Will I get hands-on practice using KNIME?

Yes. The course includes multiple hands-on demonstrations and workflow-building exercises using KNIME for real-world data collection and processing tasks.

What data sources will I learn to work with?

You will work with CSV and Excel files, JSON and XML data, relational databases, APIs, and web-scraped data sources.

What skills will I gain from this course?

You will gain skills in data collection, data cleaning, transformation, exploratory data analysis, and building automated no-code data workflows using KNIME.

Is this course suitable for beginners in data science?

Yes. The course starts with foundational concepts and gradually progresses to more advanced data processing and analysis techniques.

How is my learning evaluated in this course?

Learning is evaluated through practice quizzes, module-level graded assessments, and a final comprehensive course assessment.

How long will it take to complete the course?

The course duration varies by learner, but most participants can complete it within a few weeks with regular study and hands-on practice.

KNIME is a leading open-source no-code analytics platform used globally to build data workflows, clean datasets, and generate insights — without writing code.

How are the hands-on demonstrations structured?

Each concept is taught through step-by-step instructor video demonstrations. You're encouraged to set up KNIME on your own machine, follow along with the instructor, and pause the video whenever you need time to replicate a step or explore the interface.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Build Your First No-Code Data Workflow

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Build Your First No-Code Data Workflow

Instructor: Edureka

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Navigate the KNIME interface and build end-to-end no-code data workflows to collect data from files, databases, APIs, and web sources
Collect and integrate data from multiple sources including files, databases, APIs, and web sources by building structured no-code workflows in KNIME.
Identify, analyze, and resolve data quality issues using appropriate cleaning and transformation techniques in KNIME workflows.
Apply statistical summaries and visualizations to evaluate data patterns, relationships, and insights for informed decision-making.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

15 assignments¹

AI Graded see disclaimer

Taught in English

See how employees at top companies are mastering in-demand skills

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There are 5 modules in this course

Data science doesn't require code anymore. This course equips you with the practical skills to collect, clean, transform, and explore data using KNIME Analytics Platform — one of the world's most widely used no-code data science tools — so you can start building real workflows from day one.

Throughout this course, you'll explore the full data science lifecycle and discover how no-code platforms are transforming the way analysts, business professionals, and domain experts work with data at scale. Each concept is reinforced through step-by-step video demonstrations that you can follow along on your own KNIME setup — pause, rewind, and practice at your own pace. By the end of this course, you'll be able to: - Navigate the KNIME interface and build end-to-end no-code data workflows from scratch. - Collect and integrate data from CSV, Excel, JSON, XML, databases, APIs, and web scraping sources. - Clean, transform, and validate data using normalization, encoding, aggregation, and feature engineering techniques. - Perform exploratory data analysis using descriptive statistics, visualizations, correlation matrices, and heatmaps in KNIME. This course is designed for a diverse audience: aspiring data analysts, business analysts, operations and marketing professionals, students entering data science, and anyone looking to gain practical data skills without programming knowledge. Prior familiarity with spreadsheet tools like Excel is helpful, though no technical or coding background is required to succeed in this course. Take the first step toward no-code data mastery and build the foundational skills needed to work confidently with real-world data using KNIME.

St rengthen your foundational understanding of data science by exploring its core concepts, lifecycle, and real-world impact across industries. Learn how data-driven solutions are structured, the types of data used in analytics, and how no-code platforms enable faster insights. Gain hands-on exposure to the KNIME interface, workflows and data formats to build a strong base for practical data science without programming.

What's included

8 videos4 readings3 assignments

8 videos Total 35 minutes

Course Introduction 3 minutes
What is Data Science? 4 minutes
The Data Science Lifecycle 4 minutes
Case Study- Implementing Data Science at an OTT Giant 6 minutes
Types of Data 6 minutes
Introduction to No-Code Data Science 5 minutes
Traditional vs No-Code Workflow 5 minutes
Hands-On: Installing KNIME and Exploring the Interface 3 minutes

4 readings Total 40 minutes

Course Outline: No-Code Data Preparation and Analysis with KNIME 10 minutes
Introduction to Data Science 10 minutes
Understanding Data Formats and KNIME 10 minutes
Module Summary: Foundations of Data Science & Introduction to KNIME 10 minutes

3 assignments Total 27 minutes

Practice Assignment: Introduction to Data Science 6 minutes
Practice Assignment : Understanding Data Formats and KNIME 6 minutes
Graded Assignment: Foundations of Data Science & Introduction to KNIME 15 minutes

Develop practical data acquisition skills by learning how to collect, access, and integrate data from multiple sources and formats. Gain hands-on experience working with structured and semi-structured data, including CSV, Excel, JSON, XML, databases and web-based sources. Learn how to build your first KNIME workflows, connect to databases, and extract data using APIs and web scraping techniques for real-world data collection scenarios.

