Data collection is a process by which data is gathered and measured. All this must be done before high-quality research can begin and answers to lingering questions can be found. Data collection is usually done with software, and there are many different data collection procedures, strategies, and techniques.
So why is data collection important? It is through data collection that a business or management has the quality information they need to make informed decisions from further analysis, study, and research. Without data collection, companies would stumble around in the dark using outdated methods to make their decisions. Data collection instead allows them to stay on top of trends, provide answers to problems, and analyze new insights to great effect.
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Participants will learn how to identify which data sources likely match the research question, how to turn research questions into measurable pieces, and how to think about an analysis plan. This module also provides a general framework that allows participants not only understand each step required for a successful data collection and analysis, but also help to identify errors associated with different data sources. Participants will learn some metrics to quantify each potential error and can describe the quality of a data source.
The module reviews a range of survey data collection methods that are both interview-based (face-to-face and telephone) and self-administered (paper questionnaires that are mailed and those that are implemented online, i.e., as web surveys). Mixed mode designs are also covered as well as several hybrid modes for collecting sensitive information.
This module presents a detailed overview of qualitative methods of data collection, including observation, interviews, and focus group discussions. We will start with an in-depth overview of each method, explore how to plan for data collection, including developing data collection guides, and discuss techniques for managing data collection.
Good data collection is built on good samples. But the samples can be chosen in many ways. We will examine simple random sampling that can be used for sampling persons or records. This module also covers the steps used in weighting sample surveys, including methods for adjusting for nonresponse. Alternative techniques for imputing values for missing items will be discussed.
In this module participants will learn how data mining can potentially find useful patterns from huge data sets. The insights derived from Data Mining are used for marketing, fraud detection, scientific discovery, etc.
Based on the nature of data, there is a need for business to decide which tool is suitable to handle the data. In this module participants will learn what all tools are available in the market.
Before embarking on the data science journey, organizations need to be equipped with the right skills and be data literate. This course walks through that journey of getting an organization ready.
Data Mining studies algorithms that allow computers to find patterns and regularities in data, perform prediction and forecasting, and generally improve their performance through interaction with data.
Data Analytics, Automation, Python, Computational Thinking are the upcoming technologies that are driving organizations in the right direction.
It's time to upskill for the Industry 4.0