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  • Reports in Power BI
    Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.
  • Winning a Kaggle Competition in Python
    Learn how to approach and win competitions on Kaggle.
  • Modeling with Data in the Tidyverse
    Explore Linear Regression in a tidy framework.
  • Structural Equation Modeling with lavaan in R
    Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
  • Hierarchical and Recursive Queries in SQL Server
    Learn how to write recursive queries and query hierarchical data structures.
  • Options Trading in Spreadsheets
    Learn how to price options contracts and visualize payout of various options strategies using spreadsheets.
  • Hierarchical and Mixed Effects Models in R
    In this course you will learn to fit hierarchical models with random effects.
  • Statistical Techniques in Tableau
    Take your reporting skills to the next level with Tableau’s built-in statistical functions.
  • Web Scraping in R
    Learn how to efficiently collect and download data from any website using R.
  • Building Recommendation Engines in Python
    Learn to build recommendation engines in Python using machine learning techniques.
  • Machine Learning in the Tidyverse
    Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
  • Deploying and Maintaining Assets in Power BI
    Learn how to deploy and maintain assets in Power BI. You’ll get to grips with the Power BI Service interface and key elements in it like workspaces.
  • Factor Analysis in R
    Explore latent variables, such as personality using exploratory and confirmatory factor analyses.
  • Linear Algebra for Data Science in R
    This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
  • Programming with dplyr
    Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.
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