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Code Free Data Science

Approx. 14 hours to complete

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Course Summary

Learn how to perform data science without coding using drag-and-drop tools and pre-built algorithms. This course will teach you the basics of data analysis, visualization, and machine learning without requiring any programming skills.

Key Learning Points

No coding skills required
Learn data analysis, visualization, and machine learning
Use drag-and-drop tools and pre-built algorithms

Job Positions & Salaries of people who have taken this course might have

- USA: $62,453
- India: ₹6,98,289
- Spain: €30,000
- USA: $62,453
- India: ₹6,98,289
- Spain: €30,000
- USA: $70,065
- India: ₹6,05,000
- Spain: €27,000
- USA: $62,453
- India: ₹6,98,289
- Spain: €30,000
- USA: $70,065
- India: ₹6,05,000
- Spain: €27,000
- USA: $112,160
- India: ₹10,00,000
- Spain: €55,000

Learning Outcomes

Perform data analysis and visualization without coding
Understand the basics of machine learning
Use drag-and-drop tools and pre-built algorithms to perform data science tasks

Prerequisites or good to have knowledge before taking this course

No coding skills required
Basic knowledge of statistics is helpful

Course Difficulty Level

Beginner

Course Format

Online
Self-paced

Similar Courses

Data Science Essentials
Python Data Analysis
Machine Learning for Everyone

Related Education Paths

Notable People in This Field

Kirk Borne
Hilary Mason

Related Books

Description

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.

Knowledge

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

Outline

Welcome to the world of Big Data
Welcome to the Code Free Data Science Class
Welcome to Module 1
First Joke
Big Data Characteristics and Applications
Big Data Applications
Big Data Analytics
Introduction to Data Mining
Big Data Impact and Challanges
Big Data Analytics: Applications, Prospects and Challenges
10 signs of Data Science Maturity
NIST Big Data interoperability Framework (NBDIF)
Big Data Quiz
Module 1 quiz

Introduction to KNIME Analytics Platform
Welcome to Module 2
Module 2 Joke
Introduction to KNIME Analytics Platform
Install KNIME Analytics Platform
Exploring KNIME Workspace
Creating new workflows in KNIME
My First KNIME Workflow Exercise
Node Operations in KNIME
Data Types and Formats in KNIME
Creating Analytics Workflow in KNIME
KNIME Analytics Platform Web site link
Download KNIME
KNIME Installation Guide
KNIME getting Started
KNIME workbench guide
Example Workflows
KNIME Whitepaper
adult.csv data set
KNIME Book Chapter 1
Autos Data Set
Iris.csv data
Install KNIME Quiz
Exploring KNIME
Filtering Data Quiz
Filtering Workflow Assignment

Data Manipulation and Visualization
Intro to Module 3
Intro to Data Manipulation in KNIME
Rule Engine
String Manipulation
String Replacer
String Splitting and Column Combining
Column Resorter
Type Conversions
Database Operations
Scatterplot
Color Manager in KNIME
Line Plot
Parallel Coordinate Plot
Bar Charts

Machine Learning
Welcome to Module 4
Module 4 Joke
Introduction to Data Science
Introduction to Decision Tree Induction
Building Decision Tree Model in KNIME
Decision Tree Workflow
Intro to K-means Clustering
K-means Clustering In KNIME
Decision Tree Reading
Decision Trees Examples in KNIME
Book Chapter on Clustering
Decision Tree Assignment

Summary of User Reviews

Code-Free Data Science course on Coursera has received positive reviews from users. Many users have praised the course for its comprehensive content and practical approach. The course has an overall high rating.

Key Aspect Users Liked About This Course

The practical approach to teaching data science

Pros from User Reviews

Comprehensive content that covers all aspects of data science
Hands-on approach with practical exercises and projects
Easy to follow and understand instructions
Great for beginners and those with no programming experience
Flexible schedule that allows learners to study at their own pace

Cons from User Reviews

Some users found the course too basic and not challenging enough
Limited interaction with instructors and other learners
No certification or credentials offered upon completion
Lack of real-world examples and case studies
Some technical issues with the platform and course materials

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