Confident KNIME work - You will learn how to install, configure, and navigate KNIME efficiently, so you can start analytics projects faster and avoid common setup and usability issues from the very beginning.
End-to-end workflow design - You will build, organize, import, and export KNIME workflows with confidence, allowing you to create clear data pipelines that are easier to scale, document, and share with your team.
Reliable multi-source data import - You will learn to load data from XML, CSV, and TXT files as well as relational databases and SQL queries, so you can combine scattered sources into one consistent analytical process.
Data preparation for analysis - You will practice cleaning, standardizing, and transforming data, working with columns and rows, and checking data quality, so your datasets are ready for trustworthy analysis and modeling.
Automation with loops and variables - You will use variables, loops, and automation features in practical scenarios, helping you reduce repetitive manual work and build processes that can run repeatedly on incoming data.
Clear visualizations and reporting - You will learn how to present analytical results in a clear way and prepare recurring reports, making it easier for you to communicate findings to both business stakeholders and technical teams.
Hands-on data mining techniques - You will explore key data mining tasks and methods, including text mining, web mining, social network analysis, and map-based data work, expanding the range of analyses you can perform.
KNIME with Python, R, and Java - You will learn how to connect KNIME with Python, R, and Java, so you can extend workflows with custom code, use extra libraries, and create more flexible analytical solutions.
Before working with workflows, it is necessary to understand how data processing and analysis fit into a broader analytical context. This opening section frames the purpose of transforming raw data into usable information and prepares participants for the methods and tools developed later.
KNIME setup starts with installation, configuration, and orientation in the interface used to build analytical workflows. Participants also review the main functions of the platform, establishing a practical foundation for navigating the environment and using its basic capabilities correctly.
The platform becomes more effective when its tools operate as parts of one connected environment rather than isolated features. This section examines how components are integrated within KNIME so that data access, transformation, analysis, and downstream tasks can work together in one process.
Creating a workflow means defining how processing steps are arranged, executed, and managed within KNIME. The section covers workflow structure and operation, then extends to import and export mechanisms that support portability of developed solutions between environments or teams.
From an organizational perspective, data processing must be described and transferred in a controlled way. This section addresses documentation of business models and processing procedures, together with methods for importing and exporting processes so they can be reused, reviewed, and maintained consistently.
Core KNIME nodes form the operational basis for building workflows and controlling data movement between steps. Participants work with the logic behind these fundamental components, learning how basic nodes are used to read, manipulate, connect, and pass data through a process.
ETL processes connect data acquisition, transformation, and delivery into a single operational chain. The section explains how these stages relate to one another in KNIME and how structured ETL design supports reliable movement of data from source systems to analytical or reporting outputs.
XML, CSV, and TXT files often require different handling despite serving as common entry points for analysis. This part focuses on loading raw data from text-based sources, paying attention to file structure, parsing issues, and the first validation steps needed before transformation starts.
At the methodological level, data exploration is treated as a structured way to understand what the dataset contains before deeper analysis begins. The section organizes exploratory work around analytical reasoning, helping distinguish initial inspection, hypothesis formation, and evidence-based interpretation.
Importing data involves more than opening files, especially when information must be retrieved from relational databases or filtered with SQL. This section works through source connectivity, query-based extraction, and the practical considerations of bringing structured data into KNIME for further processing.
Data analysis in KNIME covers the path from understanding data types to preparing datasets for reliable modeling. The section includes standardization, column and row operations, transformations, statistical examination, quality checks, and the foundations needed to structure data for analytical use.
Using variables and loops makes workflows adaptable to changing parameters, multiple inputs, and repetitive operations. Participants examine how dynamic control is introduced into KNIME processes, enabling iteration, parameter-driven execution, and more flexible handling of analytical scenarios.
Building automated processes requires combining separate workflow elements into a coherent execution structure. Here the focus is on how automation is constructed in practice, with attention to sequencing, reuse of components, and organizing process logic so that repeated runs remain controlled and reliable.
Results gain value only when they can be interpreted correctly and communicated in a form suited to the audience. This section concentrates on presenting analytical outputs visually, choosing suitable forms of display, and linking charts or summaries to the underlying findings from KNIME workflows.
Data mining is presented through the main task types, the sequence of activities in a mining process, and the role of exploratory work before modeling. The section also frames key techniques used to detect patterns, segment data, and support analytical decision-making.
Routine data handling can be reduced to repeatable workflows that run with limited manual intervention. The section shows how KNIME supports automated execution of preparation and transformation steps, helping maintain consistency, traceability, and efficient processing across recurring data tasks.
When data comes from texts, websites, networks, or geospatial sources, discovery methods must be adapted to the structure of the material. This part connects Web Mining, SNA, Text Mining, and map-based data analysis as complementary ways of extracting knowledge from complex datasets.
Forecasting work begins with the core principles used to build and assess predictions from historical data. Special attention is given to identifying trends and using them correctly in forecast construction, so projected values follow observable patterns rather than ad hoc assumptions.
Java, R, and Python extend KNIME when standard nodes are not enough for a specific analytical or integration task. Participants work with the role of external scripting tools in workflows and see how code-based components connect with the platform’s processing logic.
Recurring reporting focuses on turning analytical workflows into repeatable outputs for regular use. The section addresses how to structure report generation, keep results consistent across cycles, and align prepared outputs with the data processing steps built earlier in KNIME.
Basic data literacy - You should be comfortable with concepts such as rows, columns, data types, and tables, so you can efficiently follow exercises focused on data preparation, transformation, and analysis.
Computer and file handling - You should be able to work confidently in an operating system, install applications, and manage files and folders, as the training includes setup tasks and data import activities.
SQL and database basics - You should understand the basics of relational databases and simple SQL queries, so you can follow examples related to data extraction, source integration, and database access.
Analytical thinking - You should be able to analyze problems logically and interpret results, because during the training you will prepare data, assess quality, and draw practical conclusions from outputs.
