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Data Science News
Medium
20h
255
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Integrating AI in Business Intelligence: Use Cases, Challenges, and Tips
- Large language models enable users to interact with data systems through natural language, making data analysis more accessible.
- AI automates repetitive data preparation tasks and streamlines the data cleaning and transformation process.
- AI enhances data presentation and comprehension through generated graphical representations.
- AI assists in analyzing sentiment of unstructured data sources and provides valuable context to datasets.
- Challenges in deploying AI in BI include user adoption, integration, and collaboration.
- Tips for successful integration of AI in BI include defining objectives, adopting an iterative approach, investing in data governance, starting with pilot projects, and upskilling the workforce.
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Medium
20h
0
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Software Development Landscape ( Data Analysis project )
- This data analysis project investigates the software development landscape, identifies challenges, and discovers innovative solutions.
- The research focuses on various aspects of software development, for instance, collaboration dynamics, methodological choices, agile adoption, website performance, and distributed systems.
- This research presents five research questions with comprehensive datasets to gain insights on contemporary software methodologies.
- The project utilized effective visualization techniques to analyze the data, drawing meaningful conclusions.
- The research results provide valuable insights into various software development areas, significantly contributing to the industry and academia.
- The study examined how developers work together within open-source projects on GitHub to determine how collaboration affects project success.
- The research also focused on why developers choose specific methods for developing software and how different methodologies influence project outcomes and team dynamics.
- This study identified important factors that help organizations effectively adopt and utilize agile methods in contemporary software development.
- The project investigated website performance to optimize web-based software systems and improve user experience.
- The research focused on system components such as cache, database server, load balancer, message queue, and web server, as well as corresponding Web app features such as file upload, payment gateway, real-time chat, search functionality, and user authentication to optimize the performance of distributed web systems.
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Medium
20h
33
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Analyzing Sentiment Analysis Tweets with Plotly: A Step-by-Step Guide
- Sentiment analysis is a powerful tool to understand emotions and opinions in text data.
- The blog post explains how to perform sentiment analysis on a dataset of tweets using Plotly, a Python library for interactive data visualization.
- The dataset used in this analysis contains tweets related to the COVID-19 pandemic, labeled with sentiments ranging from extremely positive to extremely negative.
- By storing the tweet data in a PostgreSQL database and utilizing Plotly for visualization, valuable insights can be gained into the sentiment of COVID-19 tweets.
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Medium
21h
286
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One Step Above PCA: What is t-SNE?
- Principal Component Analysis (PCA) is a tool for reducing dimensionality in data science.
- t-SNE (t-Distributed Stochastic Neighbor Embedding) is an alternative to PCA that preserves local structure and captures complex, non-linear relationships in data.
- t-SNE calculates similarities between data points and adjusts their positions in a lower-dimensional space iteratively to align with the original high-dimensional dataset.
- This non-linear dimensionality reduction technique is useful for data analysis and retaining intrinsic relationships within the data.
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Medium
22h
305
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WHERE NOT EXISTS: What Happens?
- NOT EXISTS SQL Operator is used to filter out rows from table_1 that already exist in table_2.
- The subquery selects the constant value 1 to indicate the existence of a row in the subquery.
- The condition checks for matching id values between table_1 and table_2.
- The NOT EXISTS operator returns FALSE if the subquery returns any records.
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Towards Data Science
22h
179
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PCA & K-Means for Traffic Data in Python
- Principal Component Analysis (PCA) can be used in traffic data to detect anomalies or to capture the patterns of a transit station's traffic history.
- PCA can be applied to reduce dimensionality and can be used for machine learning tasks including clustering, classification, and regression.
- The Taipei Metro Rapid Transit System, Hourly Traffic Data was used to keep only weekday data, with most interesting patterns during weekdays.
- PCA helps to identify when traffic trends of different stations are most representative, e.g. commute hours to cluster stations.
- PCA output matrices include Z and W, where the latter can be thought of as weights on each feature or hour, and the former as the representations of stations.
- The 3 principal components generated with PCA resulted in PC_1 weighting more on night hours, PC_2 weighting more at noon, and PC_3 about morning time.
