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.
Data Science and Analysis Tools - from Jupyter to R Markdown Specialization
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Data Science and Analysis Tools - from Jupyter to R Markdown Specialization
Hands-on Introduction to Data Science. Analyze data and create data visualizations using industry-standard data science tools without installing anything!
Instructors: Sharon Jason
12,302 already enrolled
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Get in-depth knowledge of a subject
3.7
(24 reviews)
Beginner level
Recommended experience
3 months at 2 hours a week
Flexible schedule
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Share your expertise with employers
Get in-depth knowledge of a subject
3.7
(24 reviews)
Beginner level
Recommended experience
3 months at 2 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
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.
Overview
Skills you'll gain
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
practice exercises
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Specialization - 3 course series
What you'll learn
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
Skills you'll gain
Category: R Programming
Category: Tidyverse (R Package)
Category: Data Import/Export
Category: Statistical Hypothesis Testing
Category: Statistical Analysis
Category: Data Analysis
Category: Descriptive Statistics
Category: Regression Analysis
Category: Statistical Methods
Category: Correlation Analysis
Category: Data Wrangling
Category: Probability & Statistics
What you'll learn
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
Skills you'll gain
Category: R Programming
Category: Data Visualization Software
Category: Rmarkdown
Category: Ggplot2
Category: Statistical Visualization
Category: Histogram
Category: Box Plots
Category: Data Visualization
Category: Descriptive Statistics
Category: Data Science
Category: Scatter Plots
What you'll learn
Skills you'll gain
Category: Data Manipulation
Category: Data Import/Export
Category: Data Analysis
Category: Statistical Hypothesis Testing
Category: Python Programming
Category: Pandas (Python Package)
Category: Probability & Statistics
Category: Data Cleansing
Category: Data Visualization
Category: Matplotlib
Category: Descriptive Statistics
Category: Computational Logic
Category: Data Science
Category: Exploratory Data Analysis
Category: Statistics
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Frequently asked questions
The full specialization takes about 40 hours to complete.
Comfort using the computer and elementary mathematics -- however no coding or programming experience is necessary.
Yes. You should take Course 1 followed by Course 2 and then Course 3 followed by Course 4. Or, you should take Course 3 followed by Course 4 and then Course 1 followed by Course 2. The first two courses are Python focused while the last two courses are R focused. However, it doesn't matter whether you start with Python or R first.
More questions
Financial aid available,