What you'll learn
Perform basic R programming tasks like working with data structures, data manipulation, using APIs, webscraping, and using R Studio and Jupyter.
Create relational databases and tables, load them with data from CSV files, and query data using SQL and R using JupyterLab.
Complete the data analysis process, including data preparation,
statistical analysis, and predictive modeling.
Communicate data analysis findings with data visualization charts, plots, and dashboards using libraries such as ggplot, leaflet and R Shiny.
Skills you'll gain
Shiny Dashboards
SQL & RDBMS
Data Analysis and Modelling
Data Visualization
R Programmin Language
This Specialization is intended for anyone with a passion for learning who is seeking to develop the job-ready skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist.
Through these five online courses, you will develop the skills you need to bring together often disparate and disconnected data sources and use the R programming language to transform data into insights that help you and your stakeholders make more informed decisions.
By the end of this Specialization, you will be able to perform basic R programming tasks to complete the data analysis process, including data preparation, statistical analysis, and predictive modeling. You will also be able to create relational databases and query the data using SQL and R and communicate your data findings using data visualization techniques.
Applied Learning Project
Throughout this Specialization, you will complete hands-on labs to help you gain practical experience with various data sources, datasets, SQL, relational databases, and the R programing language. You will work with tools like R Studio, Jupyter Notebooks, and related R libraries for data science, including dplyr, Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.
In the final course in this Specialization, you will complete a capstone project that applies what you have learned to a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modelling to be performed on real-world datasets.
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