Description

Introduction of Time Series Analysis in KNIME Analytics Platform:
The L4-TS: Time Series Analysis in KNIME Analytics Platform certification is designed for professionals who want to develop expertise in analyzing and forecasting time-dependent data. This course covers essential techniques such as data preparation, trend analysis, seasonal decomposition, and predictive modeling, all within the KNIME environment. Participants will learn to build robust time series workflows, enabling them to derive actionable insights and make informed decisions based on historical data patterns.

Prerequisites:

1. Basic Knowledge of KNIME:
   Familiarity with the KNIME Analytics Platform interface and the ability to create simple workflows.
2. Understanding of Data Analytics Concepts:
   A foundational understanding of data analytics principles, including data manipulation and visualization techniques.
3. Statistical Knowledge:
   Basic knowledge of statistical concepts relevant to time series analysis, such as mean, variance, and correlation.
4. Experience with Time Series Data:
   Familiarity with time series data structures and concepts, including trends, seasonality, and cyclic behavior.
5. Analytical Skills:
   Strong analytical thinking and problem-solving skills to interpret and manipulate data effectively.

Cost: $150

Prerequisites: L1, L2, & L3 Certification
Recommended Preparation Courses: L4-TS
65 minute multiple-choice exam: 45 questions

Topics Covered:

• General Time Series Analysis Concepts
• Introduction to Time Series Analysis and KNIME Components
• Naive Method, ARIMA models, Residual Analysis
• Machine Learning, Model Optimization, Deployment

Conclusion:
This certification empowers professionals to effectively analyze and forecast time series data, enhancing their ability to interpret trends and patterns. By mastering KNIME’s time series capabilities, candidates will be better positioned to drive strategic decision-making within their organizations. Achieving this certification demonstrates a commitment to leveraging advanced analytical skills in time series analysis.

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