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Natasha Balac, Ph.D.
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.
Read more
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.
Read more
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.
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
Register for this course and see more details by visiting:
OpenCourser.com/course/nzxnd7/code
What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
Data Manipulation and Visualization
Machine Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches predictive analytic approaches, which are increasingly used by organizations across all industries
Builds strong essential Data Science skills for those working with data analysis
Teaches students how to prepare data, build models, and evaluate results, which is applicable in many fields
Requires no programming background or experience, making it ideal for beginners in Data Science
Utilizes the KNIME Analytics Platform, an industry-standard tool used by professionals
Save this course
Save Code Free Data Science to your list so you can find it easily later:
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Reviews summary
Code free data science with knime
Learners say this course is a no-code option for learning the basics of data science with KNIME. Students remark that they were able to overcome the challenge of learning a new skill with the help of clear video lectures and supplemental materials. The course covers key topics such as organizing and visualizing big data, data manipulation, and machine learning techniques like decision trees and K-means. While some students mentioned occasional issues with data sets and outdated materials, the engaging assignments and well-structured content make this course a popular choice for beginners seeking a hands-on introduction to data science.
Course provides a solid foundation in KNIME software
"Great course, especially for those who have never had contact with Knime or with Big Data concepts before."
"A very nice , clear and concise introduction to the basic concepts and the Knime Platform."
Exercises reinforce learning and prepare students
"The exercises help to fix the commands."
"Good over all information provided on bigdara"
"Wonderful course to start learning Data science.Superb lectures.Knime - great platform to work with"
Instructors provide easy-to-understand explanations
"Very clearly explains the basics of using KNIME."
"The instructor did a good job to cover the DS topics as well."
"I have no background in coding, but I can complete this one."
Accessible content for those new to data science
"This course is so helpful for me as I am on the entry-level of data science learning."
"It's a good course for absolute beginners who want to learn "KNIME"."
Outdated materials and missing data can be a challenge
"Assignment 3 from Module 3 had me struggling for almost 2 weeks, all because of a spelling error in the question."
"Some of the assignments were too complicated for a beginner or weirdly worded"
"The first week of the course is mostly irrelevant"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Code Free Data Science with these
activities:
Review Data Science fundamentals
Show steps
Familiarize yourself with the basic concepts of Data Science to ensure you have a solid foundation for this course.
Browse courses on
Data Science
Show steps
-
Review key Data Science concepts such as data preprocessing, data mining, and machine learning algorithms.
-
Go through online tutorials or articles to refresh your understanding of data manipulation techniques.
-
Complete practice exercises or quizzes to test your knowledge of Data Science fundamentals.
Find a mentor in the Data Science field
Show steps
Seek guidance and support from an experienced professional in the Data Science field to enhance your learning journey.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through professional networks, conferences, or online platforms.
-
Reach out to potential mentors, express your interest, and inquire about their availability.
-
Establish regular meetings or communication channels to receive guidance and advice.
Explore the KNIME Analytics Platform
Show steps
Gain hands-on experience with the KNIME Analytics Platform, the tool you'll be using throughout this course.
Show steps
-
Follow online tutorials or documentation to learn about the KNIME interface and its capabilities.
-
Try out basic KNIME workflows to familiarize yourself with data loading, transformation, and visualization.
-
Explore the KNIME Hub to discover additional nodes and extensions that can enhance your workflows.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend industry webinars and conferences
Show steps
Connect with professionals in the Data Science field to expand your knowledge and stay informed about emerging trends.
Browse courses on
Networking
Show steps
-
Identify relevant webinars and conferences focused on Data Science and Machine Learning.
-
Attend these events virtually or in person to listen to experts and engage in discussions.
-
Network with other attendees, exchange ideas, and learn about new opportunities.
Attend Data Science workshops and hackathons
Show steps
Accelerate your learning through hands-on workshops and hackathons designed to provide practical experience in Data Science.
Show steps
-
Identify relevant Data Science workshops or hackathons that align with your interests.
-
Prepare for the event by reviewing relevant materials and practicing your skills.
-
Actively participate in the event, collaborate with others, and learn from experts.
Practice data manipulation and classification
Show steps
Reinforce your understanding of data manipulation and classification techniques by completing practical exercises.
Browse courses on
Data Manipulation
Show steps
-
Use KNIME to perform data cleaning and transformation tasks on real-world datasets.
-
Apply data classification algorithms to solve real-world problems, such as customer segmentation or fraud detection.
-
Participate in online challenges or competitions to test your skills against others.
Contribute to open-source Data Science projects
Show steps
Gain practical experience and contribute to the Data Science community by participating in open-source projects.
