4 Week Data Science Applied Labs
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4 Week Data Science Applied Labs

4 Week Data Science Applied Labs

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8 Whatney

Suite 124

Irvine, CA 92618

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Data Science

This workshop will guide the aspiring Data Scientist through modeling and machine learning. You will use machine learning methods to validate and evaluate data models.

As a beginning Data Scientist, you will learn how to load data into Python. Interpret and visualize the data, while dealing with variables and missing values. We will teach you how to come to sound conclusions about your data, despite some real-world challenges.

By the end of this course, you will have an understanding of applied predictive modeling methods, and the know how to use existing machine learning methods in Python. This will allow you to work with team members in a data science projects, find problems, and come up solutions.

This course is for IT professionals aspiring to be Data Scientists, students who want to learn about Data Science, Statisticians, Project Managers who want to expand their horizon into Data Science, and any person who is interested in Data Science.


In this workshop you’ll learn an in-depth process of Data Science :

  • Collect data from a variety of sources (e.g., Excel, web scraping, APIs and others)
  • Explore large data sets
  • Learn to use Python for executing Data Science Projects
  • Master the application of Analytics and Machine Learning techniques
  • Know how to use matplotlib and seaborn libraries to create beautiful data visualization.

This is a very practical and hands-on workshop that has lots of class exercises. Through this course, we strive to make you fully equipped to become a developer who can execute full-fledged Data Science projects.


Session I: Free Python Foundation Course!!

  • About Python
  • Data Types
  • Conditionals
  • Loops
  • Functions

Session II: The Basics

  • Data Collection and Exploration
  • Data Cleaning and Visualization
  • Introduction to Machine Learning


Session III: Fundamental Modeling Techniques

  • K-Nearest Neighbors Classification
  • Naive Bayes Classification
  • Regression and Regularization
  • Logistic Regression

Session IV: Modeling Techniques Continued & Analytics

  • K-Means Clustering
  • Ensemble Techniques
  • Dimensionality Reduction
  • Decision Trees and Random Forests


Session V: Tools

  • Recommendations Systems
  • Database Technologies
  • Map-Reduce


Session VI: Hack-A-Day

  • Final Project Working Session
  • Final Project Presentation
  • Discussion of resources & tools

Prereqs & Preparation

You must bring a laptop with a text editor.

Sublime Text is recommended and has a free trial version (http://www.sublimetext.com/).

In addition, students should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (http://continuum.io/downloads).

Anyone taking this course should have some minimum experience with programming with R, Python, or any other programming language.

If not, we offer our Python Foundation Course for Free!!!

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8 Whatney

Suite 124

Irvine, CA 92618

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