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<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Monash Data Fluency</title>
<link>https://MonashDataFluency.github.io/</link>
<description>Recent content on Monash Data Fluency</description>
<generator>Hugo -- gohugo.io</generator>
<language>en-us</language>
<copyright>Content released under a Creative Commons Attribution 4.0 International license (CC BY 4.0).</copyright>
<lastBuildDate>Fri, 23 Feb 2018 13:28:03 +1100</lastBuildDate>
<atom:link href="https://MonashDataFluency.github.io/index.xml" rel="self" type="application/rss+xml" />
<item>
<title>Drop-in session, 31 Aug 2018</title>
<link>https://MonashDataFluency.github.io/events/drop-in-session-20180831/</link>
<pubDate>Fri, 31 Aug 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/events/drop-in-session-20180831/</guid>
<description>Data Fluency Drop-In Session: Friday 31st Aug, 2pm - 4pm Guest speaker Guest speaker, Juan Nunez-Iglesias, Bio Image Analysis Research Fellow from Monash Micro Imaging will present A whirlwind introduction to image analysis in Python at our next event.
Date and Time: Friday, August 31st at 2:00 pm in G19, 15 Innovation Walk (opposite Cinque Lire cafe), Clayton campus.
Data Fluency Book Club Immediately after Juan&rsquo;s presentation we will be hosting the Data Fluency Book Club.</description>
</item>
<item>
<title>Events</title>
<link>https://MonashDataFluency.github.io/events/drop-in-session-20180622/</link>
<pubDate>Sat, 23 Jun 2018 11:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/events/drop-in-session-20180622/</guid>
<description>Data Fluency Drop-In Session: Friday 22nd Jun, 2pm - 4pm Book Club - R for Data Science (3 pm) Our &ldquo;R for Data Science&rdquo; book club discussion continues. Come sharpen your R skills by discussing the content with R experts in the community.
This time we will be discussing chapter 7 of the book: Exploratory Data Analysis.
Has your data ever surprised you with the story it told? Has visualizing changed how you subsequently analysed or modelled it?</description>
</item>
<item>
<title>Drop-in session, 25 May 2018</title>
<link>https://MonashDataFluency.github.io/events/drop-in-session-20180525/</link>
<pubDate>Wed, 25 Apr 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/events/drop-in-session-20180525/</guid>
<description>Data Fluency Drop-In Session: Friday 25th May, 2pm - 4pm This month we will have a short seminar from Matthew Browne, continue the &ldquo;R for Data Science&rdquo; book club, as well as the usual help session.
(Unfortunately Matthew will ill and couldn&rsquo;t make it - hopefully at a later date !)
Seminar - The Democratisation of Data Science (2 pm) Guest speaker: Matthew Browne, Data Scientist, Monash University
Book Club - R for Data Science (3 pm) We kicked off the Data Fluency Book Club on 27 April 2018 with the aim of sharpening our R skills by reading &ldquo;R for Data Science&rdquo; by Garrett Grolemund and Hadley Wickham.</description>
</item>
<item>
<title>Drop-in session, 27 Apr 2018</title>
<link>https://MonashDataFluency.github.io/events/drop-in-session-20180427/</link>
<pubDate>Wed, 25 Apr 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/events/drop-in-session-20180427/</guid>
<description> Data Fluency Drop-In Session: Friday 27th Apr 2018 This first drop-in session we intend to:
Run a quick book club discussion on the first 3 chapters of R for Data Science.
Work together on some coding challenges !
Ask for help (or provide help !) on your current programming or data analysis problems.
</description>
</item>
<item>
<title>01 Programming in Python (Introduction)</title>
<link>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/</link>
<pubDate>Fri, 23 Feb 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/intro/</guid>
<description>The Basics of Python Python is a general purpose programming language that supports rapid development of scripts and applications.
