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.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

The global Data Analysis Services market is poised for substantial expansion, projected to reach a significant valuation by 2060. Driven by an ever-increasing volume of digital data and the imperative for businesses to extract actionable insights for strategic decision-making, the market is expected to grow at a Compound Annual Growth Rate (CAGR) of 10.2% from 2025 to 2033. This robust growth is fueled by the transformative power of data in optimizing operations, enhancing customer experiences, and identifying new revenue streams across diverse industries. Key applications such as retail are leveraging data analysis for personalized marketing and inventory management, while the medical industry utilizes it for predictive diagnostics and drug discovery. Manufacturing sectors are benefiting from data-driven process optimization and predictive maintenance, further underscoring the broad applicability and essential nature of these services. The increasing adoption of advanced analytics techniques, including AI and machine learning, is a critical factor propelling this market forward, enabling more sophisticated data interpretation and forecasting. The competitive landscape features a blend of established technology giants and specialized analytics firms, all vying to provide cutting-edge solutions. Major players like IBM, Microsoft, Oracle, and SAP are investing heavily in their data analysis platforms and service offerings, while companies such as Accenture, PwC, and SAS Institute are recognized for their consulting and implementation expertise. Trends like the rise of cloud-based analytics, the demand for real-time data processing, and the growing emphasis on data governance and security are shaping the market's trajectory. While the potential for significant returns and competitive advantage through data analysis remains a powerful driver, challenges such as data privacy concerns, the scarcity of skilled data professionals, and the cost of implementing sophisticated analytics solutions can act as restraints. Nevertheless, the overarching demand for data-driven insights to navigate an increasingly complex business environment ensures a dynamic and growth-oriented future for the Data Analysis Services market. This report delves into the dynamic global Data Analysis Services market, providing an in-depth analysis from the historical period of 2019-2024 through to an estimated forecast period of 2025-2033. With a base year of 2025, the study meticulously examines market size, growth drivers, challenges, and future trends, offering actionable insights for stakeholders. The projected market value is expected to reach multi-million dollar figures, reflecting the escalating importance of data-driven decision-making across industries.

Imaging mass spectrometry (imaging MS) has emerged in the past decade as a label-free, spatially resolved, and multipurpose bioanalytical technique for direct analysis of biological samples from animal tissue, plant tissue, biofilms, and polymer films., Imaging MS has been successfully incorporated into many biomedical pipelines where it is usually applied in the so-called untargeted mode-capturing spatial localization of a multitude of ions from a wide mass range. An imaging MS data set usually comprises thousands of spectra and tens to hundreds of thousands of mass-to-charge (m/z) images and can be as large as several gigabytes. Unsupervised analysis of an imaging MS data set aims at finding hidden structures in the data with no a priori information used and is often exploited as the first step of imaging MS data analysis. We propose a novel, easy-to-use and easy-to-implement approach to answer one of the key questions of unsupervised analysis of imaging MS data: what do all m/z images look like? The key idea of the approach is to cluster all m/z images according to their spatial similarity so that each cluster contains spatially similar m/z images. We propose a visualization of both spatial and spectral information obtained using clustering that provides an easy way to understand what all m/z images look like. We evaluated the proposed approach on matrix-assisted laser desorption ionization imaging MS data sets of a rat brain coronal section and human larynx carcinoma and discussed several scenarios of data analysis.

Step 2 (2022)

This dataset contains p-values and statistical significance data derived from analyzing various metabolic and dietary states in mice. The data supports research investigating the effects of diet and metabolic conditions on localized variables in specific regions of mice. The files included are:

1. PValues_and_Significance_Fasted.xlsx: P-values for variables under a fasted metabolic state.
2. PValues_and_Significance_CTRL.xlsx: P-values for variables under a control dietary state.
3. PValues_and_Significance_Western.xlsx: P-values for variables under a western dietary state.
4. PValues_and_Significance_Interdietary.xlsx: P-values comparing variables between different dietary states.

