This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into “pictures,” and develop interactive visualizations, for example, with the virtual earth as a plotting canvas. In addition to describing the goals and details of each assignment, we also discuss the broader topic of graphics and key concepts that we think warrant inclusion in the statistics curricula. We advocate that more attention needs to be paid to this fundamental field of statistics at all levels, from introductory undergraduate through graduate level courses. With the rapid rise of tools to visualize data, for example, Google trends, GapMinder, ManyEyes, and Tableau, and the increased use of graphics in the media, understanding the principles of good statistical graphics, and having the ability to create informative visualizations is an ever more important aspect of statistics education. Supplementary materials containing code and data for the assignments are available online.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. 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. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

Replication Package for A Study on the Pythonic Functional Constructs' Understandability

This package contains several folders and files with code and data used in the study.

examples/
Contains the code snippets used as objects of the study, named as reported in Table 1, summarizing the experiment design.

RQ1-RQ2-files-for-statistical-analysis/
Contains three .csv files used as input for conducting the statistical analysis and drawing the graphs for addressing the first two research questions of the study. Specifically:

- ConstructUsage.csv contains the declared frequency usage of the three functional constructs object of the study. This file is used to draw Figure 4.
- RQ1.csv contains the collected data used for the mixed-effect logistic regression relating the use of functional constructs with the correctness of the change task, and the logistic regression relating the use of map/reduce/filter functions with the correctness of the change task.
- RQ1Paired-RQ2.csv contains the collected data used for the ordinal logistic regression of the relationship between the perceived ease of understanding of the functional constructs and (i) participants' usage frequency, and (ii) constructs' complexity (except for map/reduce/filter).

inter-rater-RQ3-files/
Contains four .csv files used as input for computing the inter-rater agreement for the manual labeling used for addressing RQ3. Specifically, you will find one file for each functional construct, i.e., comprehension.csv, lambda.csv, and mrf.csv, and a different file used for highlighting the reasons why participants prefer to use the procedural paradigm, i.e., procedural.csv.

Questionnaire-Example.pdf
This file contains the questionnaire submitted to one of the ten experimental groups within our controlled experiment. Other questionnaires are similar, except for the code snippets used for the first section, i.e., change tasks, and the second section, i.e., comparison tasks.

RQ2ManualValidation.csv
This file contains the results of the manual validation being done to sanitize the answers provided by our participants used for addressing RQ2. Specifically, we coded the behavior description using four different levels: (i) correct, (ii) somewhat correct, (iii) wrong, and (iv) automatically generated.

RQ3ManualValidation.xlsx
This file contains the results of the open coding applied to address our third research question. Specifically, you will find four sheets, one for each functional construct and one for the procedural paradigm. For each sheet, you will find the provided answers together with the categories assigned to them.

Appendix.pdf
This file contains the results of the logistic regression relating the use of map, filter, and reduce functions with the correctness of the change task, not shown in the paper.

FuncConstructs-Statistics.r
This file contains an R script that you can reuse to re-run all the analyses conducted and discussed in the paper.

FuncConstructs-Statistics.ipynb
This file contains the code to re-execute all the analysis conducted in the paper as a notebook.

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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 offeri

Sample data set used in an introductory course on Programming in Python

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

In this project, we aim to analyze and gain insights into the performance of students based on various factors that influence their academic achievements. We have collected data related to students' demographic information, family background, and their exam scores in different subjects.

**********Key Objectives:*********

1. Performance Evaluation: Evaluate and understand the academic performance of students by analyzing their scores in various subjects.

2. Identifying Underlying Factors: Investigate factors that might contribute to variations in student performance, such as parental education, family size, and student attendance.

3. Visualizing Insights: Create data visualizations to present the findings effectively and intuitively.

Dataset Details:

• The dataset used in this analysis contains information about students, including their age, gender, parental education, lunch type, and test scores in subjects like mathematics, reading, and writing.

Analysis Highlights:

• We will perform a comprehensive analysis of the dataset, including data cleaning, exploration, and visualization to gain insights into various aspects of student performance.

