Snapshot img { margin: 10px !important; } About SME Big Data Big data solutions include a wide range of hardware, software, and services required for processing and analyzing structured and unstructured data that is too big for traditional data processing tools to manage. These data are generated by various sources such as mobile devices, digital repositories, and enterprise applications and range in size from terabytes (10^12 bytes) to petabytes (10^15 bytes) and even exabytes (10^18 bytes). Due to the considerably large size of big data, it is difficult for SMEs to manage and analyze the data using existing traditional data processing tools. Big data solutions are being used for a wide range of applications such as conversation analysis in social networking websites, fraud management in the BFSI sector, and disease diagnosis in the Healthcare sector. Due to the increasing need for big data solutions, the Global SME Big Data market is expected to witness rapid growth during the forecast period. TechNavio's analysts forecast the Global SME Big Data market will grow at a CAGR of 42.94 percent over the period 2013-2018.Covered in this Report This report covers the present scenario and the growth prospects of the Global SME Big Data market for the period 2014-2018. To calculate the market size, the report considers revenue generated from sales ofHardware: Big data storage, servers, and networking componentsSoftware applications: Apache Hadoop, NoSQL, Cassandra, and other big data software applicationsServices: Big data analytics and consulting, implementation, support, and professional servicesTechNavio's report, the Global SME Big Data Market 2014-2018, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the APAC, the EMEA, and the Americas regions; it also covers the Global SME Big Data market landscape and its growth prospects in the coming years. The report also includes a discussion of the key vendors operating in this market.Key RegionsAmericas APACEMEAKey VendorsHewlett-Packard Co.IBM Corp.Oracle Corp.Teradata Corp.Other Prominent VendorsAmazon Web Services, Inc.Cloudera, Inc.Couchbase Inc.EMC Corp.Google Inc.Microsoft Corp.SAP AGSplunk Inc.Key Market DriverIncreasing Need to Improve Business Processes Efficiency.For a full, detailed list, view our report.Key Market ChallengeLack of Awareness among SMEs about Potential of Big Data Solutions.For a full, detailed list, view our report.Key Market TrendIncreasing Market Consolidation.For a full, detailed list, view our report.Key Questions Answered in this ReportWhat will the market size be in 2018 and what will the growth rate be?What are the key market trends?What is driving this market?What are the challenges to market growth?Who are the key vendors in this market space?What are the market opportunities and threats faced by the key vendors?What are the strengths and weaknesses of the key vendors?You can request one free hour of our analyst’s time when you purchase this market report. Details are provided within the report.

Unlock the potential of your data with our comprehensive analysis of the booming Business Analysis Tools and Software market. Discover key trends, growth drivers, and leading vendors shaping this dynamic sector, projected to reach [estimated market size in 2033] by 2033. Learn how augmented analytics, predictive modeling, and cloud-based solutions are revolutionizing business intelligence.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027. What is Big data? Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. Big data analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offerings, are driving innovatio

The High-Performance Data Analytics market is booming, projected to reach $97.19M in 2025 with a 23.63% CAGR. Discover key drivers, trends, and regional insights shaping this rapidly evolving sector. Learn about leading companies and lucrative investment opportunities. Recent developments include: May 2023: NeuroBlade announced its partnership with Dell Technologies to accelerate data analytics. This solution will offer customers security and reliability, coupled with the industry's first processor architecture proven to accelerate high throughput data analytics workloads. Through the partnership, NeuroBlade strengthen its market strategy and reinforces demand for advanced solutions., January 2023: Atos declared that it was selected by Austrian AVL List GmbH to deliver a new high-performance computing cluster based on BullSequanaXH2000 servers along with a five-year maintenance service. As the world's significant mobility technology provider for development, simulation, and testing in the automotive industry, the company would rely on Atos'supercomputer to drive more complex and powerful simulations while optimizing its energy consumption.. Key drivers for this market are: Growing Number of IT & Database Industry Across the Globe, Growing Data Volumes; Advancements in High-Performance Computing Activities. Potential restraints include: Growing Number of IT & Database Industry Across the Globe, Growing Data Volumes; Advancements in High-Performance Computing Activities. Notable trends are: On-Demand to Witness the Growth.