Data Collection Methods The data was collected by analyzing correlations between variables within localized regions of the mice. These variables were consistent within individuals but showed variation dependent on dietary or metabolic states. Data collection involved the following steps: 1. Selection of experimental groups based on dietary and metabolic conditions. 2. Quantitative measurement of specific variables in localized regions of mice. 3. Statistical analysis to determine the significance of correlations across the groups.

Data Generation and Processing 1. Generation: Measurements were obtained through laboratory analysis using standardized protocols for each dietary/metabolic condition. 2. Processing: - Statistical tests were performed to identify significant correlations (e.g., t-tests, ANOVA). - P-values were computed to quantify the significance of the relationships observed. - Data was compiled into Excel sheets for organization and clarity. Technical and Non-Technical Information - Technical Details: Each file contains tabular data with headers indicating the variable pairs analyzed, their respective p-values, and the significance level (e.g., p<0.05, p<0.01).

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Netflix is a popular streaming service that offers a vast catalog of movies, TV shows, and original contents. This dataset is a cleaned version of the original version which can be found here. The data consist of contents added to Netflix from 2008 to 2021. The oldest content is as old as 1925 and the newest as 2021. This dataset will be cleaned with PostgreSQL and visualized with Tableau. The purpose of this dataset is to test my data cleaning and visualization skills. The cleaned data can be found below and the Tableau dashboard can be found here .

Data Cleaning

We are going to: 1. Treat the Nulls 2. Treat the duplicates 3. Populate missing rows 4. Drop unneeded columns 5. Split columns Extra steps and more explanation on the process will be explained through the code comments

--View dataset

SELECT * 
FROM netflix;

--The show_id column is the unique id for the dataset, therefore we are going to check for duplicates
                                  
SELECT show_id, COUNT(*)                                                                                      
FROM netflix 
GROUP BY show_id                                                                                              
ORDER BY show_id DESC;

--No duplicates

--Check null values across columns

SELECT COUNT(*) FILTER (WHERE show_id IS NULL) AS showid_nulls,
    COUNT(*) FILTER (WHERE type IS NULL) AS type_nulls,
    COUNT(*) FILTER (WHERE title IS NULL) AS title_nulls,
    COUNT(*) FILTER (WHERE director IS NULL) AS director_nulls,
    COUNT(*) FILTER (WHERE movie_cast IS NULL) AS movie_cast_nulls,
    COUNT(*) FILTER (WHERE country IS NULL) AS country_nulls,
    COUNT(*) FILTER (WHERE date_added IS NULL) AS date_addes_nulls,
    COUNT(*) FILTER (WHERE release_year IS NULL) AS release_year_nulls,
    COUNT(*) FILTER (WHERE rating IS NULL) AS rating_nulls,
    COUNT(*) FILTER (WHERE duration IS NULL) AS duration_nulls,
    COUNT(*) FILTER (WHERE listed_in IS NULL) AS listed_in_nulls,
    COUNT(*) FILTER (WHERE description IS NULL) AS description_nulls
FROM netflix;

We can see that there are NULLS. 
director_nulls = 2634
movie_cast_nulls = 825
country_nulls = 831
date_added_nulls = 10
rating_nulls = 4
duration_nulls = 3 

The director column nulls is about 30% of the whole column, therefore I will not delete them. I will rather find another column to populate it. To populate the director column, we want to find out if there is relationship between movie_cast column and director column

-- Below, we find out if some directors are likely to work with particular cast

WITH cte AS
(
SELECT title, CONCAT(director, '---', movie_cast) AS director_cast 
FROM netflix
)

SELECT director_cast, COUNT(*) AS count
FROM cte
GROUP BY director_cast
HAVING COUNT(*) > 1
ORDER BY COUNT(*) DESC;

With this, we can now populate NULL rows in directors 
using their record with movie_cast 

UPDATE netflix 
SET director = 'Alastair Fothergill'
WHERE movie_cast = 'David Attenborough'
AND director IS NULL ;

--Repeat this step to populate the rest of the director nulls
--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET director = 'Not Given'
WHERE director IS NULL;

