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
Related Topics for further study
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
BeginnerCourse 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 sciencePros 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
Natasha Balac, Ph.D.
4.3 Raiting
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