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

本课程是 No-Code Data Science and Machine Learning 专项课程的一部分

位教师：Edureka

包含在中

了解更多

5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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.

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 No-Code Data Science and Machine Learning 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有5个模块

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.

涵盖的内容

8个视频4篇阅读材料3个作业

8个视频总计35分钟

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

4篇阅读材料总计40分钟

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

3个作业总计27分钟

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

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.

涵盖的内容

10个视频3篇阅读材料3个作业

10个视频总计46分钟

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

3篇阅读材料总计30分钟

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

3个作业总计27分钟

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

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.

涵盖的内容

12个视频5篇阅读材料4个作业

12个视频总计56分钟

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

5篇阅读材料总计50分钟

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

4个作业总计33分钟

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

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.

涵盖的内容

8个视频3篇阅读材料3个作业

8个视频总计33分钟

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

3篇阅读材料总计30分钟

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

3个作业总计27分钟

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

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.

涵盖的内容

1个视频1篇阅读材料2个作业1个讨论话题

1个视频总计3分钟

Course Summary 3分钟

1篇阅读材料总计30分钟

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

2个作业总计60分钟

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

1个讨论话题总计5分钟

Describe Your Learning Journey 5分钟

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将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Edureka

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Edureka

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常见问题

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.