant to completing most quizzes. So to get more out of the course the student must play with the code outside the context of the class. The disconnect between the two seems like a missed opportunity to force students to look objectively at the Labs and its application.

автор: Andrea C

Jan 16, 2020

theory and lab not really synced. Lab not adding lots of value.