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AI data analysis employs AI techniques and data science to enhance the processes of cleaning, inspecting, and
modelling over both structured and unstructured data. The main goal is to extract valuable information that can aid
in decision-making and drawing conclusions.
Data Collection
Data collection lets you record past events, so you can use data analysis
to find patterns. From these patterns, you can build predictive models
using machine learning to spot trends and predict future changes.
Data collection means gathering data from many sources, both offline
and online. Collecting large amount of data can be the hardest part of a
machine learning project, especially on a large scale. The amount of data
you need depends on the number of features in the data set. It’s best to
collect as much data as possible for accurate predictions. You can start
with small batches of data and see how the model performs. It’s important DATA
to collect diverse data to ensure your model covers various scenarios.
The quantity of data also depends on, how complex your model is. For
simple tasks like, license plate detection, small batches of data might be
enough. But for more complex tasks like, medical AI, you will need a lot of data. Before collecting data, data scientists
need to understand the problem, the best solution, and the data requirements. Based on these requirements, they
identify the data sources and collect the data. Data is essential for any project, and it is needed throughout the
project’s development. Therefore, identifying data needs, collecting data, and analysing it repeatedly.
Data is the main requirement for data collection methods. In any type of research or company operations, collecting
data serves primarily to support the identification of important variables, performance being the most important
among them. Thus, the process of collecting data plays a vital role almost in every field. Depending on the kind of
data being collected, there are two main categories of data collection methods: primary and secondary.
Primary Data Source
A primary data source refers to the original source from which data is collected firsthand. This data is obtained
directly from its origin, without any intermediary sources or interpretations. Primary data sources include surveys,
interviews, observations, experiments, and any other method where data is collected directly by the researcher or
organisation for a specific purpose. This type of data is considered valuable because it is tailored as per the specific
research or business needs and is often more accurate and relevant than secondary data, which is obtained from
sources which have already interpreted or analysed the original data.
Primary Data Sources
Marketing
Survey Interview Observation Experiment Questionnaire
Campaign
Let us study each source.
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