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DataCamp Course: Intermediate R

Course Description

The intermediate R course is the logical next stop on your journey in the R programming language. In this R training you will learn about conditional statements, loops and functions to power your own R scripts. Next, you can make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in the R programming language, data structure manipulations and times and dates. This R tutorial will allow you to learn R and take the next step in advancing your overall knowledge and capabilities while programming in R.

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Introduction to R

Course Description

In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this R online course today!

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Data Analysis in R, the data.table Way

Course Description

The R data.table package is rapidly making its name as the number one choice for handling large datasets in R. This online data.table tutorial will bring you from data.table novice to expert in no time. Once you are introduced to the general form of a data.table query, you will learn the techniques to subset your data.table, how to update by reference and how you can use data.table’s set()-family in your workflow. The course finishes with more complex concepts such as indexing, keys and fast ordered joins. Upon completion of the course, you will be able to use data.table in R for a more efficient manipulation and analysis process. Enjoy!

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Course: Joining Data in R with dplyr

Course Description

This course builds on what you learned in Data Manipulation in R with dplyr by showing you how to combine data sets with dplyr's two table verbs. In the real world, data comes split across many data sets, but dplyr's core functions are designed to work with single tables of data. In this course, you'll learn the best ways to combine data sets into single tables. You'll learn how to augment columns from one data set with columns from another with mutating joins, how to filter one data set against another with filtering joins, and how to sift through data sets with set operations. Along the way, you'll discover the best practices for building data sets and troubleshooting joins with dplyr. Afterwards, you’ll be well on your way to data manipulation mastery!

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Course: Exploratory Data Analysis in R: Case Study

Course Description

Once you've started learning tools for data manipulation and visualization like dplyr and ggplot2, this course gives you a chance to use them in action on a real dataset. You'll explore the historical voting of the United Nations General Assembly, including analyzing differences in voting between countries, across time, and among international issues. In the process you'll gain more practice with the dplyr and ggplot2 packages, learn about the broom package for tidying model output, and experience the kind of start-to-finish exploratory analysis common in data science.

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Course: Data Manipulation in R with dplyr

Course Description

In this interactive tutorial, you will learn how to perform sophisticated dplyr techniques to carry out your data manipulation with R. First you will master the five verbs of R data manipulation with dplyr: select, mutate, filter, arrange and summarise. Next, you will learn how you can chain your dplyr operations using the pipe operator of the magrittr package. In the final section, the focus is on practicing how to subset your data using the group_by function, and how you can access data stored outside of R in a database. All said and done, you will be familiar with data manipulation tools and techniques that will allow you to efficiently manipulate data.

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Skill Track: Data Manipulation with R

Master advanced concepts in data manipulation using powerful tools like dplyr and data.table to make your data science projects faster and more readable.

DataCamp Courses:

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Skill Track: Importing & Cleaning Data with R

Learn how to parse data in any format. Whether it's flat files, statistical software, databases, or web data, you'll learn to handle it all.

DataCamp Courses:

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DataCamp Course: Importing & Cleaning Data in R: Case Studies

Course Description

Running exciting analyses on interesting datasets is the dream of every data scientist. But first, some importing and cleaning must be done. In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Course: Cleaning Data in R

Course Description

It's commonly said that data scientists spend 80% of their time cleaning and manipulating data and only 20% of their time actually analyzing it. For this reason, it is critical to become familiar with the data cleaning process and all of the tools available to you along the way. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. After taking the course you'll be able to go from raw data to awesome insights as quickly and painlessly as possible!

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Course: Importing Data in R (Part 2)

Course Description

In this second part to Importing Data in R, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such SAS, STATA and SPSS.

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Drew LaMar onto Data Life Cycle: Wrangle

DataCamp Course: Importing Data in R (Part 1)

Course Description

Importing data into R to start your analyses—it should be the easiest step. Unfortunately, this is almost never the case. Data come in all sorts of formats, ranging from CSV and text files and statistical software files to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis.

In this course, you will get started with learning how to read CSV and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that you will learn how to read XLS files in R using readxl and gdata.

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Drew LaMar onto Data Life Cycle: Wrangle

Summer Internships in Parallel Computational Science

The SIParCS Program at the National Center for Atmospheric Research is aimed at university students who are interested in pursuing a career in such areas as:

  • Application Optimization
  • Computer Security and Formal Verification
  • Data Science
  • Numerical Methods
  • Software Engineering
  • Supercomputing Systems Operations
  • Visualization

2019 Technical Projects and Program Intern Applications are now available!  Applications due January 11, 2019

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Alycia Crall onto Opportunities in the Field

NEON Science YouTube Channel: Science Explained

These eight animated short videos explain ecological data, concepts, or techniques, including: "Ecological Forecasting: The Science of Predicting Ecosystems,"Eddy Covariance: Measuring an Ecosystem's Breath," and Mapping the Invisible: Introduction to Spectral Remote Sensing."

