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Data Collection Frameworks 2026
Rating: 4.5 out of 5(157 ratings)
1,380 students

Data Collection Frameworks 2026

Learn all about the Data Collection Frameworks and become a master Data professional
Created byKultureHire Edtech, Kadhiravan Jayachandiran
Last updated 1/2026
English
English [Auto],

What you'll learn

  • Understanding Data Collection Principles
  • Exploring Data Collection Methods and Tools
  • Ensuring Data Quality and Integrity
  • Designing Effective Data Collection Strategies

Course content

3 sections • 11 lectures • 42m total length

  • Introduction3:51

    Learn how data collection gathers and measures information on variables of interest, illustrated by a pizza shop example and methods like surveys, interviews, and observations to drive business impact.

  • Where and why we need data collection1:24

    Explore where data collection is used across industries and why it matters for resource allocation, decision making, policy formulation, and predictive analytics.

Requirements

  • No Prerequisite as such

Description

This comprehensive course is designed for professionals seeking advanced knowledge and practical skills in leveraging cutting-edge data analytics and infrastructure frameworks. The course covers a range of topics, from web and mobile analytics to real-time data streaming and observability, providing participants with a solid foundation for designing and implementing robust data solutions.


Course Structure:

  1. Module 1: Foundations of Data Analytics

    • Overview of CRISP-DM and TDSP frameworks

    • Understanding the data lifecycle and key data processing stages

    • Practical applications and case studies

  2. Module 2: Web and Mobile Analytics

    • In-depth exploration of Google Analytics and Adobe Analytics

    • Hands-on exercises for user behavior tracking and conversion analysis

    • Implementing analytics strategies for web and mobile applications

  3. Module 3: User Engagement Analytics

    • Utilizing Mixpanel and Amplitude for user engagement analysis

    • A/B testing and cohort analysis techniques

    • Developing data-driven strategies for user retention

  4. Module 4: Centralized Logging and Monitoring

    • Implementation of ELK Stack for centralized logging

    • Real-time log analysis using Splunk

    • Building custom dashboards for effective monitoring

  5. Module 5: Real-Time Data Streaming Frameworks

    • Apache Kafka and its role in building data pipelines

    • Real-world applications of Apache Flink in stream processing

    • Designing scalable and fault-tolerant streaming architectures

  6. Module 6: Cloud-Based Data Collection

    • AWS Kinesis and Google Cloud Pub/Sub for cloud-based data streaming

    • Scalability considerations in cloud-based data solutions

    • Integration with other cloud services for end-to-end data processing

  7. Module 7: Observability Frameworks

    • Introduction to Prometheus for monitoring and alerting

    • Creating interactive dashboards with Grafana

    • Best practices for achieving comprehensive system observability

Who this course is for:

  • Data Analysts looking to learn how to effectively collect and manage data for analysis purposes.
  • Business Managers looking to understand how data collection impacts decision-making and organizational success
  • IT Professionals who are looking to learn about integrating and optimizing data collection within existing IT infrastructure.
  • Market Researchers who want to explore techniques for collecting relevant and actionable market data
  • Entrepreneurs who wants to acquire skills to gather and leverage data for business growth and innovation
  • Students who want to build a strong understanding of data collection frameworks as a basis for future roles or academic pursuits

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