Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions.

Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward’s algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comprised elderly patients that participated in the PolSenior project. Each patient was characterized by over 40 biochemical and socio-geographical attributes. Introductory analysis showed that the case-study dataset comprises two clusters separated along the axis of sex hormone attributes. Further analysis was carried out separately for male and female patients. The most optimal partitioning in the male set resulted in five subgroups. Two of them were related to diseased patients: 1) diabetes and 2) hypogonadism patients. Analysis of the female set suggested that it was more homogeneous than the male dataset. No evidence of pathological patient subgroups was found. In the study we showed that outlier detection with MD and rMD allows not only to identify outliers, but can also assess the heterogeneity of a dataset. The case study proved that our procedure is well suited for identification and visualization of biologically meaningful patient subgroups.

One more step towards Machine learning! This is a titatic dataset with exploratory data analysis html file. I used pandas-profiling for fast analysis.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Dataset

This dataset was created by Prabhakar

Released under Other (specified in description)

Contents

Sonification for Exploratory Data Analysis

Chapter 8: Sonification Models

In Chapter 8 of the thesis, 6 sonification models are presented to give some examples for the framework of Model-Based Sonification, developed in Chapter 7. Sonification models determine the rendering of the sonification and possible interactions. The "model in mind" helps the user to interprete the sound with respect to the data.

8.1 Data Sonograms

Data Sonograms use spherical expanding shock waves to excite linear oscillators which are represented by point masses in model space.

• Table 8.2, page 87: Sound examples for Data Sonograms

8.2 Particle Trajectory Sonification Model

This sonification model explores features of a data distribution by computing the trajectories of test particles which are injected into model space and move according to Newton's laws of motion in a potential given by the dataset.

• Sound example: page 93, PTSM-Ex-1 Audification of 1 particle in the potential of phi(x).
• Sound example: page 93, PTSM-Ex-2 Audification of a sequence of 15 particles in the potential of a dataset with 2 clusters.
• Sound example: page 94, PTSM-Ex-3 Audification of 25 particles simultaneous in a potential of a dataset with 2 clusters.
• Sound example: page 94, PTSM-Ex-4 Audification of 25 particles simultaneous in a potential of a dataset with 1 cluster.
• Sound example: page 95, PTSM-Ex-5 sigma-step sequence for a mixture of three Gaussian clusters
• Sound example: page 95, PTSM-Ex-6 sigma-step sequence for a Gaussian cluster
• Sound example: page 96, PTSM-Iris-1 Sonification for the Iris Dataset with 20 particles per step.
• Sound example: page 96, PTSM-Iris-2 Sonification for the Iris Dataset with 3 particles per step.
• Sound example: page 96, PTSM-Tetra-1 Sonification for a 4d tetrahedron clusters dataset.

8.3 Markov chain Monte Carlo Sonification

The McMC Sonification Model defines a exploratory process in the domain of a given density p such that the acoustic representation summarizes features of p, particularly concerning the modes of p by sound.

• Sound Example: page 105, MCMC-Ex-1 McMC Sonification, stabilization of amplitudes.
• Sound Example: page 106, MCMC-Ex-2 Trajectory Audification for 100 McMC steps in 3 cluster dataset
• McMC Sonification for Cluster Analysis, dataset with three clusters, page 107
  • Stream 1 MCMC-Ex-3.1
  • Stream 2 MCMC-Ex-3.2
  • Stream 3 MCMC-Ex-3.3
  • Mix MCMC-Ex-3.4
• McMC Sonification for Cluster

Exploratory Data Analysis (EDA) of Theatres Data in India

Steps
  1. Load Data
  2. Check Nulls and Update Data if required
  3. Perform Descriptive Statistics
  4. Data Visualization
     Univariate - Single Column Visualization
       categorical - countplot
       continuous - histogram
     Bivariate - 2 Columns Visualization
      continuous vs continuous  - scatterplot, regplot
      categorical vs continuous  - boxplot
      categorical vs categorical - crosstab, heatmap
     Multivariate - Multi Columns Visualization
      correlation plot
      pairplot

Additional file 1. Zip file containing the interactive supplement.

