Schedule

Class #	Date	Course Topics
1	Wednesday September 8	Python Introduction and Data Types
2	Monday September 13	Python Conditions and Loops
3	Wednesday September 15	Python Lists, Tuples, Dictionaries, and Functions
4	Monday September 20	Python File I/O and Exceptions, Modules and Objects
5	Wednesday September 22	Continuing last lecture
6	Monday September 27	Introduction to R and the tidyverse [Quiz 1 done remotely]
7	Wednesday September 29	R Data Structures: Vectors, Lists, Matrices, and Data Frames
8	Monday October 4	Extra Office Hours
9	Wednesday October 6	Extra Office Hours [Quiz 2 done remotely]

Note

Data 531 Labs are on Wednesdays. To help students in other time zones, we use two time slots for the labs: 8:30 am - 9:30 am and 13:30 pm - 15:30 pm. In later blocks as more of us transition to face-to-face, the morning slot may be deprecated. TAs will be using Zoom for the labs.

Learning Outcomes

1. Python Introduction and Data Types

   • understand Python 2 and Python 3 have some syntax differences

   • follow Python syntax rules including indentation, variable naming, and comments

   • define and compare: algorithm, language, program, programming

   • list and explain when to use different Python data types

   • perform math expressions and understand operator precedence

   • create and execute a Python program in jupyter notebook

   • perform printing to console for output

   • use string and string functions including string indexing, subsetting, and concatenation

   • apply formatting for string output

   • use date and time functions

   • proficient in reading input from console and output results to console

2. Python Conditions and Loops

   • create comparisons and use them for decisions with if

   • combine conditions with and, or, not

   • make decisions using if/elif/else syntax

   • perform repetition using loop constructs for and while

3. Python Lists, Tuples, Dictionaries, and Functions

   • create and use lists and list functions

   • understand advance syntax for list comprehensions, list slicing

   • create and use tuples and tuple functions

   • create and use dictionaries

   • explain the difference between tuples, lists, and dictionaries

   • create and use Python functions with parameters and return a value from a function

   • explain the benefit of using functions for program decomposition

   • use built-in functions and functions in the math library including generating random numbers

   • exposure to passing functions and lambda functions

4. Python File I/O and Exceptions

   • open, read, write, and close text files

   • process CSV files including using the csv module

   • understand and define web terminology including IPv4/IPv6 address, domain, domain name, URL

   • read URLs using urllib.request

   • explain the purpose of exceptions and exception handling

   • use try-except statement to handle exceptions and understand how each of try, except, else, finally blocks are used

5. Python Modules and Objects

   • use object-oriented terminology: class, object, method, parameter, instance variable, inheritance, superclass, subclass

   • create classes with methods

   • instantiate objects ; call methods and access object properties

   • know that Python supports inheritance when defining classes

   • import Python modules and packages

   • use Biopython module to retrieve NCBI data and perform BLAST

   • build charts using matplotlib

   • perform linear regression and k-means clustering using SciPy

   • connect to and query the MySQL database using Python

   • write simple Map-Reduce programs

   • apply object methods using the dot syntax

6. Introduction to R and Review of Basic Statistics

   • understand purpose and usefulness of R and difference with Python

   • define different types of data: qualitative, quantitative

   • describe data use numerical summaries (measure of centre/spread)

   • define and calculate: mean, median, variance, standard deviation, range

   • define: quantile, quartile, interquartile range, five number summary

   • install and use RStudio

   • set and get the working directory

   • list the different types of data structures in the R language

   • write small programs/commands in R that may use variables, conditions, loops, and functions

   • use R to determine the type and structure of an object

7. R Data Structures: Vectors, Lists, Matrices, and Data Frames

   • create, index, and subset vectors, lists, and matrices

   • generate vectors of random data

   • read in data sets from files

   • use head and tail to explore a data set

   • use data frames/factors for data analysis

   • explain what factors are and why they are useful

   • create graphs/visualizations: frequency table, bar chart, histogram, boxplot using base R and ggplot2

8. R Hypothesis Testing and Linear Regression [Optional]

   • explain the purpose of confidence intervals

   • perform hypothesis testing using R

   • understand assumptions inherent in a t-test

   • compute linear models using R