Introduction to Highthroughput Data Analysis

class: center, middle
      # Programming for data analyses

Jason Stajich

https://biodataprog.github.io/2018_programming-intro/

---
      # What to expect

* This course will cover basics of using UNIX and Python for data analyses

* The topics will focus on skills for processing, filtering
      data with an emphasis on biological, genomic or transcriptomic sequence data.

* No prior programming experience required.

* You must have access to a computer that allows you to either
      run the terminal (e.g OSX or Linux) or run programs like SSH to
      log into UCR Biocluster.

* Some amount biology background will be assumed here. When in
      doubt ask questions. Others will be likely to have same
      questions!

---
      # Course Logistics
      1. Meeting in University Laboratory Building (ULB) Bioinformatics suite

2. Wednesdays scheduled for 1 hrs, Fridays for 2 hrs
      
      3. Expect to have a mix of in-class workshops and lectures.

3. Homework problem sets will be assigned to give you
      more practice and improve skills. If you don't practice you
      won't retain or master these skills.

3. Laptops are available during class setting, but you will need
      to have access to a computer outside of class to complete assignments

4. Accounts to the IIGB Biocluster will be provided during the
      course.

---
      # Resources

1. _Bioinformatics Data Skills: Reproducible and Robust Research
      with Open Source Tools_. Vince Buffalo. 2015 O'Reilly &
      Associates. Available from [O'Reilly and Associates](http://shop.oreilly.com/product/0636920030157.do), 
      [Amazon](http://amazon.com/Bioinformatics-Data-Skills-Reproducible-Research/dp/1449367372)

2. _Unix and Perl to the Rescue: A Primer_. Keith Bradnam and Ian
      Korf. [Free PDF](http://korflab.ucdavis.edu/unix_and_Perl/)

3. _Unix and Perl to the rescue!_ Bradnam and
      Korf. [Amazon](https://www.amazon.com/gp/product/0521169828?tag=keithbradnamc-20)

4. [Rosalind](http://rosalind.info/problems/locations/) - An online platform to learn bioinformatics and programming in Python.

5. Software Carpentry -
      [https://software-carpentry.org/](https://software-carpentry.org/)
      and Data Carpentry - [http://www.datacarpentry.org/](http://www.datacarpentry.org/).

6. Berk Ekmekci, Charles E. McAnany, Cameron Mura. An Introduction
      to Programming for Bioscientists: A Python-Based Primer. PLoS Comp
      Bio. DOI:  [10.1371/journal.pcbi.1004867](https://doi.org/10.1371/journal.pcbi.1004867)
      
      ---
      # Grading
      1. Homeworks (5 in total) will be worth 50% of the grade

2. Team project - presentation, written report and code
      repository worth 50% of grade.

3. You are expected to attend class. If you need be absent do let me
      know. Material will be presented from the slides, but also
      in-class explanations.

4. Your work is expected to be your own. Google, StackExchange
      are useful tools to find solutions, and resuse is part of
      coding. However, you need to work on understanding the steps in
      your code so what you turn in should be a reflection of your own
      efforts.

---
      # Homework
      1. Code should be runnable as turned in. You will deposit your
      code in your github repository. You can make one private
      personal repository to deposit and should organize a folder for
      each homework assignment (e.g. hw1, hw2, hw3).

3. Homework is due BEFORE class on the Friday it is due.

---
      # Projects

1. Topics to be selected from a set of choices or the team's
      choosing (with approval from instructor). Selection of topics
      will occur by end of October.

2. Project teams will be 1-2 individuals working together.

3. A presentation will be made by each team - last day(s) of class
      but may have to extend class time to accomodate this.

4. A final report with the details will be turned in by the group.

5. The report needs to detail what each person's contribution is
      to the project.