- Stations are clustered based on passenger distributions among the 3 periods, with K-Means being used in this article.
- Taipei Main Station is a huge transit hub, with a high-traffic pattern during morning and evening periods, while Taipei Zoo station has fewer people in either period due to few residents living in its area.
- Fine-tuning hyper-parameters of K-Means can help in better grouping of stations.
- The article presents examples of how PCA can be used for machine learning analysis, specifically for clustering transit stations depending on traffic patterns in different periods.
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Medium
22h
18
Research on inner workings of Variational Bayes method part4(Statistical Machine Learning)
- This paper presents a method for approximating the inverse of the Fisher information matrix in variational Bayes inference.
- The approach avoids computing the Fisher information matrix analytically and its explicit inversion.
- Instead, an iterative procedure generates a sequence of matrices that converge to the inverse of Fisher information.
- The proposed algorithm achieves a convergence rate of O(log s/s) and exhibits versatility across various variational Bayes domains.
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Medium
22h
7
Research on inner workings of Variational Bayes method part3(Statistical Machine Learning)
- Vector quantization (VQ) is a technique used to learn features with discrete codebook representations.
- Existing hierarchical extensions of VQ-VAE suffer from the codebook/layer collapse issue, leading to degraded reconstruction accuracy.
- To address this problem, a novel framework called HQ-VAE (hierarchically quantized variational autoencoder) is proposed.
- HQ-VAE improves codebook usage and enhances reconstruction performance in image datasets and audio datasets.
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Medium
23h
160
Updates on Federated Learning part4(AI 2024)
- Federated learning (FL) is susceptible to backdoor attacks.
- Existing academic studies rely on a high proportion of real clients, which is impractical in real-world industrial scenarios.
- DarkFed presents a practical FL backdoor attack by emulating fake clients and using a shadow dataset.
- Covert backdoor updates are strategically constructed to evade detection by defenses.
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Medium
23h
86
Updates on Federated Learning part3(AI 2024)
- Ensuring driver readiness poses challenges, and driver monitoring systems can help determine the driver's state.
- A federated learning framework is proposed for drowsiness detection in a vehicular network.
- The framework leverages the YawDD dataset and achieves an accuracy of 99.2%.
- The model's scalability is demonstrated using different numbers of federated clients.
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Medium
23h
175
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The Power of Proximity(KNN Algorithm
- The kNN algorithm can be compared to someone looking for the best neighborhood based on their preferences of neighborhood features just like similar features are compared in data points in kNN.
- Each neighborhood is compared based on the features it has, which could be the average rent cost, community vibe or even distance to local schools.
- To find the best neighborhood, based on the individual's preferences, the distance between their preferences and a neighborhood's features is calculated.
- The Majority Voting process in the k-Nearest Neighbors (kNN) algorithm is important for making predictions, especially in classification tasks.
- The kNN algorithm can be computationally intensive in its basic form, so several variants and extensions have been developed to deal with large datasets and high-dimensional feature spaces.
- kNN is used in recommendation systems to suggest products to customers that are similar to what they have liked before. It is also used in medical fields for predicting diseases in patients, credit rating prediction and image recognition tasks.
- kNN works well in complex and subtle data patterns analysis such as medical diagnosis, where making predictions is based on a clear and understandable reasoning process.
- In recommendation systems, the algorithm can quickly identify the most similar items or users and make recommendations accordingly.
- In image recognition tasks, the algorithm can classify images by comparing pixel values, taking advantage of its ability to handle multi-class cases and work well with little pre-processing of image data.
- The kNN algorithm has played a significant role in the evolution of AI and has been useful in finding the nearest neighbors that match specific criteria, making it an excellent tool for data analysis and prediction.
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Medium
23h
142
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From Words to Wisdom
- Text data is unstructured and requires specialized techniques in data science to extract useful information.
- Text preprocessing is a critical step in making raw text data ready for analysis.
- Stemming algorithms and lemmatization are two methods of text preprocessing, with lemmatization being more precise.
- The Bag of Words (BoW) model and Term Frequency-Inverse Document Frequency (TF-IDF) are foundational techniques used in text analysis and natural language processing.
- BoW treats text as a mere collection of words, ignoring the grammar and the order in which words appear.