IntroductionThe UK Biobank (UKB) is a resource that includes detailed health-related data on about 500,000 individuals and is available to the research community. However, several obstacles limit immediate analysis of the data: data files vary in format, may be very large, and have numerical codes for column names.Resultsukbtools removes all the upfront data wrangling required to get a single dataset for statistical analysis. All associated data files are merged into a single dataset with descriptive column names. The package also provides tools to assist in quality control by exploring the primary demographics of subsets of participants; query of disease diagnoses for one or more individuals, and estimating disease frequency relative to a reference variable; and to retrieve genetic metadata.ConclusionHaving a dataset with meaningful variable names, a set of UKB-specific exploratory data analysis tools, disease query functions, and a set of helper functions to explore and write genetic metadata to file, will rapidly enable UKB users to undertake their research.

The High-Performance Data Analytics (HPDA) market is experiencing robust growth, projected to reach $97.19 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.63% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and velocity of data generated across various industries necessitate advanced analytical capabilities to extract actionable insights. Furthermore, the rise of cloud computing and the adoption of on-demand services are making HPDA solutions more accessible and cost-effective for businesses of all sizes. The BFSI (Banking, Financial Services, and Insurance), Government & Defense, and Energy & Utilities sectors are leading adopters, leveraging HPDA to enhance operational efficiency, improve risk management, and gain a competitive edge. Technological advancements in areas like artificial intelligence (AI), machine learning (ML), and big data processing further contribute to market expansion. While the initial investment in HPDA infrastructure can be a restraint for some smaller enterprises, the long-term benefits in terms of improved decision-making and cost savings are proving compelling. The market is segmented by component (hardware, software, services), deployment (on-premise, on-demand), organization size (SMEs, large enterprises), and end-user industry. Competition is intense, with major players like SAS Institute, Amazon Web Services, Juniper Networks, and others vying for market share through innovation and strategic partnerships. The North American market currently holds a significant share due to high technological adoption rates and the presence of major technology companies. However, Asia-Pacific regions, particularly China and India, are witnessing rapid growth, presenting lucrative opportunities for HPDA vendors in the coming years. The projected market trajectory indicates substantial growth opportunities for HPDA solutions providers. The continued expansion of data-intensive applications across diverse sectors will remain a primary driver, further intensified by advancements in data analytics techniques and the ongoing digital transformation across industries. The shift towards cloud-based HPDA deployments is expected to accelerate, offering scalability and cost optimization benefits for organizations. Geographic expansion, particularly in developing economies, will unlock significant untapped potential. While competitive pressures remain, companies successfully differentiating their offerings through superior performance, robust security features, and tailored solutions for specific industry needs will be well-positioned to capitalize on the ongoing market expansion. Furthermore, strategic partnerships and mergers & acquisitions are anticipated to shape the competitive landscape in the coming years. Recent developments include: May 2023: NeuroBlade announced its partnership with Dell Technologies to accelerate data analytics. This solution will offer customers security and reliability, coupled with the industry's first processor architecture proven to accelerate high throughput data analytics workloads. Through the partnership, NeuroBlade strengthen its market strategy and reinforces demand for advanced solutions., January 2023: Atos declared that it was selected by Austrian AVL List GmbH to deliver a new high-performance computing cluster based on BullSequanaXH2000 servers along with a five-year maintenance service. As the world's significant mobility technology provider for development, simulation, and testing in the automotive industry, the company would rely on Atos'supercomputer to drive more complex and powerful simulations while optimizing its energy consumption.. Key drivers for this market are: Growing Number of IT & Database Industry Across the Globe, Growing Data Volumes; Advancements in High-Performance Computing Activities. Potential restraints include: High Investment Cost, Stringent Government Regulations. Notable trends are: On-Demand to Witness the Growth.

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

Recent advances in native mass spectrometry (MS) and denatured intact protein MS have made these techniques essential for biotherapeutic characterization. As MS analysis has increased in throughput and scale, new data analysis workflows are needed to provide rapid quantitation from large datasets. Here, we describe the UniDec processing pipeline (UPP) for the analysis of batched biotherapeutic intact MS data. UPP is built into the UniDec software package, which provides fast processing, deconvolution, and peak detection. The user and programming interfaces for UPP read a spreadsheet that contains the data file names, deconvolution parameters, and quantitation settings. After iterating through the spreadsheet and analyzing each file, it returns a spreadsheet of results and HTML reports. We demonstrate the use of UPP to measure the correct pairing percentage on a set of bispecific antibody data and to measure drug-to-antibody ratios from antibody–drug conjugates. Moreover, because the software is free and open-source, users can easily build on this platform to create customized workflows and calculations. Thus, UPP provides a flexible workflow that can be deployed in diverse settings and for a wide range of biotherapeutic applications.

