Intermediate Python for Finance
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
Learn to model and predict stock data values using linear models, decision trees, random forests, and neural networks.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Learn to use Python for financial analysis using basic skills, including lists, data visualization, and arrays.
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 about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
In this course, you'll learn how to import and manage financial data in Python using various tools and sources.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
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.
Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Using Python and NumPy, learn the most fundamental financial concepts.
Discover how to use the income statement and balance sheet in Power BI
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 to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn the fundamentals of valuing stocks.
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.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
Discover how to make better business decisions by applying practical data frameworks—no coding required.
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Learn to use Google Sheets to clean, analyze, and draw insights from data. Discover how to sort, filter, and use VLOOKUP to combine data.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
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 cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.
In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.
Learn how to use conditional formatting with your data through built-in options and by creating custom formulas.
Learn to analyze data over time with this practical course on Time Series Analysis in Power BI. Work with real datasets & practice common techniques.
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Use OpenAI's powerful API to enrich and summarize stock market data.
Ready to analyze and visualize financial ratios? In this project, you will take on real-world challenges like evaluating the profitability and leverage of companies across industries.
Discover how the US bond yields behave using descriptive statistics and advanced modeling.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Help the bank monitoring their fraud detection model and figuring out why it's not performing as expected.
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.
Use your SQL skills to find out how many companies reached a valuation of over $1 billion across different industries between 2019 and 2021!