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Alycia Crall onto Educational Resources

Figure of the Day: Disease version

Students use their number sense to make observations and come up with reasonable guesses or explanations for the patterns shown. Forked version with all infectious disease graphs.

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Arietta Fleming-Davies onto Figure of the Day

Global Sustainability Scholars

We foster professional development of a new generation of scientists, who represent the diversity of today’s society, in transdisciplinary sustainability science and innovation. We have run two synergistic programs that serve underrepresented groups in sustainability STEM, the Global Sustainability Scholars program serving undergraduates, Global Sustainability Scholar Early Career Fellows which serves graduate students and professionals who have graduated in the past 3 years.

 

 

 

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Helping students ask questions

  • All students should and can learn to formulate their own questions
  • All educators can easily teach the skill as part of their regular practice

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Kristin Jenkins onto Morphology BIRDD Data

DFMorph (Darwin's Finch Morphology) database

The Darwin's Finch Morphology database contains data, mostly morphological (e.g., beak dimensions), on individual Darwin’s finches. The data are from a number of published and unpublished sources.

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Explore in Radiant

Note: You must be a member of the BIRDD group to explore this data in Radiant.

Bibliography

  • Price, F. and Donovan, S. (2006). "BioQUEST Library VII: BIRDD: Beagle Investigations Return with Darwinian Data Version 2.0 (User Notes)."  BioQUEST Curriculum Consortium.
  • Lack, D.L. (1945). “The Galápagos finches (Geospizinae): a study in variation.” Occasional Papers of the California Academy of Sciences 21: 1-159.
  • Lack, D.L. (1947). Darwin's Finches: an essay on the general biological theory of evolution. Cambridge, England, Cambridge University Press.
  • Lack, D.L. (1969). “Subspecies and sympatry in Darwin's Finches.” Evolution 23: 252-263.
  • Snodgrass, R. E. and E. Heller (1904). “Papers from the Hopkins-Stanford Galapagos Expedition, 1898-99 XVI. Birds.” Proc. Wash. Acad. Sci. 5: 231-372.
  • Sulloway, F. J. (1982). “The Beagle collections of Darwin's Finches (Geospizinae).” Bulletin of the British Museum (Natural History), Zoology series 43: 49-94.
  • Swarth, H. S. (1931). “The avifauna of the Galapagos Islands.” Occ. Pap. Calif. Acad. Sci 18: 5-299.

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Drew LaMar onto Morphology BIRDD Data

Data Science for Social Good

The Data Science for Social Good Fellowship is a University of Chicago summer program to train aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. Working closely with governments and nonprofits, fellows take on real-world problems in education, health, energy, public safety, transportation, economic development, international development, and more. For three months they learn, hone, and apply their data science, analytical, and coding skills, collaborate in a fast-paced atmosphere, and learn from mentors coming from industry and academia.

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Alycia Crall onto Opportunities in the Field

Significant Opportunities in Atmospheric Research and Science (SOARS)

Each summer, SOARS protégés spend ten weeks conducting original research at the National Center for Atmospheric Research (NCAR) or at laboratories of other SOARS sponsors. By the end of the summer, protégés will prepare scientific papers and present their research at a colloquium. SOARS protégés can participate for up to four summers so that they can explore the breadth of geoscience and transition smoothly into graduate school.

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PyLadies

We are an international mentorship group with a focus on helping more women become active participants and leaders in the Python open-source community. Our mission is to promote, educate and advance a diverse Python community through outreach, education, conferences, events and social gatherings.

PyLadies also aims to provide a friendly support network for women and a bridge to the larger Python world. Anyone with an interest in Python is encouraged to participate!

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Alycia Crall onto Initiatives

Data for Democracy

Data for Democracy is a worldwide community of passionate volunteers working together to promote trust and understanding in data and technology.

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Alycia Crall onto Initiatives

Data for Black Lives

Data for Black Lives is a group of activists, organizers, and mathematicians committed to the mission of using data science to create concrete and measurable change in the lives of Black people.

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Alycia Crall onto Initiatives

Girls Who Code

Girls Who Code was founded with a single mission: to close the gender gap in technology.

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NumFOCUS

The mission of NumFOCUS is to promote sustainable high-level programming languages, open code development, and reproducible scientific research. We accomplish this mission through our educational programs and events as well as through fiscal sponsorship of open source scientific computing projects. We aim to increase collaboration and communication within the data science and scientific computing community.

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