Large Language Models for Multilingual Code Generation: A Benchmark and a Study on Code Quality

Abstract

Having been trained in the wild, Large Language Models (LLMs) may suffer from different types of bias. As shown in previous studies outside software engineering, this includes a language bias, i.e., these models perform differently depending on the language used for the query/prompt. However, so far the impact of language bias on source code generation has not been thoroughly investigated. Therefore, in this paper, we study the influence of the language adopted in the prompt on the quality of the source code generated by three LLMs, specifically GPT, Claude, and DeepSeek. We consider 230 coding tasks for Python and 230 for Java, and translate their related prompts into four languages: Chinese, Hindi, Spanish, and Italian. After generating the code, we measure code quality in terms of passed tests, code metrics, warnings generated by static analysis tools, and language used for the identifiers. Results indicate that (i) source code generated from the English queries is not necessarily better in terms of passed test and quality metrics, (ii) the quality for different languages varies depending on the programming language and LLM being used, and (iii) the generated code tend to contain mixes of comments and literals written in English and the language used to formulate the prompt.

Replication Package

This replication package is organized into two main directories: data and scripts. The datadirectory contains all the data used in the analysis, including prompts and final results. The scripts directory contains all the Python scripts used for code generation and analysis.

Data

The data directory contains five subdirectories, each corresponding to a stage in the analysis pipeline. These are enumerated to reflect the order of the process:

1. prompt_translation: Contains files with manually translated prompts for each language. Each file is associated with both Python and Java. The structure of each file is as follows:

   • id: The ID of the query in the CoderEval benchmark.
   • prompt: The original English prompt.
   • summary: The original summary.
   • code: The original code.
   • translation: The translation generated by GPT.
   • correction: The manual correction of the GPT-generated translation.
   • correction_tag: A list of tags indicating the corrections made to the translation.
   • generated_code: This column is initially empty and will contain the code generated from the translated prompt.

2. generation: Contains the code generated by the three LLMs for each programming language and natural language. Each subdirectory (e.g., java_chinese_claude) contains the following:

   • files: The files with the generated code (named by the query ID).
   • report: Reports generated by static analysis tools.
   • A CSV file (e.g., java_chinese_claude.csv) containing the generated code in the corresponding column.

3. tests: Contains input files for the testing process and the results of the tests. Files in the input_files directory are formatted according to the CoderEval benchmark requirements. The results directory holds the output of the testing process.

4. quantitative_analysis: Contains all the csv reports of the static analysis tools and test output for all languages and models. These files are the inputs for the statistical analysis. The directory stats contains all the output tables for the statistical analysis, which are shown in paper's tables.

5. qualitative_analysis: Contains files used for the qualitative analysis:

   • CohenKappaagreement.csv: A file containing the subset used to compute Cohen's kappa metrics for manual analysis.
   • files: Contains all files for the qualitative analysis. Each file has the following columns:
     • id: The ID of the query in the CoderEval benchmark.
     • generated_code: The code generated by the model.
     • comments: The language used for comments.
     • identifiers: The language used for identifiers.
     • literals: The language used for literals.
     • notes: Additional notes.

6. ablation_study: Contains files for the ablation study. Each file has the following columns:

   • id: The ID of the query in the CoderEval benchmark.
   • prompt: The prompt used for code generation.
   • generated_code, comments, identifiers, and literals: Same as in the qualitative analysis. results.pdf: This file shows the table containing all the percentages of comments, identifiers and literals extracted from the csv files of the ablation study.

   Files prefixed with italian contain prompts with signatures and docstrings translated into Italian. The system prompt used is the same as the initial one (see the paper). Files with the english prefix have prompts with the original signature (in English) and the docstring in Italian. The system prompt differs as follows:

You are an AI that only responds with Python code. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature).
Use a Python code block to write your response.
Comments and identifiers must be in Italian. 
For example:
```python
print("Hello World!")

Scripts

The scripts directory contains all the scripts used to perform all the generations and analysis. All files are properly commented. Here a brief description of each file:

• code_generation.py: This script automates code generation using AI models (GPT, DeepSeek, and Claude) for different programming and natural languages. It reads prompts from CSV files, generates code based on the prompts, and saves the results in structured directories. It logs the process, handles errors, and stores the generated code in separate files for each iteration.

• computeallanalysis.py: This script performs static code analysis on generated code files using different models, languages, and programming languages. It runs various analyses (Flake8, Pylint, Lizard) depending on the programming language: for Python, it runs all three analyses, while for Java, only Lizard is executed. The results are stored in dedicated report directories for each iteration. The script ensures the creation of necessary directories and handles any errors that occur during the analysis process.

• createtestjava.py: This script processes Java code generated by different models and languages, extracting methods using a JavaParser server. It iterates through multiple iterations of generated code, extracts the relevant method code (or uses the full code if no method is found), and stores the results in a JSONL file for each language and model combination.

• deepseek_model.py: This function sends a request to the DeepSeek API, passing a system and user prompt, and extracts the generated code snippet based on the specified programming language. It prints the extracted code in blue to the console, and if any errors occur during the request or extraction, it prints an error message in red. If successful, it returns the extracted code snippet; otherwise, it returns None.

• extractpmdreport.py: This script processes PMD analysis reports in SARIF format and converts them into CSV files. It extracts the contents of ZIP files containing the PMD reports, parses the SARIF file to gather analysis results, and saves the findings in a CSV file. The output includes details such as file names, rules, messages, and the count of issues found. The script iterates through multiple languages, models, and iterations, ensuring that PMD reports are properly processed and saved for each combination.

• flake_analysis.py: The flake_analysis function runs Flake8 to analyze Python files for errors and generates a CSV report summarizing the results. It processes the output, extracting error details such as filenames, error codes, and messages. The errors are grouped by file and saved in a CSV file for easy review.

• generatepredictionclaude_java.py: The generatecodefrom_prompt function processes a JSON file containing prompts, generates Java code using the Claude API, and saves the generated code to a new JSON file. It validates each prompt, ensures it's JSON-serializable, and sends it to the Claude API for code generation. If the generation is successful, the code is stored in a structured format, and the output is saved to a JSON file for further use.

• generatepredictionclaude_python.py: This code defines a function generatecodefrom_prompt that processes a JSON file containing prompts, generates Python code using the Claude API, and saves the generated code to a new JSON file. It handles invalid values and ensures all prompts are

The market for Knowledge Graph Visualization Tools is experiencing robust growth, driven by the increasing need for organizations to effectively manage and interpret complex data relationships. The rising adoption of big data analytics, coupled with the demand for improved data visualization and understanding, is fueling this expansion. While precise market sizing data is unavailable, a reasonable estimate, considering the rapid advancements in AI and data visualization technologies, suggests a 2025 market value in the range of $500 million. This projection considers a CAGR (Compound Annual Growth Rate) of approximately 15% from a 2019 base, factoring in the escalating adoption of these tools across various sectors. Key growth drivers include the need for enhanced decision-making based on interconnected data, improved data literacy within organizations, and the demand for intuitive, easily understood data representations. The market is segmented by application (e.g., business intelligence, research & development, customer relationship management) and type (e.g., cloud-based, on-premise, open-source solutions). North America currently holds a significant market share due to early adoption and technological advancements. However, Asia-Pacific is poised for significant growth in the coming years, driven by increasing digitalization and data generation in regions like China and India. Challenges include the complexity of implementing knowledge graph solutions and the need for skilled professionals to manage and interpret the visualized data. The forecast period (2025-2033) anticipates continued expansion, with the market likely surpassing $2 billion by 2033, driven by further technological innovations and broader industry adoption. Companies are continuously developing more sophisticated tools, incorporating features like AI-powered insights and integration with other business intelligence platforms. This, combined with a growing awareness of the strategic value of data visualization for competitive advantage, will propel the market's growth trajectory. Furthermore, the increasing adoption of cloud-based solutions will contribute to market expansion, offering flexibility and scalability to organizations of all sizes. Restraints include the high initial investment costs associated with implementing knowledge graph systems and the need for specialized expertise. However, the long-term benefits in terms of improved decision-making and enhanced business efficiency are expected to outweigh these challenges.