--When I was doing this, I found a less complex and faster way to populate a column which I will use next

Just like the director column, I will not delete the nulls in country. Since the country column is related to director and movie, we are going to populate the country column with the director column

--Populate the country using the director column

SELECT COALESCE(nt.country,nt2.country) 
FROM netflix AS nt
JOIN netflix AS nt2 
ON nt.director = nt2.director 
AND nt.show_id <> nt2.show_id
WHERE nt.country IS NULL;
UPDATE netflix
SET country = nt2.country
FROM netflix AS nt2
WHERE netflix.director = nt2.director and netflix.show_id <> nt2.show_id 
AND netflix.country IS NULL;


--To confirm if there are still directors linked to country that refuse to update

SELECT director, country, date_added
FROM netflix
WHERE country IS NULL;

--Populate the rest of the NULL in director as "Not Given"

UPDATE netflix 
SET country = 'Not Given'
WHERE country IS NULL;

The date_added rows nulls is just 10 out of over 8000 rows, deleting them cannot affect our analysis or visualization

--Show date_added nulls

SELECT show_id, date_added
FROM netflix_clean
WHERE date_added IS NULL;

--DELETE nulls

DELETE F...

Earth Observation Data Analytics Platform Market Outlook

According to our latest research, the global Earth Observation Data Analytics Platform market size in 2024 reached USD 5.36 billion, demonstrating robust expansion driven by technological advancements and increasing demand for actionable geospatial insights. The market is projected to grow at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 15.68 billion by the end of the period. The primary growth factor is the accelerated adoption of satellite imagery and data analytics across diverse sectors, including agriculture, environmental monitoring, defense, and urban planning, as organizations worldwide strive to leverage spatial intelligence for informed decision-making and operational efficiency.

A key driver fueling the growth of the Earth Observation Data Analytics Platform market is the rapid digital transformation across industries that increasingly rely on high-resolution satellite data for real-time insights. The proliferation of small satellites and the democratization of space technologies have made Earth observation data more accessible and affordable than ever before. This accessibility is fostering innovation in analytics platforms, enabling businesses and governments to analyze vast datasets for applications ranging from crop monitoring and yield prediction to disaster response and climate change analysis. The integration of artificial intelligence and machine learning algorithms further enhances the value extracted from these datasets, allowing for predictive analytics and automated anomaly detection, which are critical for sectors like agriculture and environmental monitoring.

Another significant growth factor is the rising awareness and regulatory emphasis on environmental sustainability and climate resilience. Governments and international organizations are increasingly mandating the use of Earth observation data for monitoring land use, deforestation, water resources, and emissions. This regulatory push is compelling industries to adopt advanced analytics platforms that can process and interpret satellite data for compliance, reporting, and strategic planning. Moreover, the growing frequency of natural disasters and the need for rapid, data-driven response mechanisms have positioned Earth observation analytics as an indispensable tool for disaster management agencies and humanitarian organizations. The synergy between public sector initiatives and private sector innovations is catalyzing the expansion of the market, as both segments seek to harness the full potential of geospatial intelligence.

The market's momentum is also bolstered by strategic collaborations and investments in research and development. Leading technology providers are forming alliances with space agencies, research institutions, and commercial satellite operators to develop next-generation analytics solutions that offer higher accuracy, faster processing times, and seamless integration with existing enterprise systems. Venture capital and government funding are further accelerating the commercialization of novel platforms, particularly those leveraging cloud computing and big data architectures. As a result, the competitive landscape is becoming increasingly dynamic, with new entrants and established players alike striving to differentiate their offerings through value-added services, user-friendly interfaces, and robust data security features.

The emergence of the Satellite Analytics Platform is revolutionizing the way industries harness the power of satellite data. By providing a comprehensive suite of tools for data processing, visualization, and interpretation, these platforms are enabling organizations to unlock deeper insights from satellite imagery. This technological advancement is particularly beneficial for sectors such as agriculture, urban planning, and environmental monitoring, where timely and accurate data is crucial for decision-making. The integration of artificial intelligence and machine learning within these platforms enhances their capability to deliver predictive analytics, making it possible to anticipate trends and anomalies with greater precision. As the demand for real-time geospatial intelligence grows, Satellite Analytics Platforms are becoming indispensable for businesses and governments aiming to optimize their operations and strategies.

