Introduction: Welcome and overview of the course. Introduction to the data science process and the value of learning data science.

Background: In this optional Topic, we provide a brief background in python or unix to get you up and running.

Jupyter and Numpy: Jupyter notebooks are one of the most commonly used tools in data science as they allow you to combine your research notes with the code for the analysis. After getting started in Jupyter, we'll learn how to use numpy for data analysis. numpy offers many useful functions for processing data as well as data structures which are time and space efficient.

Pandas: Pandas, built on top of numpy, adds data frames which offer critical data analysis functionality and features.

Visualization: When working with large datasets, you often need to visualize your data to gain a better understanding of it. Also, when you reach conclusions about the data, you'll often wish to use visualizations to present your results.

Mini Project: With the tools of Jupyter notebooks, numpy, pandas, and Visualization, you're ready to do sophisticated analysis on your own. You'll pick a dataset we've worked with already and perform an analysis for this first project.

Machine Learning: To take your data analysis skills one step further, we'll introduce you to the basics of machine learning and how to use sci-kit learn - a powerful library for machine learning.

Working with Text and Databases: You'll find yourself often working with text data or data from databases. This Topic will give you the skills to access that data. For text data, we'll also give you a preview of how to analyze text data using ideas from the field of Natural Language Processing and how to apply those ideas using the Natural Language Processing Toolkit (NLTK) library.

Final Project: These Topics let you showcase all your new skills in an end-to-end data analysis project. You'll pick the dataset, do the data munging, ask the research questions, visualize the data, draw conclusions, and present your results

• Machine Learning Languages, Types, and Examples
• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning
• Supervised Learning Classification
• Unsupervised Learning

• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Reliability of Random Forests
• Advantages & Disadvantages of Decision Trees

• Regression Algorithms
• Model Evaluation
• Model Evaluation: Overfitting & Underfitting
• Understanding Different Evaluation Models

• K-Means Clustering plus Advantages & Disadvantages
• Hierarchical Clustering plus Advantages & Disadvantages
• Measuring the Distances Between Clusters - Single Linkage Clustering
• Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
• Density-Based Clustering

• Predicting numeric values: regression
• Tree-based regression

• Grouping unlabeled items using k-means clustering
• Association analysis with the Apriori algorithm
• Efficiently finding frequent itemsets with FP-growth

• Using principal component analysis to simplify data
• Simplifying data with the singular value decomposition
• Big data and MapReduce