Advanced Python – Intro to Data Science for Business & Finance

• Prerequisites:
  Basic Python skills are necessary before beginning this data science course.

• Overview:
  7 hours of learning, split into 2 days
  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 including Generative AI. 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. Specific areas in finance where the data science skills acquired from this course can be effectively applied include: sentiment analysis, advanced time series analysis, risk management, real-time pricing and economic data analysis, customer segmentation analysis, and machine learning algorithm creation for financial technologies and Generative AI.
  This 7- hour course is split into two days.

• Course Offers:
  ● An overview of data science methods relevant to finance and fintech
  ● Explanation of the hype around data science, machine learning & big data
  ● Hands-on Python programming experience
  ● Understanding of effective data visualization techniques using Python
  ● Course notes, certificate of completion, and post-seminar email support for 1 year
  ● An engaging and practical training approach with a qualified instructor with relevant technical, business, and educational experiences

• Course Designed For:
  This course is relevant for professionals who want to gain a hands-on introduction to essential data science methods that are utilized in finance and fintech.

• Course Curriculum:
  ● Introduction to Data Science for Finance & Fintech ○ What is data science, why is it relevant to finance & fintech ○ Applications of data science to finance & fintech industries
  ● The Data Science Process ○ What does the data science process typically look like within an organization? ○ Overview of the main steps ○ Pitfalls & recommendations
  ● Overview of the Most Common Data Science Methods ○ Supervised vs. unsupervised learning
  ● Classification in Python for Finance & Fintech ○ When to use classification tasks ○ Overview and implementation of decision tree classification in Python to obtain better customer insights ○ 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 ○ When to use clustering tasks ○ Overview and implementation of k-means clustering in Python to understand stock data and optimize portfolios ○ 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

• Date: Wednesday, October 29th & Thursday, October 30th
  Location: Virtual, Zoom based
  Course Hours: 5:30 – 9:00 pm EDT, each day
  Early Bird Registration & Deadline: Members- $419, Non-Members – $519 until September 29, 2025
  Registration Closes: October 24, 2025 COB

 Cognitir website 

REGISTER