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Micro Seismic Monitoring Technology Market size was $714 million in 2024 and is grow to $1340 million by 2034, a CAGR of 6.5% between 2025 and 2034.

Gene expression involves using a gene's information to create a functional gene product, producing end products like proteins or non-coding RNA, which can change phenotype.

DESCRIPTION

Help a leading mobile brand understand the voice of the customer by analyzing the reviews of their product on Amazon and the topics that customers are talking about. You will perform topic modeling on specific parts of speech. You’ll finally interpret the emerging topics.

Problem Statement:

A popular mobile phone brand, Lenovo has launched their budget smartphone in the Indian market. The client wants to understand the VOC (voice of the customer) on the product. This will be useful to not just evaluate the current product, but to also get some direction for developing the product pipeline. The client is particularly interested in the different aspects that customers care about. Product reviews by customers on a leading e-commerce site should provide a good view.

Domain: Amazon reviews for a leading phone brand

Analysis to be done: POS tagging, topic modeling using LDA, and topic interpretation

Content:

Dataset: ‘K8 Reviews v0.2.csv’

Columns:

Sentiment: The sentiment against the review (4,5 star reviews are positive, 1,2 are negative)

Reviews: The main text of the review

Steps to perform:

Discover the topics in the reviews and present it to business in a consumable format. Employ techniques in syntactic processing and topic modeling.

Perform specific cleanup, POS tagging, and restricting to relevant POS tags, then, perform topic modeling using LDA. Finally, give business-friendly names to the topics and make a table for business.

Tasks:

1.Read the .csv file using Pandas. Take a look at the top few records.

2.Normalize casings for the review text and extract the text into a list for easier manipulation.

1. Tokenize the reviews using NLTKs word_tokenize function.

2. Perform parts-of-speech tagging on each sentence using the NLTK POS tagger.

3. For the topic model, we should want to include only nouns.

   1.Find out all the POS tags that correspond to nouns.
   
   2.Limit the data to only terms with these tags.
   

4. Lemmatize.

   1. Different forms of the terms need to be treated as one.
   
   2. No need to provide POS tag to lemmatizer for now.
   

5. Remove stopwords and punctuation (if there are any).

6. Create a topic model using LDA on the cleaned-up data with 12 topics.

    1. Print out the top terms for each topic.
   
    2. What is the coherence of the model with the c_v metric?
   

7. Analyze the topics through the business lens.

    1. Determine which of the topics can be combined.
   

8. Create a topic model using LDA with what you think is the optimal number of topics

    1. What is the coherence of the model?
   

9. The business should be able to interpret the topics.

    1. Name each of the identified topics.
   
    2.Create a table with the topic name and the top 10 terms in each to present to the business.
   

Proteomics Data Analysis Software Market Outlook

As per our latest research, the global Proteomics Data Analysis Software market size was valued at USD 1.28 billion in 2024, demonstrating robust momentum with a projected CAGR of 13.2% during the forecast period. By 2033, the market is forecasted to reach approximately USD 3.85 billion, driven by technological advancements, increasing demand for personalized medicine, and the integration of artificial intelligence in proteomics workflows. The rapid expansion in high-throughput proteomics and the growing adoption of cloud-based analytics platforms are significant contributors to this upward trajectory.

A primary growth factor propelling the proteomics data analysis software market is the surging demand for precision medicine and targeted therapeutics. As healthcare systems worldwide transition towards personalized treatment regimens, there is a burgeoning need for advanced analytical tools that can comprehensively interpret complex proteomic datasets. The integration of machine learning algorithms and AI-driven analytics in these software solutions is revolutionizing the field, allowing researchers and clinicians to uncover novel biomarkers, elucidate disease mechanisms, and streamline drug discovery pipelines. Moreover, the increased focus on translational research and the emergence of multi-omics approaches are further amplifying the demand for sophisticated proteomics data analysis platforms, ensuring sustained market growth over the next decade.

