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. 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.
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What you'll 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 cclassification and clustering methods
Ways to apply Data Science algorithms to real data and evaluate and interpret the results
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There are 4 modules in this course
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.
What's included
7 videos4 readings2 assignments3 discussion prompts
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.
What's included
10 videos11 readings5 assignments
What's included
14 videos2 assignments
What's included
8 videos3 readings3 assignments
Instructor
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Recommended if you're interested in Machine Learning
Johns Hopkins University
Illinois Tech
EIT Digital
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Frequently asked questions
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