Data Collection
Techniques

Seminar Overview

The analysis results are only as good as the data we collect. Therefore, using the adequate techniques for collecting the data is the prerequisite for getting the accurate results and predictions. 

SAA & CONSULTING training course will help people involved in Data Science and Analytics learn skills and techniques that enable them to adequately collect the data in today’s world which has the abundance of data and also provide them with the tools that will enable them to avoid the mistakes in collecting the data therefore being able to build their analysis on strong foundations.

Seminar Objective

Specifically you will learn to:

• Create a Data Collection Plan
• Determine Adequate Sample Size for the Research
• Identify and Avoid Biases and Common Errors that can be Present in the Data Collected
• Apply Big Data Concepts for Data Mining
• Identify the difference between Primary and Secondary Data
• Use Methods and available Software to Collect the Data in Real Time
  

Designed for

All individuals working in the field of Data Science and Data Analytics, which today means basically every professional:

• All Leaders and Professionals
• Management Professionals
• Project Managers
• Financial Managers
• Data Scientists and Analysts
• Plant Managers
• Production Planners
• And Everyone else who wants to learn how to gather high quality data

Seminar Outline

• Identify the Sources of Data and the Most Adequate Collection Methods
• Learn the Data Lifecycle Stages and the Way to Perform Data Mining
• Recognise the Data Sources of Structured, Semi Structured and Unstructured Data
• Learn How to Determine Adequate Sample Sizes for the Data Collection
• Prepare to Conduct Surveys and Identify the Response Bias in the Survey Results
• Learn the Use of Available Software for Data Collection
• Maintain the Data Quality (completeness, uniqueness, timeliness, validity, accuracy, consistency)
• Store and Manage the Data
• Learn the Modern Algorithms for Unstructured Data Collection