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 two-day, hands-on course provides a structured teaching environment where attendees learn the Python programming language as a powerful tool to conduct robust data analyses on finance-related data sets. At the end of the workshop, course participants will have applied the Python programming language and essential data analysis techniques to practical programming exercises to gain experience solving challenging finance-related 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.
No prior programming experience required.
Learning Objectives:
- Learn basic foundations in programming and data science
- Receive an overview of state-of-the-art data science and machine learning methods
- Discover how finance professionals can use data science to solve real-world problems
- Understand the advantages of data science and specific analytical methods
- Obtain hands-on Python programming experience
- Understand effective data visualization techniques using Python
- Ability to hit the ground running with executing core data tasks by the end of the course
Who Should Attend?
- Investment professionals
- Quantitative traders
- Event-driven fund managers
- PE/VC investors
- Traditional asset managers
- Fintech entrepreneurs and/or product managers
- FP&A and strategy professionals at corporations
- PE portfolio company operators
- Private wealth managers
- Economists
- IT and tech professionals at financial institutions
- Management consultants
Course Sections:
Introduction to Data Science for Finance
- What is data science, why data science is so important, which questions data science can answer
Hands-on Introduction to Python Programming for Data Science
- Why use Python for data science, how do we write programs in Python, syntax, variables, conditionals & control flow, data structures, loops, functions, modules, objects & classes
Financial Time Series Analysis in Python
- Reading in data, plotting, resampling, data slicing, computing returns, computing descriptive statistics, moving window functions, computing Bollinger Bands, computing stock correlations, loading financial data from the Internet
Linear Regression for Finance
- What is regression, what finance questions can regression help us answer, what are the different types of regression, how do we use Python to solve regression tasks, how can we assess the quality of our results, what is overfitting & how can we avoid it
Data Science Problem Solving Process
- What is data mining, overview of data science methods, data science problem solving process, differences between supervised and unsupervised tasks
Visualization of Data Science Results
- How Python is used to produce easy-to-understand and convincing visualizations that can be used for presentations and during meetings
Clustering of Financial Data
- Unsupervised modeling, when to use clustering, what is similarity and how do we measure it, intuition behind k-means, how to implement k-means clustering in Python, how to improve your clustering model, using similarity for predictive modeling (classification)
Applications of Data Science to Finance and Economics Data
Big Data 101
- What is Big Data, why is Big Data relevant, how does Big Data relate to the concepts taught in this course, overview of most common Big Data technologies
Please Note: Participants will need to bring a laptop with them to the course.
