Introduction to NIH Grants

This workshop will be open to anyone interested in having the guidance, feedback and structure for writing a grant. Potential participants could be faculty who have not written an NIH grant before, postdocs or adjunct faculty, advanced graduate students, or even early stage graduate students who want to put together a dissertation grant.  The basic process and the structure of grant applications will be discussed.  This stand-alone session can serve as a foundation to a follow-on multi-session workshop (TBA) for those actively working on a grant.

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Introduction to Pandas

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python.

In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

We plan to cover:

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Visualization in Excel

In Visualization in Excel, we will cover the fundamentals of visualization in Excel, including a checklist of considerations that should go into every visualization. We will also go through step by step instructions on how to make horizontal bar charts, slope graphs, butterfly charts, the good kind of pie charts, icon arrays, and how to graph confidence intervals. We will not be using Microsoft Office default colors.

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Basics of Excel: Part 2

This class will cover the basics of Excel, from simple formulas (SUM, COUNTIF) to more complex Excel features like Macros and the Data Analysis ToolPak. By the end of both sections, students will be able to employ Excel skills to open source policy data sets. These skills are transferrable to any sector.

Topics Covered Will Include:

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The available spaces and the waitlist for this event are both full.

Basics of Excel: Part 1

This class will cover the basics of Excel, from simple formulas (SUM, COUNTIF) to more complex Excel features like Macros and the Data Analysis ToolPak. By the end of both sections, students will be able to employ Excel skills to open source policy data sets. These skills are transferrable to any sector.

Topics Covered Will Include:

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The available spaces and the waitlist for this event are both full.

Python Fundamentals: Part 4

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Part 4 Topics: We will applying the skills learned during previous sessions to a real world social science example.

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Python Fundamentals: Part 3

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Part 3 Topics:

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The available spaces and the waitlist for this event are both full.

Python Fundamentals: Part 2

Part 2 Topics:

  • Lists
  • Loops
  • Conditionals
  • Functions
  • Scope

Knowledge requirements: Python Fundamentals: Part 1 or equivalent prior knowledge

Registration note: To participate in multiple parts of this series, please be sure to register for each day separately.

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Python Fundamentals: Part 1

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Part 1 Topics:

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The available spaces and the waitlist for this event are both full.

R Fundamentals: Part 4

R Fundamentals Part 4: Putting it all together

In the final part, we will review data importation, subsetting, and visualization. Students will then be given the majority of time to reproduce a workflow on two different datasets, ask questions, and review the solutions as a group.

 

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