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Event Registration - Introduction To Data Science For Finance
June 15, 2018
8:00 AM - 5:00 PM Eastern Daylight Time
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Introduction to Data Science for Finance
The amount of data available to organizations and individuals is unprecedented. Financial services sectors, including securities & investment services and banking, have the most digital data stored per firm on average. As a result, financial companies have been on an innovation and technology push to create new, disruptive technologies that can maximize use of these data assets to solve some of the industry’s toughest problems. This one-day, hands-on course provides a structured teaching environment where students learn classic data science methods, which are used as the bases for many financial technologies. At the end of the workshop, course participants will have applied the Python programming language and essential data science techniques to solve complex finance problems.
What This Course Offers
An overview of data science methods relevant to finance and fintech
Hands-on Python programming experience
Understanding of effective data visualization techniques using Python
Course notes, certificate of completion, and post-seminar email support for 3 months
An engaging and practical training approach with a qualified instructor with relevant technical, business, and educational experiences
A Computer Science 101 pre-course webinar
Who Is This For This course is relevant for students and professionals who want to gain a hands-on introduction to essential data science methods that are utilized in finance and fintech.
Please note that you must have taken an introductory Python programming course before attending this workshop. Cognitir will recommend a free, online Python course to participants, but this online course must be completed before the start of this data science workshop.
Introduction to Data Science for Finance & Fintech
What is data science, why is it relevant to Finance & Fintech
The Data Science Process
Overview of CRISP-DM, what does each stage of the CRISP-DM process
accomplish, presentation of common challenges, what should fintech
professionals know about this process
Applications of Data Science to Finance & Fintech Industries
Classification in Python for Finance & Fintech
When to use classification tasks
Overview and implementation of Naïve Bayes classification in Python
Evaluation of classification tasks using accuracy, confusion matrices,
expected value, etc.
Visualization classification tasks using profit curves, ROC curves, AUC, etc.
Selecting informative attributes via information gain and entropy analyses
Overview of Other Common Data Science Methods
Supervised vs. Unsupervised learning
Clustering in Python for Finance & Fintech
Unsupervised modeling strategy
When to use clustering tasks
Overview and implementation of k-means in Python
Improving k-means and using similarity for predictive modeling
Big Data for Finance
What is Big Data and why is Big Data relevant to Finance & Fintech
How does Big Data relate to the concepts taught in this course
Overview of most common Big Data technologies
Wrap-Up and Summary
MCLE New England
4751, 10 Winter Pl
Boston, MA 02208
Non- Members: $499
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