Coursera

Code Free Data Science (Coursera)

Offered by University of California, San Diego,

The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data.

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Predicting future trends and behaviors allows for proactive, data-driven decisions. During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming. Participants will gain the essential skills to design, build, verify and test predictive models.

What You Will Learn
• How to design Data Science workflows without any programming involved
• Essential Data Science skills to design, build, test and evaluate predictive models
• Data Manipulation, preparation and Classification and clustering methods
• Ways to apply Data Science algorithms to real data and evaluate and interpret the results

Syllabus

WEEK 1
Welcome to the world of Big Data
Welcome to the first module of the Code Free Data Science course. This first module will provide insight into Big Data Hype, its technologies opportunities and challenges. We will take a deeper look into the Big Data Analytics and methodology associated with Data Science approaches.

WEEK 2
Introduction to KNIME Analytics Platform
This module will introduce the KNIME analytics platform. Learners will be guided to download, install and setup KNIME. We will explore and become familiar with the KNIME workflow editor and its components. In this module we will create the very first basic workflow, and explore the kinds of analysis KNIME empowers users to perform.

WEEK 3
Data Manipulation and Visualization

WEEK 4
Machine Learning

Go to Class

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