• By employing statistical methods and machine learning techniques, we will determine the significant factors that affect student performance.

Why This Matters:

Understanding the factors that influence student performance is crucial for educators, policymakers, and parents. This analysis can help in making informed decisions to improve educational outcomes and provide support where it is most needed.

Acknowledgments:

We would like to express our gratitude to [mention any data sources or collaborators] for making this dataset available.

Please Note:

This project is meant for educational and analytical purposes. The dataset used is fictitious and does not represent any specific educational institution or individuals.

Big Data Analytics for Clinical Research Market Outlook

As per our latest research, the Big Data Analytics for Clinical Research market size reached USD 7.45 billion globally in 2024, reflecting a robust adoption pace driven by the increasing digitization of healthcare and clinical trial processes. The market is forecasted to grow at a CAGR of 17.2% from 2025 to 2033, reaching an estimated USD 25.54 billion by 2033. This significant growth is primarily attributed to the rising need for real-time data-driven decision-making, the proliferation of electronic health records (EHRs), and the growing emphasis on precision medicine and personalized healthcare solutions. The industry is experiencing rapid technological advancements, making big data analytics a cornerstone in transforming clinical research methodologies and outcomes.

Several key growth factors are propelling the expansion of the Big Data Analytics for Clinical Research market. One of the primary drivers is the exponential increase in clinical data volumes from diverse sources, including EHRs, wearable devices, genomics, and imaging. Healthcare providers and research organizations are leveraging big data analytics to extract actionable insights from these massive datasets, accelerating drug discovery, optimizing clinical trial design, and improving patient outcomes. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with big data platforms has further enhanced the ability to identify patterns, predict patient responses, and streamline the entire research process. These technological advancements are reducing the time and cost associated with clinical research, making it more efficient and effective.

Another significant factor fueling market growth is the increasing collaboration between pharmaceutical & biotechnology companies and technology firms. These partnerships are fostering the development of advanced analytics solutions tailored specifically for clinical research applications. The demand for real-world evidence (RWE) and real-time patient monitoring is rising, particularly in the context of post-market surveillance and regulatory compliance. Big data analytics is enabling stakeholders to gain deeper insights into patient populations, treatment efficacy, and adverse event patterns, thereby supporting evidence-based decision-making. Furthermore, the shift towards decentralized and virtual clinical trials is creating new opportunities for leveraging big data to monitor patient engagement, adherence, and safety remotely.

The regulatory landscape is also evolving to accommodate the growing use of big data analytics in clinical research. Regulatory agencies such as the FDA and EMA are increasingly recognizing the value of data-driven approaches for enhancing the reliability and transparency of clinical trials. This has led to the establishment of guidelines and frameworks that encourage the adoption of big data technologies while ensuring data privacy and security. However, the implementation of stringent data protection regulations, such as GDPR and HIPAA, poses challenges related to data integration, interoperability, and compliance. Despite these challenges, the overall outlook for the Big Data Analytics for Clinical Research market remains highly positive, with sustained investments in digital health infrastructure and analytics capabilities.

From a regional perspective, North America currently dominates the Big Data Analytics for Clinical Research market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and strong presence of leading pharmaceutical companies. Europe follows closely, driven by increasing government initiatives to promote health data interoperability and research collaborations. The Asia Pacific region is emerging as a high-growth market, supported by expanding healthcare IT investments, rising clinical trial activities, and growing awareness of data-driven healthcare solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, due to infrastructural and regulatory challenges. Overall, the global market is poised for substantial growth across all major regions over the forecast period.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Snapshot img

Big Data Market Size 2025-2029

The big data market size is forecast to increase by USD 193.2 billion at a CAGR of 13.3% between 2024 and 2029.