Another significant driver is the exponential rise in proteomics research funding from both public and private sectors. Governments across North America, Europe, and Asia Pacific are allocating substantial grants to life sciences research, particularly for projects focused on cancer, neurodegenerative disorders, and infectious diseases. This financial influx is fueling the adoption of cutting-edge proteomics technologies and, consequently, the software required to manage and analyze large-scale proteomic data. Additionally, biopharmaceutical companies are increasingly leveraging these analytical tools to accelerate the drug development process, reduce time-to-market, and enhance the efficacy and safety profiles of new therapeutics. The convergence of these factors is expected to maintain a positive growth trajectory for the proteomics data analysis software market.

The growing prevalence of chronic diseases and the urgent need for early and accurate diagnostics are also catalyzing the adoption of proteomics data analysis software. Hospitals and clinical laboratories are integrating these platforms into their diagnostic workflows to identify disease-specific protein signatures, monitor disease progression, and tailor treatment strategies. The ability of advanced software solutions to handle large and complex datasets with high accuracy and reproducibility is proving invaluable in clinical settings. Furthermore, the ongoing shift towards digital healthcare and the proliferation of electronic health records are creating new opportunities for the integration of proteomics data with other clinical information, paving the way for more holistic and data-driven patient care.

As proteomics research continues to evolve, the demand for Next-Gen Mass Spectrometry Software is becoming increasingly apparent. These advanced software solutions are designed to handle the complexities of modern mass spectrometry data, offering enhanced capabilities for data processing, visualization, and interpretation. By integrating cutting-edge algorithms and machine learning techniques, next-gen software allows researchers to achieve greater accuracy and depth in their analyses, facilitating the discovery of novel biomarkers and therapeutic targets. The ability to seamlessly integrate with existing laboratory workflows and instrumentation further enhances the utility of these tools, making them indispensable in both academic and industrial settings. As the proteomics landscape becomes more data-intensive, the role of next-gen mass spectrometry software in driving innovation and efficiency cannot be overstated.

From a regional perspective, North America continues to lead the global proteomics data analysis software market, accounting for the largest market share in 2024. The region's dominance is attributed to its well-established healthcare

Drone Data Analytics Market Outlook

According to our latest research, the global Drone Data Analytics market size reached USD 2.85 billion in 2024, demonstrating rapid technological adoption and integration across diverse verticals. The market is expected to grow at a robust CAGR of 27.2% from 2025 to 2033, reaching a projected value of USD 25.06 billion by 2033. This exceptional growth trajectory is primarily driven by the increasing demand for real-time data processing, advancements in drone technology, and the rising need for actionable insights across sectors such as agriculture, construction, energy, and defense. As per our latest analysis, the market is witnessing dynamic transformation, with both private and public sectors investing heavily in drone data analytics to optimize operations and enhance decision-making.

One of the most significant growth factors for the Drone Data Analytics market is the exponential increase in the adoption of drones for commercial and industrial applications. Industries such as agriculture, mining, and oil & gas are leveraging drone-based data analytics for crop health monitoring, resource exploration, and infrastructure inspection. These applications yield high-resolution, geospatially accurate data, which, when processed using advanced analytics platforms, provide actionable insights that drive operational efficiency and cost savings. The growing emphasis on precision agriculture, for instance, is a testament to how drone data analytics is revolutionizing traditional farming methods, enabling farmers to make data-driven decisions that improve yield and resource management.

Another crucial driver is the continuous advancement in artificial intelligence (AI) and machine learning (ML) algorithms, which have significantly enhanced the capability of drone data analytics platforms. Modern solutions can process vast volumes of aerial imagery and sensor data in real time, enabling predictive maintenance, anomaly detection, and automated reporting. The integration of AI-driven analytics is particularly transformative in sectors like construction and energy, where timely identification of potential issues can prevent costly downtime and ensure compliance with safety regulations. Furthermore, the proliferation of cloud-based analytics platforms has democratized access to sophisticated data processing tools, allowing organizations of all sizes to harness the power of drone data analytics without significant upfront investment in IT infrastructure.

