Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
This course introduces Python for financial analysis.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Sheets.
Learn how to build a graphical dashboard with spreadsheets to track the performance of financial securities.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Using Python and NumPy, learn the most fundamental financial concepts.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn how to access financial data from local files as well as from internet sources.
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Learn the fundamentals of valuing stocks.
Learn how to build an amortization dashboard in spreadsheets with financial and conditional formulas.
Learn how to price options contracts and visualize payout of various options strategies using spreadsheets.
Learn how to use the industry-standard pandas library to import, build, and manipulate DataFrames.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
In this course you'll learn the basics of analyzing time series data.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Discover how the US bond yields behave using descriptive statistics and advanced modeling.
Build a machine learning model to predict if a credit card application will get approved.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Explore the salary potential of college majors with a k-means cluster analysis.