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# System prepended metadata

title: Data Science Fellows Spring 2020 - Statistics Sprint

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# Data Science Fellows Spring 2020 - Statistics Sprint

## Day 5: April 6 2020

#### Agenda: 
    
    1. Go through google slides on more Bayesian statistics and networks
    2. Spin up container on CyVerse
    3. Go through final markdown file
    
#### What are some of the advatanges of using a probabilistic statistical framework?

    - Works well with large amounts of observations
    - Networks are sexy
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#### What are some of the disadvantages of using a probabilistic statistical framework?

    - Does not work well with small number of oberservations (stochastic framework)
    - Relies on good data; prone to the garbage in, garbage out problem
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#### What have you heard about Markov Chains?

    - Used them extensively for protein domain annotations (HMMs)
    - Gene predicition with hidden markov model
    - Often used by (simple) robots in state modeling / motion planning
    - Financial Modelling - stock and financial assets pricing prediction
    - PageRank from Google 
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[Lesson 4: Extending Bayes into Networks](https://github.com/rbartelme/rstudio-stats/blob/master/lessons/Lesson4_BayesianNetworks.md)

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## Day 4: April 3 2020

#### Agenda:

    1. Go through google slides on clustering and dimensionality
    2. Open CyVerse Container
    3. Go through content in markdown file

#### What do you think "data clustering" or "data partitioning" is?
    - Clustering: identifying meaningful groups of data items; Partitioning: identifying meaningful divisions of a mathematical space
    - identifying the underlining structure of the data.
    - Data clustering: identifying features in datasets that are used to measure similarity/distance across those datasets
    -  Combining a set variables or cases (depends) in similar groups according to some features that makes them closer to the group assigned than to the groups not assigned
    -Data organization 
    - identifying similar groups in your dataset. type of unsupervised clustering.
[Lesson 3: Dimensionality](https://github.com/rbartelme/rstudio-stats/blob/master/lessons/Lesson3_Dimensionality.md)

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## Day 3: April 2 2020

#### Agenda:
    
    1. Go through google slides for data intuition
    2. Spin up rstudio-stats on Cyverse
    3. Go through content on github markdown file (copied below)

[Lesson 2: Study Designs and Data Distributions](https://github.com/rbartelme/rstudio-stats/blob/master/lessons/Lesson2_DistributionsAndData.md)

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## Day 2: April 1 2020

#### Agenda:
    
    1. Go through google slides for background on probability and regression
    2. Spin up rstudio-stats on Cyverse
    3. Go through content on github markdown file (linked below)
    
#### What do you think about when you hear "probability theory"? Or just "probability"?

    - Odds, betting, chance
    - Creating predictions about future data based on current data
    - The math behind creating distributions and asking the question "what is the chance that. . . "
    - probability distributions, CLT and LLN
    - Providing a degreee of certainty regarding some outcome 
    - How likely that an event will occur in a random experiment
    
[Lesson 1: Probability and Regression](https://github.com/rbartelme/rstudio-stats/blob/master/lessons/Lesson1_Probability_Regression.md)

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## Day 1: March 30 2020

#### Agenda:
    
    1. Go through short google slides for statistics background
    2. Spin up rstudio-stats application on CyVerse DE
    3. Run through the Carpetries Introduction to R


#### List some topics or concepts you know from statistics:

    - Ex. Central limit theorem
    - Exploratory Stats (mean, median, mode, etc. Standard Deviations)
    - Sampling and Inference
    - visualizing data proprieties without graphics 
    - Get a bunch of numbers.  Do stuff with numbers.  Mostly, develop a hypothesis and a null hypothesis, and then test.
    - t-tests, 
    - ANOVA, TukeyHD, tests for determining significance of values
    - lines of best fit, R-squared values
    - Nobody really understands p-values
    
#### Introducing the R Language
[Carpentries Introduction to R](https://datacarpentry.org/R-genomics/01-intro-to-R.html)

#### Outcomes:
    - Assess what everyone already knows about statistics 
    - Gain familiarity with R before we get into lessons 1-4

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## Day 0: March 26 2020

* Post your initials under here as a sub-bullet to let me know if you got the invite and were able to access this file:
    * RB
    * HE
    * MO
    * GA
    * EL
    * AB

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