The increasing regulatory support and standardization across major economies are also propelling the market forward. Governments are recognizing the value of drone data in disaster management, urban planning, and environmental monitoring, leading to the formulation of policies that facilitate safe and efficient drone operations. This regulatory clarity is encouraging more enterprises to invest in drone data analytics solutions, confident in their ability to operate within legal frameworks. Additionally, the growing collaboration between drone manufacturers, analytics software providers, and industry stakeholders is fostering innovation, resulting in more integrated and user-friendly solutions tailored to specific industry needs.

The integration of Drone Data Processing Software is revolutionizing the way industries handle aerial data. This software plays a crucial role in transforming raw data captured by drones into meaningful insights. By utilizing sophisticated algorithms and machine learning techniques, these platforms can efficiently process large volumes of data, providing users with real-time analytics and visualization. This capability is particularly beneficial in sectors such as agriculture and construction, where timely data interpretation can lead to enhanced productivity and operational efficiency. As the demand for precise and actionable insights grows, the development and adoption of advanced Drone Data Processing Software are expected to accelerate, further driving the evolution of the Drone Data Analytics market.

From a regional perspective, North America currently dominates the Drone Data Analytics market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high concentration of technology providers, favorable regulatory environment, and substantial investments in R&D are

About Datasets:

Domain : Sales Project: Coca Cola Sales Analysis Datasets: Power BI Dataset vF Dataset Type: Excel Data Dataset Size: 52k+ records

KPI's: 1. Analyze Profit Margins per Brand 2. Sales by Region 3. Price per unit 4. Operating Profit 5. Additional Analysis

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains Power Query, Q&A visual, Key influencers visual, map chart, matrix, dynamic timeline, dashboard, formatting, text box.

This workflow adapts the approach and parameter settings of Trans-Omics for precision Medicine (TOPMed). The RNA-seq pipeline originated from the Broad Institute. There are in total five steps in the workflow starting from:

1. Read alignment using STAR which produces aligned BAM files including the Genome BAM and Transcriptome BAM.
2. The Genome BAM file is processed using Picard MarkDuplicates. producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).
3. SAMtools index is then employed to generate an index for the BAM file, in preparation for the next step.
4. The indexed BAM file is processed further with RNA-SeQC which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.
5. In parallel with transcript quantification, isoform expression levels are quantified by RSEM. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.

For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. Chromosome 12 was extracted from the Homo Sapien Assembly 38 reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.

This dataset folder is a CWLProv Research Object that captures the Common Workflow Language execution provenance, see https://w3id.org/cwl/prov/0.5.0 or use https://pypi.org/project/cwl

Steps to reproduce

To build the research object again, use Python 3 on macOS. Built with:

• Processor 2.8GHz Intel Core i7
• Memory: 16GB
• OS: macOS High Sierra, Version 10.13.3
• Storage: 250GB

1. Install cwltool

   pip3 install cwltool==1.0.20180912090223

2. Install git lfs
   The data download with the git repository requires the installation of Git lfs:
   https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs

3. Get the data and make the analysis environment ready:

   git clone https://github.com/FarahZKhan/cwl_workflows.git
   cd cwl_workflows/
   git checkout CWLProvTesting
   ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh

4. Run the following commands to create the CWLProv Research Object:

   cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json
   
   zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac
   sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256

The https://github.com/FarahZKhan/cwl_workflows repository is a frozen snapshot from https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL commit 027e8af41b906173aafdb791351fb29efc044120

This dataset presents a dual-version representation of employment-related data from India, crafted to highlight the importance of data cleaning and transformation in any real-world data science or analytics project.

🔹 Dataset Composition:

It includes two parallel datasets: 1. Messy Dataset (Raw) – Represents a typical unprocessed dataset often encountered in data collection from surveys, databases, or manual entries. 2. Cleaned Dataset – This version demonstrates how proper data preprocessing can significantly enhance the quality and usability of data for analytical and visualization purposes.

