BUS 6121 - Data Wrangling and Exploration

• Demonstrate a broad knowledge of the field of business analytics, including trends and challenges
• Evaluate sources of data and opportunities for data collection and use across an organization
• Perform data wrangling using appropriate methods and tools for a given analytics scenario
• Apply appropriate data exploration techniques for a given analytics scenario
• Use data visualization techniques to gain insights into the meaning of the data
• Communicate the meaning of statistical or analytic results to stakeholders
• Demonstrate the ability to analyze and write statistical program code in Python that automates the processing of statistical data sets that are not conducive to manual processing due to their size and/or scale

• None

• Python language environments
  • Installation, configuration, and maintenance of Python environments using shared environments (Rosie, Google CoLab, etc.) and single-user environments (Anaconda) with Jupyter Notebooks
• Python language basics
  • Expressions, operators (arithmetic, logical, and relational), data types (none, integers, floating point numeric and object), compound expressions leading to statements
  • Flow of control in Python (sequential, branching (if/elif/else), iteration (for loops, while loops, ranges, else parts to loops, iteration over collections), functions and lambda expressions). Points of syntax - importance of colons and tab stops
  • Collections in Python (lists, tuples, dictionaries, sets) indexing and slicing, iteration over collections using comprehensions, iterable objects, and generators
  • Classes and object-oriented programming in Python (instance variables, methods, operator overloading, inheritance, polymorphism)
• Numeric Python (NumPy)
  • Data structures (array and n-dimensional arrays or ndarrays)
  • Indexing, sorting, finding unique elements, random number generation, file I/O
• Pandas library
  • Series and DataFrame data structures
  • Indexing, axis manipulation, selection, filtering, sorting, ranking, summarizing
  • File I/O between Series, DataFrame and files on disk, file formats
  • Reading and writing data using various file formats (text, JSON, HTML, XML, Microsoft Excel)
• Data cleaning and preparation
  • Handling missing data, filtering out bad/missing data, filling in missing data
  • “Binning” and aggregating data values, making continuous variables discrete
  • Indicator matrices and dummy variables
• Data wrangling: join, combine, reshape
  • Hierarchical indexing, summarizing statistics by level
  • Combining and merging data sets, SQL-style “joins” among DataFrames
  • Reshaping and pivoting DataFrames
• Data visualization
  • Plotting with mathplotlib
  • Figures and subplots
  • Line graphs, bar charts, box plots, histograms, density diagrams, scatter plots, etc.
  • Seaborn, Bokeh, and other visualization libraries built on matplotlib
• Data aggregation and group operations
  • The GroupBy operator
  • The Split-Apply-Combine pragma for manipulating data
  • Processing large data sets efficiently
• Time Series Basics
  • Date ranges, frequencies, and shifting
  • Time zone handling
  • Periods and period arithmetic
  • Resampling and frequency conversion
  • Moving window functions