Algorithmic Thinking: How you should be thinking about your data





The objective of this nanocourse is to encourage students to perform complex bioinformatic and data analysis questions in their own research with a modular and methodical approach. During the first day, students will learn what an algorithm is and be introduced to the concept of algorithmic thinking. Students will learn to take problems relevant to computational biology and craft solutions in the form of flowcharts and general pseudocode. On the second day, students will learn how to transform the flowcharts and general pseudocode into very specific pseudocode. In this second session, students will also be introduced to the syntax of R and learn some basic concepts (variables, arrays, indexing, data import) that will help them implement their outlined analysis in code. Note: This course will not focus on one type of analysis in particular or cover enough R to be a fluent coder. Rather, this course is intended to help students think modularly and deconstruct their computational research questions into manageable components.

Class Assignments
Students will complete an assignment to create a flowchart and general pseudocode to address a specific complex bioinformatic problem that they are currently facing in their own research. 

Course Instructors: Kate Lachance, and Avery Davis Bell,
Course Director: Nils Gehlenborg,
Curriculum Fellow: Rachel Wright, and Ted Feldman,

Session Dates and Times
First Session Date: February 5, 2019 - 3:00PM - 6:00PM
First Session Location: TMEC 306
Second Session Date: February 12, 2019 - 3:00PM - 6:00PM
Second Session Location: For registered participants only


CLICK HERE to register for this course.