Intro to Course and Python
- Course Intro
- Course FAQs
Setup
- Installation Setup and Overview
- IDEs and Course Resources
- iPython/Jupyter Notebook Overview
Learning Numpy
- Intro to numpy
- Creating arrays
- Using arrays and scalars
- Indexing Arrays
- Array Transposition
- Universal Array Function
- Array Processing
- Array Input and Output
Intro to Pandas
- Series
- DataFrames
- Index objects
- Reindex
- Drop Entry
- Selecting Entries
- Data Alignment
- Rank and Sort
- Summary Statistics
- Missing Data
- Index Hierarchy
Working with Data: Part 1
- Reading and Writing Text Files
- JSON with Python
- HTML with Python
- Microsoft Excel files with Python
Working with Data: Part 2
- Merge
- Merge on Index
- Concatenate
- Combining DataFrames
- Reshaping
- Pivoting
- Duplicates in DataFrames
- Mapping
- Replace
- Rename Index
- Binning
- Outliers
- Permutation
Working with Data: Part 3
- GroupBy on DataFrames
- GroupBy on Dict and Series
- Aggregation
- Splitting Applying and Combining
- Cross Tabulation
Data Visualization
- Installing Seaborn
- Histograms
- Kernel Density Estimate Plots
- Combining Plot Styles
- Box and Violin Plots
- Regression Plots
- Heatmaps and Clustered Matrices
Example Projects.
- Data Projects Preview
- Intro to Data Projects
- Titanic Project - Part 1
- Titanic Project - Part 2
- Titanic Project - Part 3
- Titanic Project - Part 4
- Intro to Data Project - Stock Market Analysis
- Data Project - Stock Market Analysis Part 1
- Data Project - Stock Market Analysis Part 2
- Data Project - Stock Market Analysis Part 3
- Data Project - Stock Market Analysis Part 4
- Data Project - Stock Market Analysis Part 5
- Data Project - Intro to Election Analysis
- Data Project - Election Analysis Part 1
- Data Project - Election Analysis Part 2
- Data Project - Election Analysis Part 3
- Data Project - Election Analysis Part 4
Machine Learning
- Introduction to Machine Learning with SciKit Learn
- Linear Regression Part 1
- Linear Regression Part 2
- Linear Regression Part 3
- Linear Regression Part 4
- Logistic Regression Part 1
- Logistic Regression Part 2
- Logistic Regression Part 3
- Logistic Regression Part 4
- Multi Class Classification Part 1 - Logistic Regression
- Multi Class Classification Part 2 - k Nearest Neighbor
- Support Vector Machines Part 1
- Support Vector Machines - Part 2
- Naive Bayes Part 1
- Naive Bayes Part 2
- Decision Trees and Random Forests
- Natural Language Processing Part 1
- Natural Language Processing Part 2
- Natural Language Processing Part 3
- Natural Language Processing Part 4
