Data-Science-Python

The repository contains Data Science related assignments or projects. The sources are either come from a class at Florida Atlantic University (with permission from the professor to post old assignments) or side project from the web.

Current Topics:

1. The Python Data Science Stack

• Compute Summary Statistics
• Compute Pearson coefficient
• Test Hypothesis
• Data Cleaning/Data preprocessing
• Plot histogram, kernel density estimate (KDE) plot, lmplot (regression and fit), Box-and-whisker plots

2. Exploratory Data Analysis

• Exploratory dada analysis based on the content of each dataset
• Plot barplot, pie plot, histogram, line graph
• Data Manipulation (pivot table, group by, etc)
• Hypothesis Testing

3. Statistical Analysis Part 1, Part 2

• Compute Summary Statistics
• Compute Empirical Cumulative Distribution Function (ECDF)
• Compute Percentiles
• Computes the Pearson Correlation Coefficient
• Compute the Cumulative Distribution Function (CDF) and Complementary CDF(CCDF)
• Compute Moments
• Plot the Probability Density Function(PDF) & Cumulative Distribution Function(CDF)
• Plot histogram, swarmplot, Box-and-whisker plots, Scatter plots, Pair plots

4. Hypothesis Testing

• Hypothesis Testing
• Compute Empirical Cumulative Distribution Function (ECDF)
• Compute p-values
• Test of Correlation

5. Regression Analysis Part 1, Part 2, Part 3

• Linear Regression by least squares
• Linear Regression on Anscombe's quartet data
• Polynomial Regression
• Regularization in Polynomial Regression: Ridge regression (L2 Regularization), Lasso regression (L1 Regularization)
• Compute RMSE and R^2 score
• Multiple Regression
• Logistic Regression

6. Data Science / Machine Learning workflow (CAP5768 Final Project)

• Compute Summary Statistics
• Decision Trees
• Plot Pair plots
• Digit classification with Naive Bayes classifier using scikit-learn's MultinomialNB()
• Digit classification with RandomForestClassifier
• Hyperparameter Optimization with GridSearchCV
• Face Recognition with multi-class SVM classifier
• Principal Component Analysis (PCA)

7. Affect of Outliers on Central Tendency Measures

• Mean, Median, and Mode