Data science courses

Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.

Learn about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.

Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.

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 to implement custom trading strategies in Python, backtest them, and evaluate their performance!

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

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.

In this course, youll learn how to import and manage financial data in Python using various tools and sources.

Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.

Discover how to use the income statement and balance sheet in Power BI

In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.

Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.

You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.

Learn how to access financial data from local files as well as from internet sources.

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

You will use Net Revenue Management techniques in Google Sheets for a Fast Moving Consumer Goods company.