What's included

10 videos3 readings3 assignments

10 videos Total 46 minutes

Data Collection and Cleaning Overview 4 minutes
Data Formats and Storage Structures 7 minutes
Hands-On: Viewing and Exploring Data in KNIME 4 minutes
Hands-On: Reading CSV and Excel Files in KNIME 4 minutes
Hands-On: Creating Your First Workflow 5 minutes
Hands-On: Working with JSON + XML Data in KNIME 7 minutes
Essentails to Databases and SQL Operations 4 minutes
Hands-On: Connecting to Databases (SQLite) in KNIME 4 minutes
Collecting Web Data Through APIs and Web Scrapping 4 minutes
Hands-On: Web Scraping with KNIME 5 minutes

3 readings Total 30 minutes

Data Acquisition and Working with Different Data Formats 10 minutes
Database and Web Data Collection 10 minutes
Module Summary: Data Collection -Methods, Sources and Techniques 10 minutes

3 assignments Total 27 minutes

Practice Assignment: Data Acquisition and Working with Different Data Formats 6 minutes
Practice Assignment : Database and Web Data Collection 6 minutes
Graded Assignment: Data Collection : Methods, Sources and Techniques 15 minutes

Enhance your ability to prepare high-quality data by identifying and resolving common data issues such as missing values, duplicates, and inconsistencies. Learn essential data transformation techniques including normalization, standardization, encoding, aggregation, and feature creation. Gain hands-on experience building automated data processing pipelines in KNIME to ensure clean, reliable, and analysis-ready datasets.

What's included

12 videos5 readings4 assignments

12 videos Total 56 minutes

Exploring Dimensions of data quality 4 minutes
Hands-On: Identifying Missing Data Patterns and Outliers in KNIME 5 minutes
Hands-On: Handling Missing Values and Removing Duplicates in KNIME 5 minutes
Data Transformation using Normalization 4 minutes
Data Transformation using Standardization 4 minutes
Data Transformation using Encoding 4 minutes
Hands-On: Scaling and Normalization in KNIME 7 minutes
Hands-On: Encoding Categorical Variables in KNIME 4 minutes
Hands-On: Data Aggregation and Grouping in KNIME 4 minutes
Creating New Variables Through Feature Engineering 3 minutes
Hands-On: Feature Creation Techniques in KNIME 6 minutes
Hands-On: Building Automated Data Processing Pipelines 6 minutes

5 readings Total 50 minutes

Data Quality and Cleaning 10 minutes
Normalization vs. Standardization 10 minutes
Data Transformation Techniques 10 minutes
Feature Engineering and Pipeline Automation 10 minutes
Module Summary: Data Processing - Cleaning, Transformation and Quality 10 minutes

4 assignments Total 33 minutes

Practice Assignment: Data Quality and Cleaning 6 minutes
Practice Assignment : Data Transformation Techniques 6 minutes
Practice Assignment : Feature Engineering and Pipeline Automation 6 minutes
Graded Assignment: Data Processing: Cleaning, Transformation and Quality 15 minutes

Strengthen your analytical skills by learning how to explore, summarize, and interpret data effectively. Apply descriptive statistics, distribution analysis, and visualization techniques to uncover patterns, trends, and relationships within datasets. Gain hands-on experience creating charts, correlation matrices, and heatmaps in KNIME to support data-driven insights and informed decision-making.

What's included

8 videos3 readings3 assignments

8 videos Total 33 minutes

Exploratory Data Analysis for Better Decision-Making 3 minutes
Summarizing Data Using Descriptive Statistics 4 minutes
Hands-On: Statistical Analysis in KNIME 7 minutes
Hands-On: Understanding Data Distribution and Outliers in KNIME 5 minutes
Data Visualization Principles and Chart Types 3 minutes
Hands-On: Creating Visualizations in KNIME 4 minutes
Understanding Data Relationships 3 minutes
Hands-On: Correlation Matrix and Heatmaps in KNIME 3 minutes

3 readings Total 30 minutes

Statistical Analysis and Data Distribution 10 minutes
Data Visualization and Correlation Analysis 10 minutes
Module Summary: Exploratory Data Analysis (EDA) 10 minutes

3 assignments Total 27 minutes

Practice Assignment: Statistical Analysis and Data Distribution 6 minutes
Practice Assignment: Data Visualization and Correlation Analysis 6 minutes
Graded Assignment: Exploratory Data Analysis (EDA) 15 minutes

This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz.

What's included

1 video1 reading2 assignments1 discussion prompt

1 video Total 3 minutes

Course Summary 3 minutes

1 reading Total 30 minutes

Practice Project: Building a No-Code Data Intelligence System for MarketPulse Analytics 30 minutes

2 assignments Total 60 minutes

Knowledge Check: Getting Started with No-Code Data Science 30 minutes
Strategic Implementation of a No-Code Data Intelligence Pipeline Using KNIME 30 minutes

1 discussion prompt Total 5 minutes

Describe Your Learning Journey 5 minutes

Instructor

Edureka

153 Courses 141,745 learners

Offered by

Edureka

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Frequently asked questions

This course is designed for beginners, aspiring data analysts, business professionals, and non-technical users who want to work with data using a no-code approach.

The course covers data science fundamentals, data collection from files, databases, and web sources, data cleaning, transformation, feature engineering, and exploratory data analysis using KNIME.

No prior programming experience is required. All concepts and workflows are implemented using KNIME’s visual, drag-and-drop interface.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.