Honors Data Analytics

Course Expectations

Below you'll find the links to the documentation for the Python libraries that we'll be using throughout the course:

Matplotlib.pyplot (styles, tight-layout, OOP?)

Regular Expressions

Numpy

Pandas

Data sets can be found here: https://github.com/bobg207/Honors_Data_Analysis

Unit 1: Review

Unit 2: Numpy (reason, creating, slicing, stats)

Unit 3: Pandas

    Part 1: Intro

     Part 2: Loading Data

     Part 3: Plotting

                       Portland Weather Data - Due 10/3

                 Euro 2012 - Due 10/7

                 Alcohol Consumption

Unit 4: Statistical Analysis

    Part 1: Descriptive Statistics

                 Auto_Data

    Part 2: Data Visualization and Analysis Presentation 

                  Website Times - Due 10/23

    Part 3: Correlation

                 Linear Regression Function - 10/29

                    - takes two panda's series as parameters

                    - returns the slope and y-intercept of the line of best fit

                    Data/Regression Line Plotting Function - 10/29

                    - takes 3 panda's series as parameters

                    - plots the data and regression line

                 Auto_Data Revisited - 11/8

    Part 4: Outliers, Residuals, and Root Mean Squared Error

                Explanation of the following are found in the link above.  Create a new 

                notebook with solutions contained within it.  Place your functions in the                        first cell after the imports.

                    4a - Function for removing outliers

                    4b - Function for calculating RMSE and R^2

                    Part 4 Project - use the 2018_MLB_Hitting_Stats.txt 

                                                 and 2018_MLB_Pitching_Stats_II.txt

                                                 files on GitHub

    Part 5: Higher Ordered Regression Fits

                           5a - Update Least Squares function to handle Quadratic Regression

                    5b - Auto Data Revisited - Submit when complete

                    5c - Bi-Linear Regression (Moneyball Problem)

                    5d - 3-D representation of Moneyball data - Submit when complete

    Part 6: K-Means Clustering

                    6a - Function to normalize data and function to find centroids

                    6b - Driving Clustering Data 

                           ** might start with this data set:

                  {'a':[1, 1.5, 5, 8, 1, 9], 'b':[2, 1.8, 8, 8, 0.6, 11]}

                   6c - Customer Data - Submit when complete

    Part 7: K-Nearest Neighbor and the Seattle Housing Data

                    7a - Create a function to separate a set of data into a training set and test 

                            set. (2/24)

                    7b - Start on Problems 8a-d from the lecture. (2/26)