- TF-IDF is a statistical measure used to evaluate how important a word is to a document in a collection or corpus.
- BoW and TF-IDF have limitations in handling synonyms and polysemy.
- In handling large amounts of text data, BoW can result in high-dimensional and sparse data.
- Without additional processing like stop-word removal or term weighting, BoW models may be biased towards frequent, less informative words.
- Service industry companies like hotels or airlines collect vast amounts of text data to enhance customer satisfaction, improve services, and tailor marketing strategies.
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Medium
23h
153
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The Art of Data Transformation
- Data transformation is essential in data science to make raw data useful for analysis, enhancing data quality, and facilitating integration from multiple sources. They enhance the performance and accuracy of statistical models and algorithms, facilitate meaningful data comparison, and ensure consistency across different data sets. Techniques like tokenization, stemming, and lemmatization are used to reduce the number of unique words the model has to handle, thereby focusing on the essence rather than the form of the word in text data transformations. In numerical data processing, transformations can reduce the effects of skewness and outliers leading to improvements in model accuracy and robustness. Transforming categorical data into numerical formats allows machine learning models to process and learn from the data, and proper encoding of categorical variables impacts the model's performance.
- Transformations can be used to enhance or isolate certain features within an image that are important for a specific analysis. Before feeding images into a model, it is often necessary to preprocess them to make them suitable for analysis. Techniques like Bag of Words, Term Frequency-Inverse Document Frequency, and word embeddings not only convert text into numerical values but also help in reducing the dimensionality so that the model can be trained using less computational power. Normalizing an image’s intensity values can reduce the effect of lighting variations and improve the consistency of input data, which is particularly important for achieving high performance in many image processing and machine learning applications.
- Categorical data transformations are important in machine learning because many models and algorithms cannot handle categorical data directly. These algorithms require numerical inputs, making it necessary to transform categorical variables into numerical formats. One-Hot Encoding creates a new binary column for each category of the variable, while Label Encoding assigns an integer to categorical data based on an explicit ordering. Replacing categories with their frequencies and values derived from the average value of the target variable for that category are useful when the frequency of categories is an essential characteristic for the model.
- Text data transformations, such as converting text into numerical formats like vectors, allow algorithms to perform statistical analysis, find patterns, and make predictions. Transformations such as lowercasing all letters, removing punctuation and standardizing terms ensure consistency across the dataset, which reduces complexity and improves the model’s performance.
- Transforming data to be more normally distributed or linearizing relationships between variables, can improve the effectiveness and predictiveness of statistical methods and machine learning algorithms. Many algorithms perform better when numerical input variables are on a similar scale, and transformations can be used to scale them. Translations can also be used to reduce the effects of skewness and outliers, leading to improvements in model accuracy and robustness in numerical data processing.
- Data augmentation using image transformations is essential for good performance while training deep learning models and for proper model training. Techniques like shifts, flips, rotations, and color changes increase the diversity of the dataset. Transformations can be utilized to enhance or isolate specific features within an image that are essential for a particular analysis. Random brightness and contrast adjustments, color separations, scaling pixel values, feature scaling, selective color channel usage, and the addition of random noise are examples of image transformations.
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Medium
23h
168
Updates on Federated Learning part2(AI 2024)
- Federated learning (FL) is a privacy-preserving machine learning approach.
- Recently, gradient inversion attacks have been recognized as a privacy risk in FL.
- A novel Gradient Inversion attack based on the Style Migration Network (GI-SMN) is proposed.
- GI-SMN outperforms state-of-the-art gradient inversion attacks and can overcome certain defenses.
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Medium
23h
220
Updates on Federated Learning part1(AI 2024)
- Deep learning has shown incredible potential across various tasks, but accessing data stored on personal devices poses privacy challenges.
- Federated learning (FL) has emerged as a privacy-preserving technology that enables collaborative training of machine learning models without sending raw data to a central server.
- This survey paper provides a literature review of privacy attacks and defense methods in FL, identifies limitations, and discusses successful industry applications.
- The paper also explores the efficacy of a hybrid federated-continual learning paradigm for robust web phishing detection, achieving high accuracy and outperforming traditional approaches.
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