Python&rsquo;s main advantages:
Open Source software, supported by Python Software Foundation Available on all platforms (ie. Windows, Linux and MacOS) It is a general-purpose programming language Supports multiple programming paradigms Very large community with a rich ecosystem of third-party packages Interpreter Python is an interpreted language which can be used in two ways:</description>
</item>
<item>
<title>02 Data Analysis in Python</title>
<link>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/</link>
<pubDate>Fri, 23 Feb 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/working_with_data/</guid>
<description>Working With Pandas DataFrames We can automate the process of performing data manipulations in Python. It&rsquo;s efficient to spend time building the code to perform these tasks because once it&rsquo;s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis.
The Dataset For this lesson, we will be using the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA</description>
</item>
<item>
<title>03 Indexing, Slicing and Subsetting</title>
<link>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/</link>
<pubDate>Fri, 23 Feb 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/indexing/</guid>
<description>In lesson 02, we read a CSV into a Python pandas DataFrame. We learned:
How to save the DataFrame to a named object, How to perform basic math on the data, How to calculate summary statistics, and How to create basic plots of the data. In this lesson, we will explore ways to access different parts of the data using:
Indexing, Slicing, and Subsetting Loading our data We will continue to use the surveys dataset that we worked with in the last lesson.</description>
</item>
<item>
<title>04 Automation with Loops</title>
<link>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/</link>
<pubDate>Fri, 23 Feb 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/loops/</guid>
<description>An example task that we might want to repeat is printing each character in a word on a line of its own.
word = 'lead' We can access a character in a string using its index. For example, we can get the first character of the word 'lead', by using word[0]. One way to print each character is to use four print statements:
print(word[0]) print(word[1]) print(word[2]) print(word[3]) Gives output</description>
</item>
<item>
<title>05 Making Plots With ggplot</title>
<link>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/</link>
<pubDate>Fri, 23 Feb 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/workshops/intro_to_python/20180323-launch-workshop/plotting/</guid>
<description>Introduction Python has powerful built-in plotting capabilities such as matplotlib, but with great power comes great complexity. For this exercise, we are going to use different python library, plotnine. There are a number of different libraries to choose from, but we are setting on plotnine as this is python port of original ggplot2 an R library (package), which is a very nice way to create publication quality plots and syntax is preserved, meaning you can take your python ggplot code and run it in R if you want it.</description>
</item>
<item>
<title>23 Mar 2018 : Launch workshop - Welcome to Data Fluency</title>
<link>https://MonashDataFluency.github.io/events/launch-workshop-23-mar-2018/</link>
<pubDate>Fri, 23 Feb 2018 13:28:03 +1100</pubDate>
<guid>https://MonashDataFluency.github.io/events/launch-workshop-23-mar-2018/</guid>
<description>Launch workshop Build your data fluency through ongoing skill development opportunities. Take advantage of free workshops, weekly drop-in sessions, hackathon events, technical talks, and more. Become part of our growing community of practice.
The Data Fluency program will be launched on 23 March at 10am. Find out more about Data Fluency.
We will running workshops at the Matheson Library, 1pm - 5pm. Make sure you register for the workshops to get a spot.</description>
</item>
<item>
<title>Drop-in session, 27 Jul 2018</title>
<link>https://MonashDataFluency.github.io/events/drop-in-session-20180727/</link>
<pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
<guid>https://MonashDataFluency.github.io/events/drop-in-session-20180727/</guid>
<description>Data Fluency Drop-In Session: Friday 27nd Jul, 2pm - 4pm Data Fluency Book Club 2:00 pm July 27th, G19, 15 Innovation Walk (opposite Cinque Lire Cafe).
Book Club (R for Data Science) &amp; Exploring OpenRefine (2 ~ 3 pm) For the Data Fluency book club for this month (2 pm, July 27th), we will be looking at Chapters 9 through to 16 of &ldquo;R for Data Science&rdquo;, which covers &ldquo;data wrangling&rdquo;: loading and tidying data, operations such as spreading, gathering and joining.</description>
</item>
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</rss>