MTH 448/548 Data Oriented Computing

Logistics

• MTH 448/548 Syllabus
  • Class meetings
  • Instructor
  • Prerequisites
  • Learning Outcomes
  • Course Resources
  • Grading
    • Project Reports
    • Weekly digests
  • Incomplete Grades
  • Academic Integrity
  • Accessibility Resources
• Current Tasks
  • Weekly Digest 13
• Project Report Guide
  • Collaboration
  • Using external resources
  • Report grading rubrics
  • Report Style Guide
    • Report introduction
    • Report conclusions
    • Report organization
    • References
    • Math formatting
    • Code structure
    • Code sequencing
    • Code comments
    • Execution errors
    • Output size
    • Output formatting
    • Graphs and plots
  • Notebook gallery
• Useful links
  • Markdown cheatsheet
  • LaTeX symbols
  • Python docs
  • Numpy docs
  • Seaborn docs
  • Plotly docs
  • Json docs
  • Pandas docs
  • Requests docs
  • Beautiful Soup docs
  • Python Data Science Handbook by Jake VanderPlas

Schedule

• Preliminaries
• Week 1 (1/31-2/6)
• Week 2 (2/7-2/13)
• Week 3 (2/14-2/20)
• Week 4 (2/21-2/27)
• Week 5 (2/28-3/6)
• Week 6 (3/7-3/13)
• Week 7 (3/14-3/20)
• Week 8 (3/28-4/3)
• Week 9 (4/4-4/10)
• Week 10 (4/11-4/17)
• Week 11 (4/18-4/24)
• Week 12 (4/25-5/1)
• Week 13 (5/2-5/8)
• Week 14 (5/9-5/15)

Tools

• Numpy
  • Numpy Basics
    • Vectorized computations
    • Numpy arrays vs lists
    • Creating numpy arrays
    • Multidimensional numpy arrays
    • Mathematical operations on multidimensional arrays
    • Slicing multidimensional arrays
    • Sorting
    • Aggregation functions
  • Boolean and fancy indexing
    • Boolean arrays
    • Logical operations on Boolean arrays
    • Indexing with Boolean arrays
    • Fancy indexing
    • Fancy indexing example
  • Array shapes and broadcasting
    • Shapes of numpy arrays
    • Broadcasting
    • Broadcasting rules
• Pandas
  • Pandas basics
    • Pandas Series
    • Pandas DataFrame
    • Selecting data in a DataFrame
    • Sorting
    • Modifying DataFrames and Series
    • Aggregations
  • Reshaping DataFrames
    • Transpose
    • Setting and reseting an index
    • MultiIndex
    • Stack and unstack
  • GroupBy
    • The GroupBy object
    • Simple aggregations
    • GroupBy.agg
    • GroupBy.transform
    • GroupBy.apply
    • groupby options
  • Combining DataFrames
    • pd.concat
    • pd.merge
• Seaborn
  • Seaborn
    • Plots from dataframes
    • Figure-level and axes-level functions
    • Customizing plots
  • Seaborn plot types
    • Scatter plot
    • Line plot
    • Bar plot
    • Strip plot
    • Swarm plot
    • Box plot
    • Violin plot
    • Histogram plot
    • Joint plot
    • Pair plot
• Plotly
  • Plotly basics
    • Traces and figures
    • Figure layout
    • Underscore Notation
    • Updating layout
    • Updating traces
    • Subplots
    • Templates
  • Plotly Express
    • Plots from dataframes
    • Customizing plots
  • Plotly Express plot types
    • Scatter plot
    • Line plot
    • Bar plot
    • Strip plot
    • Box plot
    • Violin plot
    • Histogram plot
    • Sunburst plot
    • Marginal plots
    • Pair plot
    • Animated plots
    • Choropleth maps
• Requests
  • Requests basics
  • Binary files
  • User-Agent
  • Sending data
  • POST requests
• Beautiful Soup
  • First steps
  • Links
    • Link formats
  • Images
  • Tables
• JSON
  • The json module
  • Example: currency exchange rates
• Regular expressions
  • Regular expressions
    • Prelude: raw strings
    • Visualizing regular expressions
    • Character classes
    • Repetitions
    • Non-greedy matches
    • Match groups
    • Anchors
    • Flags
    • Inserting comments
    • Matching special characters
  • Functions in the re module
    • re.findall
    • re.sub
• SQL
  • SQL basics
    • SQL in Jupyter Notebook
    • Creating tables
    • Deleting tables
    • Creating table records
    • Modifying records
    • Deleting records
  • SQL queries
    • SELECT … FROM …
    • WHERE
    • ORDER
    • LIMIT
    • Aggregate functions
    • GROUP BY
    • HAVING
  • SQL joins, union, intersection
    • INTERSECT
    • UNION
    • EXCEPT
    • Joins
  • Views
  • SQL with Python

Projects

• Project: Recognizing digits with k-NN
  • MNIST database
  • Objectives
• Project: MNIST with k-means
  • Objectives
• Project: Baby names
  • Database of baby names
  • Objectives
• Project: Scrapping marathon results
  • Objectives
  • Notes
• Project: Discord logs
  • Objectives
    • Part 1
    • Part 2
  • Note
• Project: Analysis of marathon results
  • Objectives
• Project: Text classification with naive Bayes
  • Objectives
• Optional Project: Music database
  • Objectives
    • Notes.
    • Exercise 1
    • Exercise 2
    • Exercise 3
    • Exercise 4
    • Exercise 5
    • Exercise 6
    • Exercise 7
    • Exercise 8
    • Exercise 9
    • Exercise 10