Understanding the concepts and core technologies of Data Science
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콘텐주
Data Science is an academic discipline that collects, processes, and analyzes data to derive meaningful information and insights. It is used to discover hidden patterns and relationships from massive amounts of data and predict the future. It can be said to be an interdisciplinary field of study that combines various fields such as statistics, computer science, machine learning, and domain knowledge.
The goal of data science is to provide information to decision makers so that they can make data-driven decisions. To do this, data scientists process and analyze vast amounts of structured and unstructured data using various technologies and tools. Advanced analysis techniques such as data mining, machine learning, natural language processing, and text mining are used.
In order to extract useful information from data, you need to understand the characteristics of the data well. Therefore, exploratory data analysis (EDA) that identifies the distribution, variability, and outliers of the data must be conducted first. Various visualization techniques such as histograms, box plots, and scatter plots are utilized.
Before analyzing data, a data preprocessing process is required. This is the process of processing data into a form suitable for analysis by handling missing values, removing outliers, transforming variables, and creating derived variables. The preprocessing process requires a lot of domain knowledge. This is because you need to determine which variables are important and how to transform them.
Statistics and machine learning are mainly used in data analysis. Depending on the type of data, various techniques such as regression analysis, classification analysis, cluster analysis, and association analysis are applied. Recently, advanced machine learning techniques such as deep learning are also widely used.
Regression analysis is a method to identify linear relationships between independent and dependent variables. There are simple regression and multiple regression. Classification analysis is a method to predict categorical dependent variables, and representative examples include logistic regression, decision trees, and SVM. Cluster analysis is a technique to group objects with similar characteristics, and association analysis is an analysis that finds simultaneous purchase patterns between products in transaction data.
To handle text data, natural language processing technology is required. There are methods such as Bag-of-Words and TF-IDF that quantify documents based on the frequency of word appearance, and Word2Vec and GloVe that convert words into embedding vectors. Recently, powerful deep learning language models such as BERT and GPT-3 have been in the spotlight.
Computer vision technology is used for image/video data analysis. It is used in various fields such as image classification, object detection, face recognition, and autonomous driving. Deep learning algorithms such as CNN and R-CNN are showing excellent performance. Image generation using GAN is also receiving much attention.
In data science, big data technology that handles large amounts of data is very important. Distributed processing using Hadoop and Spark is widely used, and recently, cloud-based big data platforms are also in the spotlight. Various technologies such as NoSQL, stream processing, and real-time analysis are being utilized.
Python and R are representative programming languages used for data analysis. Python, which provides a vast library including Pandas, NumPy, and Matplotlib, is the most popular language in the field of data science. R, which specializes in statistics and visualization, is also widely used.
Data scientists need to have not only programming skills, but also domain knowledge and communication skills. They need to be able to interpret data analysis results from a business perspective and clearly communicate them to decision makers. Another important skill is to be able to express complex information in an easily understandable way through visualization.
Data science has become a key field that drives business innovation in companies. Data science is being used in various fields such as market prediction, customer segmentation, personalized marketing, anomaly detection, and recommendation systems. Data science has enabled us to capture new business opportunities and improve operational efficiency.
Data science has now become a must-have capability in every industry. Data-driven decision-making is essential for companies to gain a competitive edge. Data scientists will need to evolve into talents with business insight and problem-solving skills, not just statistical or coding skills.
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