Introduction

Welcome to the Cyclistic bike-share analysis case study! In this case study, you will perform many real-world tasks of a junior data analyst. You will work for a fictional company, Cyclistic, and meet different characters and team members. In order to answer the key business questions, you will follow the steps of the data analysis process: ask, prepare, process, analyze, share, and act. Along the way, the Case Study Roadmap tables — including guiding questions and key tasks — will help you stay on the right path.

Scenario

You are a junior data analyst working in the marketing analyst team at Cyclistic, a bike-share company in Chicago. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships. Therefore, your team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, your team will design a new marketing strategy to convert casual riders into annual members. But first, Cyclistic executives must approve your recommendations, so they must be backed up with compelling data insights and professional data visualizations.

Ask

How do annual members and casual riders use Cyclistic bikes differently?

Guiding Question:

What is the problem you are trying to solve?
  How do annual members and casual riders use Cyclistic bikes differently?
How can your insights drive business decisions?
  The insight will help the marketing team to make a strategy for casual riders

Prepare

Guiding Question:

Where is your data located?
  Data located in Cyclistic organization data.

How is data organized?
  Dataset are in csv format for each month wise from Financial year 22.

Are there issues with bias or credibility in this data? Does your data ROCCC? 
  It is good it is ROCCC because data collected in from Cyclistic organization.

How are you addressing licensing, privacy, security, and accessibility?
  The company has their own license over the dataset. Dataset does not have any personal information about the riders.

How did you verify the data’s integrity?
  All the files have consistent columns and each column has the correct type of data.

How does it help you answer your questions?
  Insights always hidden in the data. We have the interpret with data to find the insights.

Are there any problems with the data?
  Yes, starting station names, ending station names have null values.

Process

Guiding Question:

What tools are you choosing and why?
  I used R studio for the cleaning and transforming the data for analysis phase because of large dataset and to gather experience in the language.

Have you ensured the data’s integrity?
 Yes, the data is consistent throughout the columns.

What steps have you taken to ensure that your data is clean?
  First duplicates, null values are removed then added new columns for analysis.

How can you verify that your data is clean and ready to analyze? 
 Make sure the column names are consistent thorough out all data sets by using the “bind row” function.

Make sure column data types are consistent throughout all the dataset by using the “compare_df_col” from the “janitor” package.
Combine the all dataset into single data frame to make consistent throught the analysis.
Removed the column start_lat, start_lng, end_lat, end_lng from the dataframe because those columns not required for analysis.
Create new columns day, date, month, year, from the started_at column this will provide additional opportunities to aggregate the data
Create the “ride_length” column from the started_at and ended_at column to find the average duration of the ride by the riders.
Removed the null rows from the dataset by using the “na.omit function”
Have you documented your cleaning process so you can review and share those results? 
  Yes, the cleaning process is documented clearly.

Analyze Phase:

Guiding Questions:

How should you organize your data to perform analysis on it? The data has been organized in one single dataframe by using the read csv function in R Has your data been properly formatted? Yes, all the columns have their correct data type.

What surprises did you discover in the data?
  Casual member ride duration is higher than the annual members
  Causal member widely uses docked bike than the annual members
What trends or relationships did you find in the data?
  Annual members are used mainly for commute purpose
  Casual member are preferred the docked bikes
  Annual members are preferred the electric or classic bikes
How will these insights help answer your business questions?
  This insights helps to build a profile for members

Share

Guiding Quesions:

Were you able to answer the question of how ...

Pathogen diversity resulting in quasispecies can enable persistence and adaptation to host defenses and therapies. However, accurate quasispecies characterization can be impeded by errors introduced during sample handling and sequencing which can require extensive optimizations to overcome. We present complete laboratory and bioinformatics workflows to overcome many of these hurdles. The Pacific Biosciences single molecule real-time platform was used to sequence PCR amplicons derived from cDNA templates tagged with universal molecular identifiers (SMRT-UMI). Optimized laboratory protocols were developed through extensive testing of different sample preparation conditions to minimize between-template recombination during PCR and the use of UMI allowed accurate template quantitation as well as removal of point mutations introduced during PCR and sequencing to produce a highly accurate consensus sequence from each template. Handling of the large datasets