Each record captures multiple attributes related to individuals in the Indian job market, including: - Age Group
- Employment Status (Employed/Unemployed)
- Monthly Salary (INR)
- Education Level
- Industry Sector
- Years of Experience
- Location
- Perceived AI Risk
- Date of Data Recording

Transformations & Cleaning Applied:

The raw dataset underwent comprehensive transformations to convert it into its clean, analysis-ready form: - Missing Values: Identified and handled using either row elimination (where critical data was missing) or imputation techniques. - Duplicate Records: Identified using row comparison and removed to prevent analytical skew. - Inconsistent Formatting: Unified inconsistent naming in columns (like 'monthly_salary_(inr)' → 'Monthly Salary (INR)'), capitalization, and string spacing. - Incorrect Data Types: Converted columns like salary from string/object to float for numerical analysis. - Outliers: Detected and handled based on domain logic and distribution analysis. - Categorization: Converted numeric ages into grouped age categories for comparative analysis. - Standardization: Uniform labels for employment status, industry names, education, and AI risk levels were applied for visualization clarity.

Purpose & Utility:

This dataset is ideal for learners and professionals who want to understand: - The impact of messy data on visualization and insights - How transformation steps can dramatically improve data interpretation - Practical examples of preprocessing techniques before feeding into ML models or BI tools

It's also useful for: - Training ML models with clean inputs
- Data storytelling with visual clarity
- Demonstrating reproducibility in data cleaning pipelines

By examining both the messy and clean datasets, users gain a deeper appreciation for why “garbage in, garbage out” rings true in the world of data science.

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Text Analytics Market Size 2024-2028

The text analytics market size is forecast to increase by USD 18.08 billion, at a CAGR of 22.58% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of Service-Oriented Architecture (SOA) among end-users. SOA's flexibility and scalability make it an ideal choice for text analytics applications, enabling organizations to process vast amounts of unstructured data and gain valuable insights. Additionally, the ability to analyze large volumes of unstructured data provides valuable insights through data analytics, enabling informed decision-making and competitive advantage. Furthermore, the emergence of advanced text analytical tools is expanding the market's potential by offering enhanced capabilities, such as sentiment analysis, entity extraction, and topic modeling. However, the market faces challenges that require careful consideration. System integration and interoperability issues persist, as text analytics solutions must seamlessly integrate with existing IT infrastructure and data sources.
Ensuring compatibility and data exchange between various systems can be a complex and time-consuming process. Addressing these challenges through strategic partnerships, standardization efforts, and open APIs will be essential for market participants to capitalize on the opportunities presented by the market's growth.

What will be the Size of the Text Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing demand for insightful data interpretation across various sectors. Text preprocessing techniques, such as stop word removal and lexical analysis, form the foundation of text analytics, enabling the extraction of meaningful insights from unstructured data. Topic modeling and transformer networks are current trends, offering improved accuracy and efficiency in identifying patterns and relationships within large volumes of text data. Applications of text analytics extend to fake news detection, risk management, and brand monitoring, among others. Data mining, customer feedback analysis, and data governance are essential components of text analytics, ensuring data security and maintaining data quality.

Text summarization, named entity recognition, deep learning, and predictive modeling are advanced techniques that enhance the capabilities of text analytics, providing actionable insights through data interpretation and data visualization. Machine learning algorithms, including machine learning and deep learning, play a crucial role in text analytics, with applications in spam detection, sentiment analysis, and predictive modeling. Syntactic analysis and semantic analysis offer deeper understanding of text data, while algorithm efficiency and performance optimization ensure the scalability of text analytics solutions. Text analytics continues to unfold, with ongoing research and development in areas such as prescriptive modeling, API integration, and data cleaning, further expanding its applications and capabilities.

The future of text analytics lies in its ability to provide valuable insights from unstructured data, driving informed decision-making and business growth.

How is this Text Analytics Industry segmented?

