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See you soon,

Andy and the QuantNet team.
 
I plan on taking this course and the advanced C++ course after I finish the first C++ course. Between the python and advanced C++ course, any recommendation on which to take first, or is it situationally dependent?
 
I plan on taking this course and the advanced C++ course after I finish the first C++ course. Between the python and advanced C++ course, any recommendation on which to take first, or is it situationally dependent?
I'd say it depends on your situation. A few considerations:
  • The advanced C++ course will take you longer, and take up more of your time overall. So if you have more time now, it may make sense to take advanced C++ first. Conversely, if you will have more time in a few months, it may make sense to take Python first.
  • The advanced C++ course takes you from C++ proficient to C++ expert. Some people prefer to become an expert in one language before embarking on others, whereas other people prefer to be proficient in multiple languages before attempting to become an 'expert' in one, as the practical benefit of proficiency is greater than expertise.
 

Daniel Duffy

C++ author, trainer
C++ -> Python -> Advan C++ can be a good choice as well. depending on your goals.

I find 'switching' between the two languages useful.
Actually, the functional paradigm (tuple, variadic params, higher-order functions, decorators) in Python could be a great step-up to Adv C++11.
 
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There are some additional 'production' topics such as unit testing, design patterns, and code management (i.e. GIT) that are beyond the scope of the course, but may be presented as followup 'mini-modules' in the future (along with other topics).
What was the verdict on this? Will these be covered?

Looking at this recent job ad, for instance, it's clear those skills are now expected:
Cerberus Capital Management
Posted 2 weeks ago

As a Quantitative Analyst / Data Scientist, you will contribute to the firm’s objectives by designing, implementing and deploying quantitative models for a broad range of business objectives, such as asset pricing, demand forecasting, sentiment analysis, and other machine-learning techniques for pattern recognition and statistical modeling. You may also participate in due diligence analyses of future investments, or evaluate 3rd party solutions and cloud-based tools for client adoption.

Responsibilities:
  • Build predictive models using machine-learning techniques that generate data-driven insights on modern data platforms (Spark, Hadoop and other map-reduce tools);
  • Develop and productionalize containerized algos for deployment in hybrid cloud environments (GCP, Azure)
  • Connect and blend data from various data sources within enterprise tools (python, pandas, or SQL) to enable application of Data Science methods
  • Create metrics and analytical reports to ensure data quality and business value. Clean, structure and normalize data to eliminate redundant or unnecessary information to enable robust and sound analysis
  • Participate in the development of both back-end data pipelines and front-end applications
  • Generate analytical reports to track adherence of client processes to business strategy
  • Apply statistical methods to predict future client business outcomes
  • Participate in due diligence of investment proposals as a Data Science and Technology expert
  • Evaluate 3rd party solutions for functionality, quality and applicability to client use cases.


Requirements:
  • University degree in Mathematics, Engineering, Statistics, Computer Science or Physics. Advanced degree preferred but not required.
  • Solid knowledge of Linear Algebra, Probability Theory, Statistics and Optimization, including regression analysis, parameter estimation, factors selection, PCA, hypothesis testing, time series, queuing theory, survival analysis, clustering, linear programming.
  • Knowledge of machine learning methods, such as regularization, random forests, neural networks and deep learning.
  • Ability to write algorithms and implement pipelines in Python. Knowledge of Scala, R, is a plus.
  • Experienced in SQL. Familiarity with various relational database platforms is a plus (SQL Server, MySql, PostgreSQL, Oracle, Snowflake, Vertica, etc). Ability to write efficient and robust queries.
  • Familiarity with DevOps process for model deployment and unit testing.
  • Experience of work in cloud environments, especially MS Azure, is a plus.
  • Experience of work in collaborative development environment (GIT, Azure DevOps, JIRA).
  • Ability to present ideas and solutions in business-friendly and user-friendly language to colleagues, management and clients.
 
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What was the verdict on this? Will these be covered?
The course topics are quite comprehensive, but these topics fall beyond the scope. Per the above, they will likely be presented as followup(s) to the course in the future, presented as 'mini-modules'. These are discrete topics that would distract from a broad Python course, but are important enough that they deserve independent coverage.

The full syllabus of the course is here: https://quantnet.com/doc/Python-Course-Overview.pdf
There is an FAQ here as well: FAQ: Python Online Course
 

Daniel Duffy

C++ author, trainer
What was the verdict on this? Will these be covered?

Looking at this recent job ad, for instance, it's clear those skills are now expected:
'Responsibilities' are kind of on the job stuff, while "Requirements' corresponds to mathematical and financial skills.
Job ads sometimes mention product knowledge (e.g. Visual Basic 6.0) which do not have a long shelf life. The trick is to learn future-proof generic skills.
 
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