produced from SMRT-UMI sequencing was facilitated by a novel bioinformatic pipeline, Probabilistic Offspring Resolver for Primer IDs (PORPIDpipeline), that automatically filters and parses reads by sample, identifies and discards reads with UMIs likely created from PCR and sequencing errors, generates consensus sequences, checks for contamination within the dataset, and removes any sequence with evidence of PCR recombination or early cycle PCR errors, resulting in highly accurate sequence datasets. The optimized SMRT-UMI sequencing method presented here represents a highly adaptable and established starting point for accurate sequencing of diverse pathogens. These methods are illustrated through characterization of human immunodeficiency virus (HIV) quasispecies. Methods This serves as an overview of the analysis performed on PacBio sequence data that is summarized in Analysis Flowchart.pdf and was used as primary data for the paper by Westfall et al. "Optimized SMRT-UMI protocol produces highly accurate sequence datasets from diverse populations – application to HIV-1 quasispecies" Five different PacBio sequencing datasets were used for this analysis: M027, M2199, M1567, M004, and M005 For the datasets which were indexed (M027, M2199), CCS reads from PacBio sequencing files and the chunked_demux_config files were used as input for the chunked_demux pipeline. Each config file lists the different Index primers added during PCR to each sample. The pipeline produces one fastq file for each Index primer combination in the config. For example, in dataset M027 there were 3–4 samples using each Index combination. The fastq files from each demultiplexed read set were moved to the sUMI_dUMI_comparison pipeline fastq folder for further demultiplexing by sample and consensus generation with that pipeline. More information about the chunked_demux pipeline can be found in the README.md file on GitHub. The demultiplexed read collections from the chunked_demux pipeline or CCS read files from datasets which were not indexed (M1567, M004, M005) were each used as input for the sUMI_dUMI_comparison pipeline along with each dataset's config file. Each config file contains the primer sequences for each sample (including the sample ID block in the cDNA primer) and further demultiplexes the reads to prepare data tables summarizing all of the UMI sequences and counts for each family (tagged.tar.gz) as well as consensus sequences from each sUMI and rank 1 dUMI family (consensus.tar.gz). More information about the sUMI_dUMI_comparison pipeline can be found in the paper and the README.md file on GitHub. The consensus.tar.gz and tagged.tar.gz files were moved from sUMI_dUMI_comparison pipeline directory on the server to the Pipeline_Outputs folder in this analysis directory for each dataset and appended with the dataset name (e.g. consensus_M027.tar.gz). Also in this analysis directory is a Sample_Info_Table.csv containing information about how each of the samples was prepared, such as purification methods and number of PCRs. There are also three other folders: Sequence_Analysis, Indentifying_Recombinant_Reads, and Figures. Each has an .Rmd file with the same name inside which is used to collect, summarize, and analyze the data. All of these collections of code were written and executed in RStudio to track notes and summarize results. Sequence_Analysis.Rmd has instructions to decompress all of the consensus.tar.gz files, combine them, and create two fasta files, one with all sUMI and one with all dUMI sequences. Using these as input, two data tables were created, that summarize all sequences and read counts for each sample that pass various criteria. These are used to help create Table 2 and as input for Indentifying_Recombinant_Reads.Rmd and Figures.Rmd. Next, 2 fasta files containing all of the rank 1 dUMI sequences and the matching sUMI sequences were created. These were used as input for the python script compare_seqs.py which identifies any matched sequences that are different between sUMI and dUMI read collections. This information was also used to help create Table 2. Finally, to populate the table with the number of sequences and bases in each sequence subset of interest, different sequence collections were saved and viewed in the Geneious program. To investigate the cause of sequences where the sUMI and dUMI sequences do not match, tagged.tar.gz was decompressed and for each family with discordant sUMI and dUMI sequences the reads from the UMI1_keeping directory were aligned using geneious. Reads from dUMI families failing the 0.7 filter were also aligned in Genious. The uncompressed tagged folder was then removed to save space. These read collections contain all of the reads in a UMI1 family and still include the UMI2 sequence. By examining the alignment and specifically the UMI2 sequences, the site of the discordance and its case were identified for each family as described in the paper. These alignments were saved as "Sequence Alignments.geneious". The counts of how many families were the result of PCR recombination were used in the body of the paper. Using Identifying_Recombinant_Reads.Rmd, the dUMI_ranked.csv file from each sample was extracted from all of the tagged.tar.gz files, combined and used as input to create a single dataset containing all UMI information from all samples. This file dUMI_df.csv was used as input for Figures.Rmd. Figures.Rmd used dUMI_df.csv, sequence_counts.csv, and read_counts.csv as input to create draft figures and then individual datasets for eachFigure. These were copied into Prism software to create the final figures for the paper.

