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Data Science and Analysis Tools - from Jupyter to R Markdown

This specialization is intended for people without programming experience who seek an approachable introduction to data science that uses Python and R to describe and visualize data sets. This course will equip learners with foundational knowledge of data analysis suitable for any analyst roles. In these four courses, you will cover everything from data wrangling to data visualization. These topics will help prepare you to handle various types of data sets, giving you enough knowledge of data science to proficiently compare data sets, describe their relationship, and produce visualizations that highlight that relationship.

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What's inside

Four courses

Data Analysis in Python with pandas & matplotlib in Spyder

(0 hours)

Code and run your first Python script in minutes! This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages.

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Visualizing & Communicating Results in Python with Jupyter

(0 hours)

Code and run your first Python program in minutes without installing anything! This course is designed for learners with limited coding experience, providing a foundation for presenting data using visualization tools in Jupyter Notebook.

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Data Analysis in R with RStudio & Tidyverse

(0 hours)

Code and run your first R program in minutes without installing anything! This course provides foundational knowledge of data analysis in R for learners with no prior coding experience. Modules cover descriptive statistics, importing and wrangling data, and using statistical tests. Examples are presented in R using the industry-standard Integrated Development Environment (IDE) RStudio.

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Visualizing Data & Communicating Results in R with RStudio

(0 hours)

Code and run your first R program in minutes without installing anything! This course provides foundational knowledge of data visualizations and R Markdown, covering bar charts, histograms, heat maps, and more. Completion of the previous course (Data Analysis in R with RStudio & Tidyverse) or similar experience is recommended.

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Learning objectives

Be familiar with data science libraries in python and r.
Data transformation and normalization in python and r.

Create graph and charts using python and r.

Be familiar with data science libraries in python and r.
Data transformation and normalization in python and r.
Create graph and charts using python and r.

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Time

32 hours

Level

Introductory

Via

Coursera

Institution

Codio

Instructors

Anh Le

Kevin Noelsaint

Level

Introductory

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