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 Advanced Data Science & Machine Learning with Python for Finance

​Course Goals and Overview:

This hands-on data science course is a sequel to the Introduction to Data Science & Python for Finance workshop. This course will provide an overview of modern machine learning algorithms that analysts, portfolio managers, traders and chief investment officers should understand and in a context that goes beyond a broader level introductory class in data science. Classification methods are touched upon in the introduction course, but the Advanced Data Science & Machine Learning with Python for Finance course focuses exclusively on this highly demanded and rapidly adopted segment of data science and machine learning.

This course will explore advanced classification methods in​​cluding neural networks and decision trees which are among the most effective data science techniques. This workshop also provides an introduction to deep learning, a technique which has significantly increased the performance of machine learning algorithms over the last years and is heavily used in the financial services industry. Deep learning utilizes algorithms and methods that perform in a similar manner to the human brain. According to Gartner, 80% of data scientists will be competent in deep learning and deep learning will be utilized in a much larger role in different forms of predictive analytics across all functional areas of business including finance and markets.

At the end of the workshop, participants will be comfortable applying the Python programming language to build common classification algorithms and evaluate & interpret their accuracies in the context of finance.

Learning Objectives:

  • An overview and specific focus on core classification methods and how to use them to solve real-world problems in the finance industry.
  • Aims to provide attendees with a high level understanding and working knowledge of highly coveted artificial intelligence areas including deep learning and neural networks and their direct application to the field of financial analysis and capital markets.
  • Provide attendees that work in the finance industry with the ability to evaluate and select from a variety of classification methods and tools as these techniques continue to be adapted and implemented at an ever increasing rate.
  • Further and more advanced hands-on Python programming experience beyond the introduction course.

Who Should Attend:

  • Individuals working with or needing to understand machine-learning algorithms, specifically classification methods.
  • Graduates of the Introduction to Data Science for Finance course. The introduction course or equivalent is a prerequisite.

Course Sections:

Review of Core Data Science Methods

  • Supervised vs. Unsupervised learning, Classification, Regression, Clustering, Dimensionality Reduction, Ensemble, etc.

Selecting Informative Attributes

  • Information gain and entropy, overfitting/generalization

Decision Trees &  Random Forests

  • What is it?
  • How to do this in Python
  • Coding Challenge

K-Nearest Neighbors

  • What is it
  • How to do this in Python 
  • K-Nearest Neighbors Coding Challenge

Support Vector Machines

  • What are they?
  • How to do this in Python - example
  • SVM Coding Challenge

Neural Networks

  • What is it
  • How to use this in Python with an example
  • Neural Nets Coding Challenge 

Deep Learning

  • Why the hype?
  • How to get started with deep learning

Evaluation of Classification Methods

  • Accuracy, confusion matrix. ROC, AUC, Precision, Recall, etc.

Final Project

  • Given a dataset and a classification mandate, attendees have to run these different classification models and figure out which one is "best"

What is Needed from You:

  • Participants will need to bring a laptop with them to the course.
  • It is recommended that participants have previously taken Introduction to Data Science for Finance as a prerequisite. If you have not been able to take this course, please contact the instructors at info@cognitir.com

 Registration Fees

​CFAW Members: $499.00

Non-Members: $599.00

Currency: U.S. Dollar ($)

 Dates and Location

 
June 26, 2019

 
Location & Registration 
Coming Soon

 Discounts & Refunds

Group Discount: Organizations are eligible for a 10% discount when registering three or more people for an event. All registrations must be received at the same time. Email all discount registrations together to events@cfawashington.org

Cancellations, Substitution, and Refund Requests

 
  • Substitutions are welcome at any time.
  • Cancelations must be made in writing and emailed to events@cfawashington.org on or before 5 business days prior to the event.
  • No refunds will be processed after 5 pm ET, 5 business days prior to the event. 
  • A $25 administrative fee will be applied to all cancellations.​
  • Refunds will not be issued for no shows