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.
What You Will Learn:
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
Coursera
Discover the power of R programming for data analysis with our introductory course, 'Data Analysis in R with RStudio & Tidyverse'. This comprehensive guide is perfect for beginners looking to start their journey in data science. Learn how to import, clean, and analyze datasets using RStudio and the versatile Tidyverse package suite.
Coursera
Discover the power of RStudio as you learn to visualize data and communicate your findings effectively. Ideal for those new to coding, this course offers a hands-on approach to understanding bar charts, histograms, heat maps, and more through R Markdown.
Coursera
Embark on a comprehensive learning journey where you'll discover how to analyze data with ease using Python, specifically focusing on pandas & matplotlib in the Spyder environment. Perfect for newcomers, this course offers an accessible entry point into coding and data analysis, equipping you with essential skills that can be applied across various fields.
Coursera
Discover how to transform raw data into compelling stories using Python and Jupyter Notebook. This beginner-friendly course equips you with essential coding skills and visualization techniques for effective data presentation.