Course Goals and 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. Finance companies that want to maximize use of this available data require professionals who have a keen understanding of data science and know how to use it to solve meaningful business challenges.
This is a one day course, split over two sessions of 3.5 hours. This 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.
Specific area in finance where 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.
Learning Objectives:
- 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 3 months
- An engaging and practical training approach with a qualified instructor with relevant technical, business, and educational experiences
Who Should Attend: 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.
Prerequsities: You must have taken Cognitir's Introduction to Python for Business and Finance course before attending this workshop. Alternatively, you can take a free online Python course offered by a third party to get up to speed. Cognitir will provide a link to this prerequisite course.
Course Sections:
Introduction to Data Science for Finance & Fintech
- What is data science, why is it relevant to Finance & Fintech
- Explanation of the hype around data science, machine learning & big data
The Data Science Process
- How 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
Please Note: Participants will need to bring a laptop with them to the course.
This Virtual Course is Instructed by our training partner, Cognitir.