Browse courses on
Open Source
Show steps
-
Identify open-source Data Science projects that align with your interests and skills.
-
Review the project documentation and contribute code, documentation, or bug fixes.
-
Engage with the project community to learn from others and share your knowledge.
Build a predictive model using KNIME
Show steps
Apply your knowledge to create a predictive model using KNIME, demonstrating your understanding of the course concepts.
Browse courses on
Predictive Modeling
Show steps
-
Define a real-world problem that you want to solve using predictive modeling.
-
Gather and prepare the necessary data for your model.
-
Build and train a predictive model using KNIME's machine learning capabilities.
-
Evaluate the performance of your model and make adjustments as needed.
-
Document your project and share your findings with others.
Review Data Science fundamentals
Show steps
Familiarize yourself with the basic concepts of Data Science to ensure you have a solid foundation for this course.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Review key Data Science concepts such as data preprocessing, data mining, and machine learning algorithms.
- Go through online tutorials or articles to refresh your understanding of data manipulation techniques.
- Complete practice exercises or quizzes to test your knowledge of Data Science fundamentals.
Find a mentor in the Data Science field
Show steps
Seek guidance and support from an experienced professional in the Data Science field to enhance your learning journey.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks, conferences, or online platforms.
- Reach out to potential mentors, express your interest, and inquire about their availability.
- Establish regular meetings or communication channels to receive guidance and advice.
Explore the KNIME Analytics Platform
Show steps
Gain hands-on experience with the KNIME Analytics Platform, the tool you'll be using throughout this course.
Show steps
Show steps
Show steps
- Follow online tutorials or documentation to learn about the KNIME interface and its capabilities.
- Try out basic KNIME workflows to familiarize yourself with data loading, transformation, and visualization.
- Explore the KNIME Hub to discover additional nodes and extensions that can enhance your workflows.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend industry webinars and conferences
Show steps
Connect with professionals in the Data Science field to expand your knowledge and stay informed about emerging trends.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Identify relevant webinars and conferences focused on Data Science and Machine Learning.
- Attend these events virtually or in person to listen to experts and engage in discussions.
- Network with other attendees, exchange ideas, and learn about new opportunities.
Attend Data Science workshops and hackathons
Show steps
Accelerate your learning through hands-on workshops and hackathons designed to provide practical experience in Data Science.
Show steps
Show steps
Show steps
- Identify relevant Data Science workshops or hackathons that align with your interests.
- Prepare for the event by reviewing relevant materials and practicing your skills.
- Actively participate in the event, collaborate with others, and learn from experts.
Practice data manipulation and classification
Show steps
Reinforce your understanding of data manipulation and classification techniques by completing practical exercises.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Use KNIME to perform data cleaning and transformation tasks on real-world datasets.
- Apply data classification algorithms to solve real-world problems, such as customer segmentation or fraud detection.
- Participate in online challenges or competitions to test your skills against others.
Contribute to open-source Data Science projects
Show steps
Gain practical experience and contribute to the Data Science community by participating in open-source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source Data Science projects that align with your interests and skills.
- Review the project documentation and contribute code, documentation, or bug fixes.
- Engage with the project community to learn from others and share your knowledge.
Build a predictive model using KNIME
Show steps
Apply your knowledge to create a predictive model using KNIME, demonstrating your understanding of the course concepts.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Define a real-world problem that you want to solve using predictive modeling.
- Gather and prepare the necessary data for your model.
- Build and train a predictive model using KNIME's machine learning capabilities.
- Evaluate the performance of your model and make adjustments as needed.
- Document your project and share your findings with others.
Career center
Learners who complete Code Free Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you will lead projects with an ultimate goal of improving business by leveraging data. This course may be useful to you by teaching you about predictive analytics without programming. You'll gain essential skills like designing predictive models and interpreting data.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will build and deploy machine learning models for business. Predictive analytics are important to this field. This course may be helpful by introducing you to the KNIME Analytics Platform. You'll get hands-on experience applying algorithms to real data.
Business Analyst
Business Analyst
As a Business Analyst, you'll be involved in the planning and execution of data-driven strategies. This course will introduce you to the fundamentals of data science. You'll learn how to use predictive analytics to solve problems and make recommendations.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to solve complex problems. Predictive analytics is important for this field and this course will teach you the basics. Also, you'll gain experience with the KNIME Analytics Platform.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course will introduce you to data science and help you learn how to use analytics tools. You'll also gain experience in using the KNIME Analytics Platform.
