Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn how to clean data with Apache Spark in Python.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Learn how to blend business, data, and AI, and set goals to drive success with an effectively scalable AI Strategy.
Explore ways to work with date and time data in SQL Server for time series analysis
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Learn how to build and test data engineering pipelines in Python using PySpark and Apache Airflow.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Learn how to make predictions from data with Apache Spark, using decision trees, logistic regression, linear regression, ensembles, and pipelines.
Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Gain a clear understanding of data privacy principles and how to implement privacy and security processes.
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn to conduct image analysis using Keras with Python by constructing, training, and evaluating convolutional neural networks.
Dive into the world of machine learning and discover how to design, train, and deploy end-to-end models.
Learn efficient techniques in pandas to optimize your Python code.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Learn how to analyze data with spreadsheets using functions such as SUM(), AVERAGE(), and VLOOKUP().
Unlock more advanced AI applications, like semantic search and recommendation engines, using OpenAI's embedding model!
In this course you will learn to fit hierarchical models with random effects.
Explore Large Language Models (LLMs) in business. Realize their value and where you can begin utilizing them today.
Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
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.
Build multiple-input and multiple-output deep learning models using Keras.
In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, and techniques to improve productivity.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
This course will show you how to integrate spatial data into your Python Data Science workflow.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
Learn the fundamentals of how to build conversational bots using rule-based systems as well as machine learning.
Create interactive data visualizations in Python using Plotly.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
In this interactive course, you’ll learn how to use functions for your Tableau calculations and when you should use them!
In this course, you'll learn the basics of relational databases and how to interact with them.
Learn to construct compelling and attractive visualizations that help communicate results efficiently and effectively.
Visualize seasonality, trends and other patterns in your time series data.
Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Sheets.
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.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Master SQL Server programming by learning to create, update, and execute functions and stored procedures.