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Rav Ahuja, Yan Luo, Saishruthi Swaminathan, Tiffany Zhu, Yiwen Li, Gabriela de Queiroz, and Jeff Grossman

Data Science and Data Analytics skills are in high demand and R is the programming language of choice for many data professionals. This Applied Data Science with R program emphasizes a hands-on approach to developing job-ready skills for analyzing and visualizing data using R.

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Data Science and Data Analytics skills are in high demand and R is the programming language of choice for many data professionals. This Applied Data Science with R program emphasizes a hands-on approach to developing job-ready skills for analyzing and visualizing data using R.

You will start the program by learning the fundamentals of R language, including common data types and structures, and utilize it for basic programming and data manipulation tasks.

As you progress in the program, you will learn about relational database concepts and gain a foundational knowledge of the SQL language. You will access and analyze data in databases using R and SQL through Jupyter notebooks.

You will learn various data analysis techniques – from cleaning and refining data to developing, evaluating, and tuning , data science models. You will also learn how to tell a compelling story with data by creating graphs, visualizations, dashboards and interactive data applications.

In each course you will complete hands-on labs and projects to help you gain practical experience with data manipulation, analysis and visualization using a variety of datasets. You will work with tools like R Studio, Jupyter Notebooks, Watson Studio and related R libraries for data science, including dplyr, Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

By the end of the program you will be able to apply the data science skills and techniques that you have accumulated and show case those skills in the R Data Science Capstone Project. involving a real-world dataset, and inspired by a real business challenge. This project will culminate in a presentation for reporting the results of data analysis with stakeholders.

What you'll learn

  • Perform basic R programming tasks such as using common data structures, data manipulation, using APIs, webscraping, and working with R Studio and Jupyter.
  • Create relational databases and query the data using SQL and R from JupyterLab.
  • Complete the data analysis process, including data preparation, statistical analysis, and predictive modeling.
  • Communicate data findings using data visualization charts, plots, and dashboards using libraries such as ggplot, leaflet and R Shiny.
  • Demonstrate data analysis and visualization skills by completing a project that requires data collection, analysis, basic hypothesis testing, visualization, and modelling performed on real-world datasets.

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What's inside

Five courses

SQL for Data Science with R

(15 hours)
A majority of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language for communicating with and extracting data from databases. A working knowledge of databases and the SQL language is a must if you want to become a data scientist. This course introduces relational database concepts and helps you learn and apply foundational knowledge of the SQL and R languages.

Analyzing Data with R

(15 hours)
The R programming language is purpose-built for data analysis. This course starts with a question, and then walks you through the process of answering it through data. You will first learn important techniques for preparing your data for analysis. Then you will learn how to gain a better understanding of your data through exploratory data analysis.

Visualizing Data with R

(6 hours)
In this course, you will learn the Grammar of Graphics, a system for describing and building graphs. You will also learn how to use the ggplot2 data visualization package for R to create basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will then learn how to use the Leaflet package for R to create map plots. Finally, you will learn about creating interactive dashboards using the R Shiny package.

R Data Science Capstone Project

(6 hours)
In this capstone project, you will apply data science skills and techniques to real-world datasets. You will collect and analyze data, perform hypothesis testing, and create visualizations. The project culminates in a presentation of your data analysis report.

R Programming Basics for Data Science

(7 hours)
The R language plays a critical role in data analysis and is a common programming language in data science & analytics. This course will introduce you to R language fundamentals like data types, techniques for manipulation, and how to implement fundamental programming tasks. We’ll also cover common data structures and programming fundamentals.

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