Data Sciience Training

Data Science Course Reviews

The course is practical oriented. Good place for learning.

Data Science Course Content

Introduction to Data Science

• Big Data and Data Science hype – and getting past the hype
• Why now? – Datafication
• Current landscape of perspectives
• Skill sets needed

Statistical Inference

• Populations and samples
• Statistical modeling, probability distributions, fitting a model
• Intro to R

Exploratory Data Analysis and the Data Science Process

• Basic tools (plots, graphs and summary statistics) of EDA
• Philosophy of EDA
• The Data Science Process
• Case Study: RealDirect (online real estate firm)

Three Basic Machine Learning Algorithms

• Linear Regression
• k-Nearest Neighbors (k-NN)
• k-means

One More Machine Learning Algorithm and Usage in Applications

• Motivating application: Filtering Spam
• Why Linear Regression and k-NN are poor choices for Filtering Spam
• Naive Bayes and why it works for Filtering Spam
• Data Wrangling: APIs and other tools for scrapping the Web

Feature Generation and Feature Selection (Extracting Meaning From Data)

• Motivating application: user (customer) retention
• Feature Generation (brainstorming, role of domain expertise, and place for imagination)
• Feature Selection algorithms
• Filters; Wrappers; Decision Trees; Random Forests

Recommendation Systems: Building a User-Facing Data Product

• Algorithmic ingredients of a Recommendation Engine
• Dimensionality Reduction
• Singular Value Decomposition
• Principal Component Analysis
• Exercise: build your own recommendation system

Mining Social-Network Graphs

• Social networks as graphs
• Clustering of graphs
• Direct discovery of communities in graphs
• Partitioning of graphs
• Neighborhood properties in graphs

Data Visualization

• Basic principles, ideas and tools for data visualization
• Examples of inspiring (industry) projects
• Exercise: create your own visualization of a complex dataset

Data Science and Ethical Issues

• Discussions on privacy, security, ethics
• A look back at Data Science
• Next-generation data scientists