Introduction to Data Science for Finance: Python Course

JAN
11
Friday, 9:00 am - 5:00 pm
 
Location: Biltmore Court 
CFALA Member Center
520 S Grand Ave. Suite 655 
Los Angeles, CA 90071
 
Instructor: TBA
 
Time: 9:30 am - 1:00 pm: Morning Session
1:00 pm - 2:00 pm: Lunch
2:00 pm - 5:00 pm: Afternoon Session
 
Dress: Casual
   
 
Add to Calendar 01/11/2019 09:00 AM 01/11/2019 5:00 pM America/Los_Angeles Introduction to Data Science for Finance: Python Course 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 the 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. Biltmore Court CFALA Member Center 520 S Grand Ave. Suite 655 Los Angeles, CA 90071 CFALA info@cfala.org false MM/DD/YYYY
An Education Committee Sponsored Event

Overview

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 the 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.

Find out how investment professionals use investment knowledge to translate data science into business models and career growth.

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.

Course and Contact Information
Level: Beginner
Prerequisite: Introductory Python Programming Course
Duration: 1 Day
info@cognitir.com
+1 908 505 5991 (US); +44 75 0686 49 85 (UK); www.cognitir.com

Course Curriculum
  • Introduction to Data Science for Finance & Fintech
    • What is data science, why is it relevant to Finance & Fintech
  • The Data Science Process
    • Overview of CRISP­DM, what does each stage of the CRISP­DM process accomplish, presentation of common challenges, what should fintech professionals know about this process
  • 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
  • Clustering in Python for Finance & Fintech
    • Unsupervised modeling strategy
    • When to use clustering tasks
    • Measuring similarity
    • Overview and implementation of k­-means in Python
    • Improving k-­means and using similarity for predictive modeling
  • Applications of Data Science to Finance & Fintech Industries
  • 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 
Course Content Developers

David Haber | david@cognitir.com
David heads Cognitir's products and technology. He has led programming workshops at the undergraduate and graduate levels, at blue chip companies, and world renowned management consulting firms. David has experience working with both startups and large corporations. Previously, he was a lead software and machine learning engineer at Soma Analytics, an investor­backed and award­winning health­tech startup in London. David also worked on optimizing large­scale payment processing systems at Deutsche Bank in Singapore. Outside of Cognitir, he currently advises HiDoc, an early stage digital health startup in Germany. David holds an MEng (First­Class Honours) in Computer Science from Imperial College London (UK) where he focused on statistical machine learning. He presented his work at international conferences and won several awards for his work. During his studies, he also served as a teaching assistant at Imperial College where he helped undergraduate students master fundamental computer science concepts.

Neal Kumar | neal@cognitir.com
At Cognitir, Neal leads strategy and business development initiatives and advises on new product development. Outside of Cognitir, Neal consults C­level teams and senior business managers on a variety of strategic topics ranging from M&A to marketing. He also leads training seminars for Wall Street Prep and has consistently received top reviews from attendees and created two training courses that were used in seminars worldwide. Before his consulting and training careers, Neal taught secondary mathematics in St. Louis Public Schools (USA) as a Teach for America Corps Member. Prior to joining Teach For America, Neal worked in investment banking at JPMorgan and Houlihan Lokey. Neal received his MBA from London Business School (UK) and BBA in Finance from the University of Notre Dame (USA). He is also a CFA Charterholder and a Member of the CFA Institute Education Advisory Committee (EAC) Working Body where he helps shape CFA Program Content.
 
Early Registration Fees (Expires December 28th, 2018)
$399 (Members) | $499 (Non-Member)

Registration Fees
$499 (Members) | $599 (Non-Member)
 
Payment Information
We accept the following:

If you prefer to pay by check please email info@cfala.org and request to pay by check. Your registration will be completed automatically and you will receive an email confirmation.

Mail check to: 
CFA Society of Los Angeles, 520 S. Grand Ave, Suite 655, Los Angeles CA 90071.

*Credit card payments will only be accepted through the secure online registration, and not by phone or email.

Cancellations
Enrollee cancellations must be made in writing and received at least 5 business days before the first day of class. All cancellations will incur a $30.00 processing fee. If enrollment is canceled after the 5-day deadline, a 50% cancellation fee will be charged.
Chair: 
Rama Malladi, CFA
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