Course

Data Science and Analysis Tools - from Jupyter to R Markdown

Codio

This specialization is designed for individuals new to programming and seeking an accessible introduction to data science using Python and R. It equips learners with foundational knowledge of data analysis, data wrangling, and data visualization, suitable for various analyst roles.

Throughout the four courses, participants will delve into data analysis using Python and R, gaining insights into data transformation and normalization. They will learn to create graphs and charts using both programming languages, enabling them to proficiently compare data sets and produce compelling visualizations that effectively illustrate relationships within the data.

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Data Science and Analysis Tools - from Jupyter to R Markdown

This specialization comprises four courses covering data analysis in Python with pandas & matplotlib in Spyder, visualizing & communicating results in Python with Jupyter, data analysis in R with RStudio & Tidyverse, and visualizing data & communicating results in R with RStudio.

Data Analysis in Python with pandas & matplotlib in Spyder

MODULE 1: Data Analysis in Python with pandas & matplotlib in Spyder

Describe a numerical data set with statistics
Import and describe a mixed data set using pandas and matplotlib
Determine if populations are different using statistical tests
Describe relationships between variables using statistical tests

Visualizing & Communicating Results in Python with Jupyter

MODULE 2: Visualizing & Communicating Results in Python with Jupyter

Create charts to describe and compare the composition of data sets
Illustrate the distribution of data through visualizations
Generate visualizations for specialized data (e.g. geographical, three dimensional, etc)

Data Analysis in R with RStudio & Tidyverse

MODULE 3: Data Analysis in R with RStudio & Tidyverse

Describe a numerical data set using statistical functions in R
Import and manipulate data sets using Tidyverse
Determine if populations are different using statistical tests
Use statistical tests to describe or explain the relationship between data sets

Visualizing Data & Communicating Results in R with RStudio

MODULE 4: Visualizing Data & Communicating Results in R with RStudio

Create charts to describe and compare the composition of data sets
Illustrate the distribution of data through visualizations
Create specialized visualizations such as heat maps, correlograms, and mosaic plots
Use R Markdown to create documents, reports, and presentations

Course

Data Science and Analysis Tools - from Jupyter to R Markdown

Course Modules

Data Analysis in Python with pandas & matplotlib in Spyder

Visualizing & Communicating Results in Python with Jupyter

Data Analysis in R with RStudio & Tidyverse

Visualizing Data & Communicating Results in R with RStudio

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