The text analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


Component

  Software
  Services


Geography

  North America

    US


  Europe

    France
    Germany


  APAC

    China
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Text analytics is a dynamic and evolving market, driven by the increasing importance of data-driven insights for businesses. Cloud computing plays a significant role in its growth, as companies such as Microsoft, SAP SE, SAS Institute, IBM, Lexalytics, and Open Text offer text analytics software and services via the Software-as-a-Service (SaaS) model. This approach reduces upfront costs for end-users, as they do not need to install hardware and software on their premises. Instead, these solutions are maintained at the company's data center, allowing end-users to access them on a subscription basis. Text preprocessing, topic modeling, transformer networks, and other advanced techniques are integral to text analytics.

Fake news detection, spam filtering, sentiment analysis, and social media monitoring are essential applications. Deep learning, machine l

SAR Data Analytics Market Outlook

According to our latest research, the global SAR Data Analytics market size reached USD 1.82 billion in 2024, exhibiting robust momentum driven by rapid technological advancements and increasing adoption across various sectors. The market is projected to expand at a CAGR of 12.4% from 2025 to 2033, culminating in a forecasted value of USD 5.23 billion by 2033. This strong growth is primarily fueled by the rising demand for actionable geospatial intelligence, the proliferation of satellite launches, and the growing need for real-time data analytics in critical applications such as defense, disaster management, and environmental monitoring.

One of the primary growth factors for the SAR Data Analytics market is the increasing reliance on advanced remote sensing technologies across both public and private sectors. Synthetic Aperture Radar (SAR) technology enables all-weather, day-and-night imaging, making it invaluable for applications that require continuous monitoring regardless of environmental conditions. Governments and organizations are leveraging SAR-based analytics for enhanced border surveillance, maritime monitoring, and infrastructure management. The capability to derive meaningful insights from vast volumes of SAR data has become a strategic asset, driving investments in analytics platforms that can process, interpret, and visualize complex datasets efficiently. As machine learning and AI integration within SAR analytics platforms improve, the accuracy and speed of data interpretation are reaching new heights, further enhancing the value proposition for end-users.

Another significant driver is the increasing frequency and severity of natural disasters, which has underscored the critical importance of timely and precise situational awareness. SAR Data Analytics plays a pivotal role in disaster management by providing near real-time imagery and change detection capabilities, enabling authorities to assess damage, coordinate response efforts, and allocate resources more effectively. The growing awareness of climate change and its impact on global weather patterns has also heightened the need for continuous environmental monitoring. SAR analytics empower organizations to monitor deforestation, glacial movements, urban expansion, and agricultural trends, supporting data-driven decision-making for sustainability and risk mitigation. The integration of cloud-based solutions is further enhancing accessibility, allowing stakeholders to access and analyze SAR data remotely and collaboratively.

The market is also witnessing robust growth due to increasing investments in space technology and the democratization of satellite data access. The surge in satellite launches, particularly by commercial operators, has resulted in a significant increase in the availability of high-resolution SAR imagery. This abundance of data is fostering innovation in analytics solutions tailored for specific industry verticals such as oil and gas exploration, mining, and precision agriculture. The competitive landscape is characterized by a wave of partnerships and collaborations between satellite data providers, analytics software vendors, and end-users, aimed at delivering integrated solutions that address unique operational challenges. The growing adoption of cloud deployment models and the emergence of subscription-based analytics services are lowering entry barriers, enabling small and medium enterprises to harness the power of SAR data analytics.

Regionally, North America continues to lead the SAR Data Analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to strong investments in defense and security, a mature space industry, and the early adoption of advanced analytics technologies. Europe is witnessing substantial growth, driven by cross-border environmental monitoring initiatives and increasing commercial applications. Asia Pacific is emerging as a high-growth region, propelled by expanding space programs, rising awareness of disaster management, and burgeoning commercial sector demand. Latin America and the Middle East & Africa, while still nascent, are expected to witness accelerated adoption as satellite infrastructure and digital transformation initiatives gain traction.

Component Analysis

The SAR Data Analytics market by component is segmented into Software, Hardware, and Services</b&g