Understand the satellite dataset before training. Explore distributions, preprocessing steps, and key insights to evaluate 3rd-party models effectively.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Recent calls to take up data science either revolve around the superior predictive performance associated with machine learning or the potential of data science techniques for exploratory data analysis. Many believe that these strengths come at the cost of explanatory insights, which form the basis for theorization. In this paper, we show that this trade-off is false. When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. We present a systematic five-step theory-building and theory-testing cycle that consists of: 1. Element identification (reduction); 2. Exploratory analysis (induction); 3. Hypothesis development (retroduction); 4. Hypothesis testing (deduction); and 5. Theorization (abduction). We demonstrate the usefulness of this approach, which we refer to as co-duction, in a vignette where we study firm growth with real-world observational data.

Daily Machine Learning Practice – 1 Commit per Day

Author: Astrid Villalobos Location: Montréal, QC LinkedIn: https://www.linkedin.com/in/astridcvr/

Objective The goal of this project is to strengthen Machine Learning and data analysis skills through small, consistent daily contributions. Each commit focuses on a specific aspect of data processing, feature engineering, or modeling using Python, Pandas, and Scikit-learn.

Dataset Source: Kaggle – Sample Sales Data File: data/sales_data_sample.csv Variables: ORDERNUMBER, QUANTITYORDERED, PRICEEACH, SALES, COUNTRY, etc. Goal: Analyze e-commerce performance, predict sales trends, segment customers, and forecast demand.

**Project Rules **Rule Description 🟩 1 Commit per Day Minimum one line of code daily to ensure consistency and discipline 🌍 Bilingual Comments Code and documentation in English and French 📈 Visible Progress Daily green squares = daily learning 🧰 Tech Stack

Languages: Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn Tools: Jupyter Notebook, GitHub, Kaggle

Learning Outcomes By the end of this challenge: Develop a stronger understanding of data preprocessing, modeling, and evaluation. Build consistent coding habits through daily practice. Apply ML techniques to real-world sales data scenarios.

Data description: This dataset consists of spectroscopic data files and associated R-scripts for exploratory data analysis. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra were collected from 67 samples of polymer filaments potentially used to produce illicit 3D-printed items. Principal component analysis (PCA) was used to determine if any individual filaments gave distinctive spectral signatures, potentially allowing traceability of 3D-printed items for forensic purposes. The project also investigated potential chemical variations induced by the filament manufacturing or 3D-printing process. Data was collected and analysed by Michael Adamos at Curtin University (Perth, Western Australia), under the supervision of Dr Georgina Sauzier and Prof. Simon Lewis and with specialist input from Dr Kari Pitts.

Data collection time details: 2024
Number of files/types: 3 .R files, 702 .JDX files
Geographic information (if relevant): Australia
Keywords: 3D printing, polymers, infrared spectroscopy, forensic science

Wetlands Ecological Integrity Depth to Water Logger data from 2009-2019 at Great Sand Dunes National Park. This includes Raw dataset (primarily hourly), daily summaries, weekly summaries, and monthly summaries. Included in the data package are exploratory data analysis figures at the daily, weekly and monthly time steps. Lastly included is the R code used to extract the depth to water logger data from the National Park Service Aquarius data system, and to create the exploratory data analysis figures.

Wetlands Ecological Integrity Depth to Water Logger data from 2009-2019 at Florissant Fossil Beds National Monument. This includes Raw dataset (primarily hourly), daily summaries, weekly summaries, and monthly summaries. Included in the data package are exploratory data analysis figures at the daily, weekly and monthly time steps. Lastly included is the R code used to extract the depth to water logger data from the National Park Service Aquarius data system, and to create the exploratory data analysis figures.

