Learn Data Analysis: What’s Math Got to do With It?

What’s math got to do with data analysis? Unfortunately, for those of us who are chronic humanities people, math has a lot to do with it. This might seem like a daunting barrier, especially if the last time you looked at a math problem was in a high school algebra class. This is also true for learners who are already skilled with the technological aspect of data analysis but are not familiar with the mathematics side of thing. However, there are so many resources available to help self-directed students learn the basics and get up to speed for the purposes of data analytics! Using the resource platforms described in last week’s blog post, these resources will have even chronic humanities people playing with numbers in no time!  

LinkedIn Learning 

  • Learning Everyday Math 
    • Look, some of us did not absorb or retain the basic math lessons of our early education. That’s okay! This is a no-judgment zone, and this 2 hour course will help users learn how to calculate percentages for tips and taxes, compare prices while shopping, find the area and volume for home-improvement projects, and learn the basics of probability.  
  • Become a Data Scientist 
    • This 21 hour Learning Path is made up of 12 courses that focus more on the statistical side of data analysis than the technical steps of the process. This course is more geared toward users with experience in IT and computers, so it is not the best for people who do not have a strong technical background. However, for those who are familiar with computer science and want to pivot into data analytics, this is an ideal curriculum.   

O’Reilly Books and Videos (Make sure to follow these instructions for logging in!)

  • Essential Math for Data Science 
    • This eBook mixes basic coding skills with math lessons to cover the essential analytical skills needed for data science work. Relevant aspects of calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks are covered in plain English. The chapters include exercises with answers for self-assessment as well as career advice for budding data analysts.   
  • Statistics for Data Science using Python 
    • Besides books, O’Reilly also has expert curated playlists that consist of chapters of several different books, videos and more. This is a great way of getting the most out of several resources to focus on a single skill. This playlist covers the essential statistic concepts found in 11 different resources. Learn about Normal distribution, hypothesis tests, p-values, central limit theorem and more without having to dig for the resources yourself!  
  •   Data Science 101: Methodology, Python, and Essential Math 
    • On top of books and playlists, O’Reilly also has video-based courses. This course covers a lot of data analytics basics, but those who want to focus on the math aspect will benefit from Chapters 15-19. These chapters cover linear algebra, mathematical structures, probability, random variables and multiple variables, and statistical inference.  

In the Catalog 

Be sure to come back next week for the thrilling continuation with “*hacker voice* I’m In: Coding and Software for Data Analysis! 

Lightning Review: How to Use SPSS

“A nice step-by-step explanation!”

“Easy, not too advanced!”

“A great start!”

           Real, live reviews of Brian C. Cronk’s How to Use SPSS: A Step-By-Step Guide to Analysis and Interpretation by some of our patrons! This book, the Tenth Edition of this nine-chapter text published by Taylor and Francis, is ripe with walkthroughs, images, and simple explanations that demystifies the process of learning this statistical software. Also containing six appendixes, our patrons sang its praises after a two-hour research session here in the Scholarly Commons!

           SPSS, described on IBM’s webpage as “the world’s leading statistical software used to solve business and research problems by means of ad-hoc analysis, hypothesis testing, geospatial analysis and predictive analytics. Organizations use IBM SPSS Statistics to understand data, analyze trends, forecast and plan to validate assumptions and drive accurate conclusions’ is one of many tools CITL Statistical Consulting uses on a day-to-day basis in assisting Scholarly Commons patrons. Schedule a consultation with them from 10 am to 2 pm, Monday through Thursday, for the rest of the summer!

           We’re thrilled to hear this 2018 title is a hit with the researcher’s we serve! Cronk’s book, and so many more works on software, digital publishing, data analysis, and so much more make up our reference collection – free to use by anyone and everyone in the Scholarly Commons!

CITL Workshops and Statistical Consulting Fall 2017

CITL is back at it again with the statistics, survey, and data consulting services! They have a busy fall 2017, with a full schedule of workshops on the way, as well as their daily consulting hours in the Scholarly Commons.

