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Learn how to use Power BI for supply chain analytics in this case study. Create a make vs. buy analysis tool, calculate costs, and analyze production volumes.
Discover how the Pinecone vector database is revolutionizing AI application development!
Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
Learn how to efficiently collect and download data from any website using R.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
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.
This Power BI case study follows a real-world business use case where you will apply the concepts of ETL and visualization.
Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
Explore the Stanford Open Policing Project dataset and analyze the impact of gender on police behavior using pandas.
Learn how to work with streaming data using serverless technologies on AWS.
Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.
Learn how to write recursive queries and query hierarchical data structures.
Dive into our Tableau case study on supply chain analytics. Tackle shipment, inventory management, and dashboard creation to drive business improvements.
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
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 ensure clean data entry and build dynamic dashboards to display your marketing data.
Learn how to access financial data from local files as well as from internet sources.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Learn how to use Python to analyze customer churn and build a model to predict it.
Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.
Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Develop the skills you need to clean raw data and transform it into accurate insights.
From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Learn to write scripts that will catch and handle errors and control for multiple operations happening at once.
Learn to streamline your machine learning workflows with tidymodels.
