• Data Science:

    R Programming Basics for Data Science

    Build a foundation in R: wrangle, analyze, and visualize data.
    Topics:
    ⮚ Basic R syntax
    ⮚ Foundational R programming concepts: data types, vectors arithmetic, indexing
    ⮚ Operations in R: sorting, dplyr data wrangling, making plots

    Data Science: Linear Regression

    Learn how to implement linear regression, one of the most common statistical modeling approaches in data science.
    Topics:
    ⮚ Theoretical basics of linear regression
    ⮚ Understanding confounding and how to detect it
    ⮚ Learn how to examine relationships between variables by implementing linear regression in R

    Data Science: Wrangling

    The steps that convert data from its raw form to the tidy form for analysis is called data wrangling.
    Topics:
    ⮚ Import different data formats into R.
    ⮚ Web scraping
    ⮚ R tidyverse to tidy data for analysis
    ⮚ String processing with regex
    ⮚ Wrangling data using dplyr
    ⮚ Text mining

    Probability and Statistics in Data Science

    SQL For Data Science

    Introduction to Computational Thinking and Data Science

    Data Science: Inference & Modeling

    Visualizing Data with R

    Introduction to Data Science and Machine Learning

    Data and AI Fundamentals

    Big Data Analytics Using Spark

    Data Science Productivity Tools

    Reproducible reports using GitHub, git, Unix/Linux, and RStudio.
    Topics:
    ⮚ keeping file system organized.
    ⮚ version control systems git; keep track of changes in your scripts and reports;
    ⮚ GitHub; repository for collaborations.
    ⮚ RStudio and Rmarkdown; reproducible reports.