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Question Paper Solutions of chapter Exploratory Data Analytics and Descriptive Statistics of Data Analytics Skills for Managers, 5th Semester , Bachelor in Business Administration 2020 - 2021

This repository contains material related to the analysis performed in the article "Best Practices for Your Exploratory Factor Analysis: a Factor Tutorial". The material includes the data used in the analyses in .dat format, the labels (.txt) of the variables used in the Factor software, the outputs (.txt) evaluated in the article, and videos (.mp4 with English subtitles) recorded for the purpose of explaining the article. The videos can also be accessed in the following playlist: https://youtube.com/playlist?list=PLln41V0OsLHbSlYcDszn2PoTSiAwV5Oda. Below is a summary of the article: "Exploratory Factor Analysis (EFA) is one of the statistical methods most widely used in Administration, however, its current practice coexists with rules of thumb and heuristics given half a century ago. The purpose of this article is to present the best practices and recent recommendations for a typical EFA in Administration through a practical solution accessible to researchers. In this sense, in addition to discussing current practices versus recommended practices, a tutorial with real data on Factor is illustrated, a software that is still little known in the Administration area, but freeware, easy to use (point and click) and powerful. The step-by-step illustrated in the article, in addition to the discussions raised and an additional example, is also available in the format of tutorial videos. Through the proposed didactic methodology (article-tutorial + video-tutorial), we encourage researchers/methodologists who have mastered a particular technique to do the same. Specifically, about EFA, we hope that the presentation of the Factor software, as a first solution, can transcend the current outdated rules of thumb and heuristics, by making best practices accessible to Administration researchers". STEPS TO REPRODUCE This repository is composed of four types of files: 1) three video files in .mp4 format (with English subtitles), which discuss the article and the extra example mentioned in it; 2) two databases in .dat format: i) 1047 observations with 24 variables of the WHOQOL instrument discussed in the article; and ii) 918 observations with 10 variables of the FWB scale (extra example); 3) two labels files (.txt format) to be incorporated into the Factor software; and 4) five output files in .txt format. The steps are: 1st: Read the article “Best Practices for Your Exploratory Factor Analysis: a Factor Tutorial”. DOI: 10.1590/1982-7849rac2022210085.en; OR 1st: Watch the videos: i) 1_Video_BestPractices.mp4 (https://youtu.be/ITh1w4tFerA); and ii) 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0); 2nd: Insert the database WHOQOL_Data.dat into the Factor software and, optionally, the label file WHOQOL_Labels.txt, as explained in section 4.2 of the article or in the section that begins at the timestamp 6:35 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=395); 3rd: Configure the analyses as explained in section 4.3 of the article or in the section that begins at the timestamp 10:45 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=645); 4th: Interpret the first output file (1_Output_WHOQOL_4Factors.txt) as explained in section 4.4 of the article or in the section that begins at the timestamp 20:45 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=1245); 5th: Interpret the second output file (2_Output_WHOQOL_2Factors.txt) as explained in the section that starts at the timestamp 49:53 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=2993); 6th: Interpret the third output file (3_Output_WHOQOL_2Factors_Ajusted.txt) as explained in the section that starts at the timestamp 1:05:45 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=3945); and 7th: Interpret the fourth output file (4_Output_WHOQOL_2Factors_Bifactor.txt) as explained in the section that starts at the timestamp 1:13:14 of the video 2_Video_MultidimensionalExample.mp4 (https://youtu.be/9X77ARoyys0?t=4394); OR, optionally, to replicate the extra example mentioned in the article: 8th: Insert the database FWB_Data.dat into the Factor software and, optionally, the label file FWB_Labels.txt, as explained in the section that starts at the timestamp 4:50 of the video 3_Video_UnidimensionalExample.mp4 (https://youtu.be/wFTGJG8XRRs?t=290); 9th: Configure the analyses as explained in the section that starts at the timestamp 8:32 of the video 3_Video_UnidimensionalExample.mp4 (https://youtu.be/wFTGJG8XRRs?t=512); and 10th: Interpret the output file FWB_Output.txt as explained in the section that begins at the timestamp 22:58 of the video 3_Video_UnidimensionalExample.mp4 (https://youtu.be/wFTGJG8XRRs?t=1378).

Wetlands Ecological Integrity Depth to Water Logger data from 2007-2019 at Rocky Mountain National Park. This includes Raw dataset (primarily hourly), daily summaries, weekly summaries, and monthly summaries. Included in the data package are exploratory data analysis figures at the daily, weekly and monthly time steps. Lastly included is the R code used to extract the depth to water logger data from the National Park Service Aquarius data system, and to create the exploratory data analysis figures.