The market is experiencing a significant rise due to the increasing volume of data being generated across industries. This data deluge is driving the need for advanced analytics and processing capabilities to gain valuable insights and make informed business decisions. A notable trend in this market is the rising adoption of blockchain solutions to enhance big data implementation. Blockchain's decentralized and secure nature offers an effective solution to address data security concerns, a growing challenge in the market. However, the increasing adoption of big data also brings forth new challenges. Data security issues persist as organizations grapple with protecting sensitive information from cyber threats and data breaches.
Companies must navigate these challenges by investing in robust security measures and implementing best practices to mitigate risks and maintain trust with their customers. To capitalize on the market opportunities and stay competitive, businesses must focus on harnessing the power of big data while addressing these challenges effectively. Deep learning frameworks and machine learning algorithms are transforming data science, from data literacy assessments to computer vision models.

What will be the Size of the Big Data 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 Sample

In today's data-driven business landscape, the demand for advanced data management solutions continues to grow. Companies are investing in business intelligence dashboards and data analytics tools to gain insights from their data and make informed decisions. However, with this increased reliance on data comes the need for robust data governance policies and regular data compliance audits. Data visualization software enables businesses to effectively communicate complex data insights, while data engineering ensures data is accessible and processed in real-time. Data-driven product development and data architecture are essential for creating agile and responsive business strategies. Data management encompasses data accessibility standards, data privacy policies, and data quality metrics.
Data usability guidelines, prescriptive modeling, and predictive modeling are critical for deriving actionable insights from data. Data integrity checks and data agility assessments are crucial components of a data-driven business strategy. As data becomes an increasingly valuable asset, businesses must prioritize data security and privacy. Prescriptive and predictive modeling, data-driven marketing, and data culture surveys are key trends shaping the future of data-driven businesses. Data engineering, data management, and data accessibility standards are interconnected, with data privacy policies and data compliance audits ensuring regulatory compliance.
Data engineering and data architecture are crucial for ensuring data accessibility and enabling real-time data processing. The data market is dynamic and evolving, with businesses increasingly relying on data to drive growth and inform decision-making. Data engineering, data management, and data analytics tools are essential components of a data-driven business strategy, with trends such as data privacy, data security, and data storytelling shaping the future of data-driven businesses.

How is this Big Data Industry segmented?

The big data 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.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


End-user

  BFSI
  Healthcare
  Retail and e-commerce
  IT and telecom
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the realm of big data, on-premise and cloud-based deployment models cater to varying business needs. On-premise deployment allows for complete control over hardware and software, making it an attractive option for some organizations. However, this model comes with a significant upfront investment and ongoing maintenance costs. In contrast, cloud-based deployment offers flexibility and scalability, with service providers handling infrastructure and maintenance. Yet, it introduces potential security risks, as data is accessed through multiple points and stored on external servers. Data

The Metabolomics workshop on experimental and data analysis training for untargeted metabolomics was hosted by the Proteomics Society of India in December 2019. The Workshop included six tutorial lectures and hands-on data analysis training sessions presented by seven speakers. The tutorials and hands-on data analysis sessions focused on workflows for liquid chromatography-mass spectrometry (LC-MS) based on untargeted metabolomics. We review here three main topics from the workshop which were uniquely identified as bottlenecks for new researchers: a) experimental design, b) quality controls during sample preparation and instrumental analysis and c) data quality evaluation. Our objective here is to present common challenges faced by novice researchers and present possible guidelines and resources to address them. We provide resources and good practices for researchers who are at the initial stage of setting up metabolomics workflows in their labs.

Complete detailed metabolomics/lipidomics protocols are available online at EMBL-MCF protocol including video tutorials.

Objective: The objective of this review is to identify what is known about Indigenous data analysis methods for research. Introduction: Understanding Indigenous data analyses methods for research is crucial in health research with Indigenous participants, to support culturally appropriate interpretation of research data, and culturally inclusive analyses in cross-cultural research teams. Inclusion Criteria: This review will consider primary research studies that report on Indigenous data analysis methods for research. Method: Medline (via Ovid SP), PsycINFO (via Ovid SP), Web of Science (Clarivate Analytics), Scopus (Elsevier), Cumulated Index to Nursing and Allied Health Literature CINAHL (EBSCOhost), ProQuest Central, ProQuest Social Sciences Premium (Clarivate) will be searched. ProQuest (Theses and Dissertations) will be searched for unpublished material. Studies published from inception onwards and written in English will be assessed for inclusion. Studies meeting the inclusion criteria will be assessed for methodological quality and data will be extracted.

