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 R and/or 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
  • I-Python (Interactive Python) console mode, Integrated Development Environment (IDE) and Jupyter Notebook environment
  • Installation, configuration, and maintenance of I-Python environments using shared environments (Rosie, Google CoLab, etc.) and single-user environments (Anaconda)
• 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
• Machine learning basics
  • Linear regression fundamentals, simple and multivariate linear models
  • Training sets, test sets, validation sets, and other data set partitioning
  • Closed form equation solution for linear regression on small data sets
  • Iterative linear regression techniques for large data sets
  • Learning rates, error surfaces, residual analysis
  • Polynomial regression, logistic regression