“Pegah Badaghi”, Snap’s data analyst, discussed the developments and challenges of data analysis in the field of artificial intelligence with an interesting speech at the second Pandora startup gathering. Explaining the role of data preprocessing and different algorithms, he pointed out the importance of cleaning data before using it in models and emphasized that 80% of data analysis time should be spent on data preparation.
Entering the world of data analysis with artificial intelligence
At the beginning of the speech, he pointed out the important differences between artificial intelligence and machine learning and said: “These two concepts are often confused, while artificial intelligence is a broader set of machine learning and many of its applications require machine-driven data. They don’t have.”
With examples of quality control systems in factories and temperature regulation sensors, he explained that many tools and technologies of artificial intelligence are not only dependent on computer data, but also take advantage of other processes.
Badaghi went on to discuss the field of natural language processing (NLP) and said: “Natural language processing is one of the fields that has made significant progress in the past decades. “In the beginning, there were simple grammars for analyzing and processing language, but with the introduction of data and more sophisticated algorithms, we have been able to achieve a level of accuracy in language recognition that previously seemed impossible.”
He emphasized: “At the initial stages of data analysis, even before entering into modeling, great care should be taken in cleaning and preparing the data.”
data preprocessing; The key to the success of models
In another part of the speech, Pegah Badaghi pointed out the importance of data pre-processing and said: “One of the basic parts of any data analysis project is data pre-processing. This step includes removing inappropriate data, correcting erroneous data, and converting the data into understandable formats for the models.”
He explained that if the initial data is not properly prepared, the output of the models will not be reliable. “If the data is not clean, the models will reach the wrong outputs and this can affect the whole project,” said Badaghi.
Choosing the right algorithm; A vital part of data analysis
Badaghi went on to discuss the selection of different algorithms and said: “Choosing the right algorithm for each data analysis project depends on the type of data and the problem in question. From simple algorithms such as decision trees to complex neural networks, each of these methods has its advantages and limitations.
He explained well-known algorithms such as neural networks and decision trees and emphasized: “For any model, understanding the theory behind the algorithms can help the data analyst choose the best method.”
Badaghi pointed out one of the big challenges of data analysis, which is the bias of models, and said: “Sometimes models are biased due to incorrect input data or lack of diversity in the data. This can lead to inaccurate and unreliable outputs.”
He explained that data analysts should always be aware of this issue and carefully review the data to ensure the accuracy of the results.
Data analysis with artificial intelligence has a bright future
At the end of his speech, Pegah Badaghi once again emphasized the importance of data preparation and pre-processing and said: “Data analysis depends on the quality of the data more than anything else. A data analyst needs to know how to clean and prepare the data to get the best possible results from the models.”
He concluded: “Data analysis with artificial intelligence has a bright future, but to achieve success in this field, one must always be careful and spend enough time on data preparation.”
In general, Pegah Badaghi’s speech at the second Pandora startup gathering clearly showed that data analysis and artificial intelligence are changing and evolving in different aspects. He carefully examined the stages of data preprocessing, choosing algorithms and common challenges in this field, and by presenting practical examples, he was able to clarify the importance of these issues for the audience.
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