Code is compatible with Matlab v2020. The corresponding open-source alternative is Octave (https://octave.org/).

Sample data for exercises in Further Adventures in Data Cleaning.

Big Data Analytics in Banking Market Outlook

The Big Data Analytics in Banking market size was valued at approximately USD 23.5 billion in 2023, and it is projected to grow to USD 67.2 billion by 2032, showcasing a robust CAGR of 12.3%. This exponential growth is driven by the increasing demand for more refined data analysis tools that enable banks to manage vast amounts of information and derive actionable insights. The banking sector is increasingly acknowledging the need for advanced analytics to enhance decision-making processes, improve customer satisfaction, and mitigate risks. Factors such as digital transformation, regulatory pressure, and the need for operational efficiency continue to propel the market forward.

One of the primary growth factors in the Big Data Analytics in Banking market is the heightened emphasis on risk management. Banks are continuously exposed to various risks, including credit, market, operational, and liquidity risks. Big Data Analytics plays a crucial role in identifying, measuring, and mitigating these risks. By analyzing large volumes of structured and unstructured data, banks can gain insights into potential risk factors and develop strategies to address them proactively. Furthermore, regulatory requirements mandating more stringent risk management practices have compelled banks to invest in sophisticated analytics solutions, further contributing to market growth.

Another significant driver of this market is the increasing need for enhanced customer analytics. With the rise of digital banking and fintech solutions, customers now demand more personalized services and experiences. Big Data Analytics enables banks to understand customer behavior, preferences, and needs by analyzing transaction histories, social media interactions, and other data sources. By leveraging these insights, banks can offer tailored products and services, improve customer retention rates, and gain a competitive edge in the market. Additionally, customer analytics helps banks identify cross-selling and up-selling opportunities, thereby driving revenue growth.

Fraud detection is also a critical area where Big Data Analytics has made a significant impact in the banking sector. The increasing complexity and frequency of financial frauds necessitate the adoption of advanced analytics solutions to detect and prevent fraudulent activities effectively. Big Data Analytics allows banks to analyze vast amounts of transaction data in real-time, identify anomalies, and flag suspicious activities. By employing machine learning algorithms, banks can continuously improve their fraud detection capabilities, minimizing financial losses and enhancing security for their customers. This ongoing investment in fraud detection tools is expected to contribute significantly to the growth of the Big Data Analytics in Banking market.

Data Analytics In Financial services is revolutionizing the way banks operate by providing deeper insights into financial trends and customer behaviors. This transformative approach enables financial institutions to analyze vast datasets, uncovering patterns and correlations that were previously inaccessible. By leveraging data analytics, banks can enhance their financial forecasting, optimize asset management, and improve investment strategies. The integration of data analytics in financial operations not only aids in risk assessment but also supports regulatory compliance by ensuring accurate and timely reporting. As the financial sector continues to evolve, the role of data analytics becomes increasingly pivotal in driving innovation and maintaining competitive advantage.

Regionally, North America remains a dominant player in the Big Data Analytics in Banking market, driven by the presence of major banking institutions and technology firms. The region's early adoption of advanced technologies and a strong focus on regulatory compliance have been pivotal in driving market growth. Europe follows closely, with stringent regulatory frameworks like GDPR necessitating advanced data management and analytics solutions. In the Asia Pacific region, rapid digital transformation and the growing adoption of mobile banking are key factors propelling the market forward. The Middle East & Africa and Latin America, while currently smaller markets, are experiencing steady growth as banks in these regions increasingly invest in analytics solutions to enhance their competitive positioning.

Component Analysis

In the Big Data Analytics in

Raw data outputs 1-18

Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis.

Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis.

Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis.

Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study.

Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones.

Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively.

Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively.

Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis.

Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis.

Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs.

Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis.

Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining.

Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section.

Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell.

Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").

The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:

ID = group identifier (1-2000) x = numeric (Level 1) y = numeric (Level 1) w = binary (Level 2)

In all data sets, missing values are coded as "NA".

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.