The market for free online survey software and tools is experiencing robust growth, driven by the increasing need for efficient and cost-effective data collection across diverse sectors. The accessibility of these tools, coupled with their user-friendly interfaces, has democratized market research, enabling small businesses, academic institutions, and non-profit organizations to conduct surveys with ease. While the exact market size in 2025 is unavailable, a reasonable estimate, considering the market's growth trajectory and the expanding adoption of digital tools, places it around $1.5 billion. This robust growth is fueled by several key drivers: the rising popularity of online research methods, the need for rapid data acquisition and analysis, and the increasing sophistication of free survey software features, which now include advanced analytics and reporting capabilities. Furthermore, the diverse application across market research, academic studies, internal enterprise management and other sectors, further drives growth. Market segmentation by survey type (mobile vs. web) presents opportunities for specialized tool development and market penetration. Although some constraints like limitations in advanced features compared to paid software and data security concerns exist, the ongoing innovation and development of free software tools mitigate these challenges to a large extent. The competitive landscape is vibrant, featuring established players like SurveyMonkey and Qualtrics alongside newer entrants, fostering continuous improvement and competitive pricing. The projected Compound Annual Growth Rate (CAGR) for the market, while not explicitly given, can be estimated conservatively at 12% for the forecast period of 2025-2033. This estimate considers the continued digitalization of market research and the ongoing expansion of the online survey software market. The regional breakdown suggests North America and Europe will remain dominant markets, but the Asia-Pacific region is expected to demonstrate significant growth fueled by increasing internet penetration and a burgeoning middle class. The presence of several Chinese companies in the list of major players further supports this projection. The market will continue to witness innovation in areas such as AI-powered survey design and analysis, and increased integration with other business software platforms, further driving market growth and attracting new users.

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.

We release two tomographic scans of a pomegranate with two levels of radiation dosage for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels.

The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel.

Both dataset were collected over a 360 degrees in circular and continuous motion with 501 projections distributed evenly over the full circle. The uploaded dataset are not binned or normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 50kV, 20W, respectively. The total scanning time were 7 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.

These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's GitHub page.

For more information or guidance in using these dataset, please get in touch with

• s.b.coban [at] cwi.nl

Achieving a high-quality reconstruction of a phylogenetic tree with branch lengths proportional to absolute time (chronogram) is a difficult and time-consuming task. But the increased availability of fossil and molecular data, and time-efficient analytical techniques has resulted in many recent publications of large chronograms for a large number and wide diversity of organisms. Knowledge of the evolutionary time frame of organisms is key for research in the natural sciences. It also represent valuable information for education, science communication, and policy decisions. When chronograms are shared in public, open databases, this wealth of expertly-curated and peer-reviewed data on evolutionary timeframe is exposed in a programatic and reusable way, as intensive and localized efforts have improved data sharing practices, as well as incentivizited open science in biology. Here we present DateLife, a service implemented as an R package and an R Shiny website application available at www.datelife.org, that provides functionalities for efficient and easy finding, summary, reuse, and reanalysis of expert, peer-reviewed, public data on time frame of evolution. The main DateLife workflow constructs a chronogram for any given combination of taxon names by searching a local chronogram database constructed and curated from the Open Tree of Life Phylesystem phylogenetic database, which incorporates phylogenetic data from the TreeBASE database as well. We implement and test methods for summarizing time data from multiple source chronograms using supertree and congruification algorithms, and using age data extracted from source chronograms as secondary calibration points to add branch lengths proportional to absolute time to a tree topology. DateLife will be useful to increase awareness of the existing variation in alternative hypothesis of evolutionary time for the same organisms, and can foster exploration of the effect of alternative evolutionary timing hypotheses on the results of downstream analyses, providing a framework for a more informed interpretation of evolutionary results. Methods This dataset contains files, figures and tables from the two examples shown in the manuscript (small example and fringillidae example), as well as from the cross validation analysis performed. Small example of the Datelife workflow. 1. Processed an input of 6 bird species within the Passeriformes (Pheucticus tibialis, Rhodothraupis celaeno, Emberiza citrinella, Emberiza leucocephalos, Emberiza elegans and Platyspiza crassirostris); 2. Used process names to search DateLife's chronogram database; 3. Summarized results from matching chronograms. Fringillidae example: http://phylotastic.org/datelife/articles/fringiliidae.html Cross validation: We performed a cross validation analysis of the DateLife workflow using 19 Fringillidae chronograms found in datelife's database. We used the individual tree topologies from each of the 19 source chronograms as inputs, treating their node ages as unknown. We then estimated dates for these topologies using node ages of chronograms belonging t o the remaining 12 studies as secondary calibrations, smoothingwith BLADJ.

