Programming Assessment for Incoming MSDS Students

Basic Programming Concepts

Working from the Command-line

• Distinguish between major operating systems.
• Recognize the command line of a Unix-family operating system.
• Use basic shell commands for navigating and modifying directories and files.

Git and GitHub

• Distinguish between Git and GitHub.
• Perform basic Git operations, such as clone, add, commit, push, and pull.
• Perform basic GitHub operations, such as repository creation, fork, pull request, and fetch upstream.

Programming Editors

• Be able to use a standard programming editor, such as VS Code or JupyterLab, for coding Python and RStudio for R. 
   

Python

Python Basics

• Determine which version of Python you are using on a machine.
• Locate where the Python interpreter is installed on a machine.
• Use the print function.
• Write a simple “Hello, World!” .py file.
• Execute a Python program file from the command line.
• Read and write files from disk within a program.

Variables, Data Types, and Data Structures

• Describe and use Python’s basic data types: integers, floats, booleans, strings, and complex numbers.
• Define literal representations of each data type.
• Cast data from one type to another.
• Inspect the data type of a variable.
• Describe and use Python’s built-in data structures: lists, dictionaries, tuples, and sets.
• Describe and use the basic operators associated with each data type and structure.
• Initialize new data structures.
• Define the difference between mutable and immutable data structures.
• Add and remove data from mutable data structures.
• Perform variable interpolation with Python strings.

Syntax and Grammar

• Write valid variable names.
• Assign values to variables.
• Define expressions and statements.
• Use proper indentation for code blocks.

Control Structures

• Write for-loops, while-loops, and describe their general use cases.
• Employ conditional logic statements that return boolean values and structure program flow.
• Define exit conditions of for- and while-loops.
• Use the proper keywords to break out of loops and skip iterations.
• Use list, dictionary, and set comprehensions for compact looping.
• Create and use iterators.

Functions

• Access common built-in functions, such as print and open.
• Import Common libraries, such as math and random.
• Import and alias attributes (function, classes, variables) from a module.
• Create user-defined functions.
• Understand variable scope, including global, local, and nonlocal.
• Create and use lambda functions.
• Understand general design principles for functions.
• Recognize and write a recursive function.

NumPy

• Describe key features of NumPy array design.
• Recognize NumPy data types.
• Perform basic array indexing, slicing, and broadcasting.  
• Perform boolean indexing.
• Perform fancy indexing.
• Perform basic arithmetic operations with arrays.
• Understand and perform universal and vectorized functions.

Pandas

• Describe key features of Pandas DataFrame and Series design.
• Understand role of indexes in Pandas data structures.
• Perform indexing using loc and iloc.
• Perform column selection and creation.
• Apply vectorized functions to Pandas data structures.
• Reshape, transpose, stack, unstack Data Frames.
• Summarize Pandas Data Frames with value counts and groupby.

R Basics

• Use RStudio to edit and run R programs.
• Understand the essential differences between R and Python syntax.
• Understand R’s rules for naming variables.
• Describe and use R’s basic data types: logical, character, integer, float, and complex.
• Assign values to variables with R’s assignment operator.
• Understand the difference between R’s assignment operator and assigning values to parameters in functions.
• Describe and use R’s basic data structures: vector, matrix, list, and dataframe.
• Extract values from data structures with indexes, slicing, and boolean indexing.
• Create for- and while-loops with conditional statements.
• Produce scatterplots, bar graphs, and histograms with the plot function.
• Summarize data in data frames with the table function.
• Create user-defined functions.
• Install and use libraries.
• Understand how R handles namespaces.

The Tidyverse and Dplyr

• Awareness of the Tidyverse and how it differs from basic R.
• Define the expression “tidy data.”
• Describe and use Tibbles.
• Describe and use the basic data transformation verbs: select, filter, arrange, rename, mutate, and summarize etc.
• Use the pipe operator to chain functions together.
• Describe and use the split-apply-combine pattern for generating aggregate statistics on data sets.

Visualization

• Understand the layered architecture behind the grammar of graphics approach to visualization.
• Create layered graphics using the plus operator.
• Create graphics that visualize data from Dplyr operations.
• Create and render RMarkdown documents.