Data Architect
Data Architect
As a Data Architect, you'll design and build data storage systems. This course will introduce you to data science and help you build a foundation by teaching you how to use analytics tools. You'll also get experience with data manipulation and visualization.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course will help you develop your essential skills, like designing predictive models and interpreting data.
Database Administrator
Database Administrator
As a Database Administrator, you'll manage and maintain databases and other related software. This course will introduce you to data science and teach you how to use analytics tools. You'll also gain experience with data manipulation and visualization.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make recommendations. This course will help build your data science knowledge, teaching you how to use analytics tools and interpret data. You'll also get experience with data manipulation and visualization.
Product Manager
Product Manager
As a Product Manager, you'll create and manage products. This course will help you build a foundation by teaching you how to use data science and analytics tools. You'll also gain experience with data manipulation and visualization.
Operations Manager
Operations Manager
As an Operations Manager, you'll plan, organize, and direct activities to ensure efficient operations. This course will introduce you to data science and teach you how to use analytics tools. You'll also gain experience with data manipulation and visualization.
Data Engineer
Data Engineer
As a Data Engineer, you'll build and maintain systems for storing and processing data. This course will help you build a foundation by introducing you to data science and teaching you how to use analytics tools. You'll also get experience with data manipulation and visualization.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you'll use data to make investment decisions. This course may be useful by introducing you to data science and teaching you how to use predictive analytics. You'll also gain experience with the KNIME Analytics Platform.
Actuary
Actuary
As an Actuary, you'll use data to assess risk and uncertainty. This course may be useful by introducing you to data science and teaching you how to use predictive analytics.
Market Research Analyst
Market Research Analyst
As a Market Research Analyst, you'll collect, analyze, and interpret data to inform marketing decisions. Predictive analytics will be an important skill to have. This course may help you learn the basics and gain experience with the KNIME Analytics Platform.
For more career information including salaries, visit:
OpenCourser.com/course/nzxnd7/code
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Code Free Data Science.
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, regression, and classification. It is written by a leading researcher in the field, making it a valuable resource for anyone interested in learning about pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning, covering topics such as regression, classification, and clustering. It is written by leading researchers in the field, making it a valuable resource for anyone interested in learning about statistical learning.
Provides a comprehensive overview of machine learning, covering topics such as supervised and unsupervised learning, regression, and classification. It is written by a leading researcher in the field, making it a valuable resource for anyone interested in learning about machine learning.
Save
Provides a comprehensive overview of deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It is written by leading researchers in the field, making it a valuable resource for anyone interested in learning about deep learning.
Provides a comprehensive overview of big data analytics, covering topics such as data collection, storage, processing, and analysis. It is written by leading researchers in the field, making it a valuable resource for anyone interested in learning about big data analytics.
Provides a practical introduction to machine learning, covering topics such as data preprocessing, model selection, and hyperparameter tuning. It is written in a clear and concise style, making it a great resource for beginners and experienced practitioners alike.
Save
Provides a comprehensive overview of data mining, covering topics such as data preprocessing, model selection, and evaluation. It is written by leading researchers in the field, making it a valuable resource for anyone interested in learning about data mining.
Provides a comprehensive overview of data science, covering topics such as data collection, preparation, analysis, and visualization. It is written in a clear and concise style, making it an excellent resource for beginners.
Provides a comprehensive overview of data mining, covering topics such as data preprocessing, model selection, and evaluation. It is written by leading researchers in the field, making it a valuable resource for anyone interested in learning about data mining.
Save
Provides a practical introduction to data science, covering topics such as data collection, preparation, analysis, and visualization. It is written by leading researchers in the field, making it a valuable resource for anyone interested in learning about data science.
Provides a practical introduction to machine learning, covering topics such as supervised and unsupervised learning, regression, and classification. It is written in a clear and concise style, making it a great resource for beginners.
Provides a practical introduction to data science, covering topics such as data collection, preparation, analysis, and visualization. It is written in a clear and concise style, making it a great resource for beginners.
Provides a gentle introduction to machine learning, covering topics such as supervised and unsupervised learning, regression, and classification. It is written in a friendly and approachable style, making it a great resource for beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nzxnd7/code
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Level
Introductory
Via
Coursera
Institution
University of California San Diego
Instructor
Natasha Balac, Ph.D.
Language
English
Transcripts
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Good to know
Teaches predictive analytic approaches, which are increasingly used by organizations across all industries
Builds strong essential Data Science skills for those working with data analysis
Teaches students how to prepare data, build models, and evaluate results, which is applicable in many fields
Requires no programming background or experience, making it ideal for beginners in Data Science
Utilizes the KNIME Analytics Platform, an industry-standard tool used by professionals
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