Their workshops are as follows:

  • 9/19: R I: Getting Started with R
  • 10/17: R I: Getting Started with R
  • 9/26: R II: Inferential Statistics
  • 10/24: R II: Inferential Statistics
  • 10/3: SAS I: Getting Started with SAS
  • 10/10: SAS II: Inferential Statistics with SAS
  • 10/4: STATA I: Getting Started with Stata
  • 9/20: SPSS I: Getting Started with SPSS
  • 9/27: SPSS II: Inferential Statistics with SPSS
  • 10/11: ATLAS.ti I: Qualitative Data analysis
  • 10/12: ATLAS.ti II: Data Exploration and Analysis

Workshops are free, but participants must register beforehand. For more information about each workshop, and to register, head to the CITL Workshop Details and Resources page.

And remember that CITL is at the Scholarly Commons Monday – Friday, 10 AM – 4 PM.You can always request a consultation, or walk-in.

“Fact Check Yourself Before You Fact Wreck Yourself”: A Primer on Information Literacy Resources

We have all fallen for fake news at some point in our lives and we can all learn skills to help prevent that from happening again. Technology can change our world for the better and help us combat the problem of fake news. Facebook and Google are increasingly incorporating fact checking and ways to see if sources are verified into their platforms, and we even have Illinois students working hard to solve the problem of fake news in social media from a technological perspective.

Snopes, Politifact, and Washington Post Fact Checker, are all great places to start. Plus, Snopes for example, has pulled pranks to make sure you aren’t too reliant on one source, which may sound bad but they want you to remain skeptical of all sources, and not become too dependent on one source.

However,  it’s more important to really learn that just because something sounds like it could be true does not mean that it isn’t complete baloney. My friend Jesse E., a playwright based in New York City, came up with a clever saying to think about before sharing any news stories on social media: “Fact check yourself before you fact wreck yourself”. Overall, you need to attain a certain level of information literacy.

What is information literacy?

  • Critically thinking about your sources of information, where they come from, and why they were created.
    • Even when that requires extra effort
      • Even when you are just scrolling through headlines on social media

How old is this problem?

Older than you may think!

The first fake photograph, was created in 1840 by Hippolyte Bayard, an early pioneer of photography. Specifically, in his Self-Portrait as a Drowned Man — a very meta demonstration of his photography process — he claimed he was a photography pioneer who committed suicide over getting overlooked for Daguerre and his Daguerrotype.

And of course feel free to debate or suggest more media literacy must reads in the comments!

What else hasn’t changed?

Statistics are still hard and people do crazy things with numbers all the time. Luckily, you can get a good overview of statistics and common errors here through our small but mighty non-circulating collection of stats books. And don’t be afraid to ask your wildest stats questions to our experts here!

Interested in becoming more information literate or helping your students become more information literate?

Digital Zombies

Inspired by Max Brooks’ World War Z, “Digital Zombies” is a hybrid online and in person information literacy scavenger hunt where players learn about and eventually make their own fake historical sources. This resource was created by history and information science researchers based in California and Ontario originally for students in the University of California system, but easily adapted to other locales.

“Sleeping with the Enemy: Wikipedia in the College Classroom.”

This provocatively titled article focuses on research done at Lycoming College, where professors decided to confront Wikipedia and online source use issues in a creative way, by having students actually write their own Wikipedia articles. This study shows a great way to get students interested in how sources are created and contribute to a source that the public often relies on for general reference information.

A great journalism LibGuide from FIU chock full of good tips can be found here at http://libguides.fiu.edu/c.php?g=626398&p=4374383 for those who enjoy LibGuides.

The Programming Librarian (ALA) has also recently put out a list of fake news fighting resources!

And of course our very own information literacy information portal!

SourceLab is a course sequence and digital history initiative here on campus!

And remember, Scholarly Commons is a great place to begin your quest for the truth!