We report theoretical best estimates of vertical transition energies (VTEs) for a large number of excited states and molecules: the quest database. This database includes 1489 aug-cc-pVTZ VTEs (731 singlets, 233 doublets, 461 triplets, and 64 quartets) for both valence and Rydberg transitions occurring in molecules containing from 1 to 16 non-hydrogen atoms. Quest also includes a significant list of VTEs for states characterized by a partial or genuine double-excitation character, known to be particularly challenging for many computational methods. The vast majority of the reported values are deemed chemically accurate, that is, are within ±0.05 eV of the FCI/aug-cc-pVTZ estimate. This allows for a balanced assessment of the performance of popular excited-state methodologies. We report the results of such benchmarks for various single- and multireference wave function approaches, and provide extensive Supporting Information allowing testing of other models. All corresponding data associated with the quest database, along with analysis tools, can be found in the associated GitHub repository at the following URL: https://github.com/pfloos/QUESTDB.

Intelligent Risk Management Market Outlook

The global intelligent risk management market size was valued at approximately USD 8.7 billion in 2023 and is projected to reach USD 22.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.1% during the forecast period. This growth is primarily driven by the increasing complexity of business operations and the rising need for advanced risk management solutions that leverage artificial intelligence and machine learning technologies.

One of the key growth factors in the intelligent risk management market is the rapid digital transformation across various industry verticals. As businesses increasingly adopt digital technologies, the complexity and volume of data have grown exponentially, requiring sophisticated risk management solutions. These intelligent systems utilize advanced data analytics, AI, and machine learning to predict, manage, and mitigate risks effectively. This digital shift has created a conducive environment for the adoption of intelligent risk management solutions, ensuring real-time insights and proactive risk mitigation strategies.

Additionally, regulatory pressures have amplified the demand for more robust risk management frameworks. Businesses are now required to comply with an ever-growing list of regulations and standards, which necessitates the deployment of comprehensive risk management solutions. Intelligent risk management tools not only help in regulatory compliance but also enhance the overall governance framework. This compliance-driven need for advanced risk management tools is a significant driver for market growth.

The increasing occurrence of cyber threats and data breaches is another pivotal factor propelling the intelligent risk management market. As cyber-attacks grow in both number and sophistication, organizations face unprecedented risks to their data and operations. Intelligent risk management systems equipped with AI and machine learning capabilities offer a proactive approach to identify potential vulnerabilities, assess threats, and implement mitigation strategies, thereby safeguarding businesses against cyber risks.

Risk Analytics plays a crucial role in the intelligent risk management landscape, providing organizations with the ability to analyze vast amounts of data to identify potential risks and vulnerabilities. By leveraging advanced algorithms and machine learning techniques, risk analytics solutions offer predictive insights that enable businesses to make informed decisions and implement effective risk mitigation strategies. As the volume and complexity of data continue to grow, the demand for robust risk analytics tools is increasing, helping organizations to not only comply with regulatory requirements but also to enhance their overall risk management frameworks. The integration of risk analytics into intelligent risk management systems ensures that businesses can proactively address emerging threats and maintain operational resilience in an ever-evolving risk environment.

Regionally, North America is expected to dominate the intelligent risk management market, driven by the presence of a large number of key market players and early adoption of advanced technologies. The region's strong regulatory framework and high awareness regarding risk management further contribute to its leading position. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period, owing to rapid industrialization, growing IT sector, and increasing focus on regulatory compliance.

Component Analysis

The intelligent risk management market by component is segmented into software and services. The software segment is poised to hold a significant share of the market, driven by the increasing adoption of advanced risk management platforms that offer real-time analytics, risk assessment, and mitigation strategies. These software solutions are designed to integrate seamlessly with existing enterprise systems, providing comprehensive risk management capabilities. The ongoing advancements in AI and machine learning algorithms further enhance the functionality of these software solutions, making them indispensable for modern enterprises.

Within the software segment, predictive analytics and risk assessment tools are witnessing high demand. These tools leverage large datasets and advanced algorithms to predict potential risks and provide actionable insights for mitigation. Enterprises are incre