YoVDO

Data Science and Analysis Tools - from Jupyter to R Markdown

Offered By: Codio via Coursera

Tags

Data Science Courses Data Analysis Courses R Programming Courses Jupyter Notebooks Courses

Course Description

Overview

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.

Syllabus

Course 1: Data Analysis in Python with pandas & matplotlib in Spyder
- Offered by Codio. Code and run your first Python script in minutes without installing anything! This course is designed for learners with ... Enroll for free.

Course 2: Visualizing & Communicating Results in Python with Jupyter
- Offered by Codio. Code and run your first Python program in minutes without installing anything! This course is designed for learners with ... Enroll for free.

Course 3: Data Analysis in R with RStudio & Tidyverse
- Offered by Codio. Code and run your first R program in minutes without installing anything! This course is designed for learners with no ... Enroll for free.

Course 4: Visualizing Data & Communicating Results in R with RStudio
- Offered by Codio. Code and run your first R program in minutes without installing anything! This course is designed for learners with ... Enroll for free.


Courses

  • 0 reviews

    10 hours 43 minutes

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    Code and run your first Python script in minutes without installing anything! 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. In this course, you will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, a longer-form lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.
  • 0 reviews

    9 hours 46 minutes

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    Code and run your first R program in minutes without installing anything! This course is designed for learners with no prior coding experience, providing foundational knowledge of data analysis in R. The modules in this course cover descriptive statistics, importing and wrangling data, and using statistical tests to compare populations and describe relationships. This course presents examples in R using the industry-standard Integrated Development Environment (IDE) RStudio. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, a cumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.
  • 0 reviews

    10 hours 31 minutes

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    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. This course helps learners describe and make inferences from data, and better communicate and present data. The modules in this course will cover a wide range of visualizations which allow you to illustrate and compare the composition of the dataset, determine the distribution of the dataset, and visualize complex data such as geographically-based data. Completion of Data Analysis in Python with pandas & matplotlib in Spyder before taking this course is recommended. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, an accumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.
  • 0 reviews

    9 hours 38 minutes

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    Code and run your first R program in minutes without installing anything! This course is designed for learners with limited coding experience, providing foundational knowledge of data visualizations and R Markdown. The modules in this course cover different types of visualization models such as bar charts, histograms, and heat maps as well as R Markdown. Completion of the previous course (Data Analysis in R with RStudio & Tidyverse) in this specialization or similar experience is recommended. To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, a cumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.

Taught by

Anh Le and Kevin Noelsaint

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