The OECD Programme for International Student Assessment (PISA) surveys collected data on students’ performances in reading, mathematics and science, as well as contextual information on students’ background, home characteristics and school factors which could influence performance. This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses. In addition to the inclusion of the necessary techniques, the manual also includes a detailed account of the PISA 2006 database and worked examples providing full syntax in SPSS.

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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

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

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

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The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request F

This statistic displays the various applications of data analytics and mining across procurement processes, according to chief procurement officers (CPOs) worldwide, as of 2017. Fifty-seven percent of the CPOs asked agreed that data analytics and mining had been applied to intelligent and advanced analytics for negotiations, and 40 percent of them indicated data analytics and mining had been applied to supplier portfolio optimization processes.

Exploratory data analysis.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Google Data Analytics Capstone Project

Cyclistic Dataset

Case Study: How Does a Bike-Share Navigate Speedy Success?

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. Characters and teams.

Cyclistic: A bike-share program that features more than 5,800 bicycles and 600 docking stations. Cyclistic sets itself apart by also offering reclining bikes, hand tricycles, and cargo bikes, making bike-share more inclusive to people with disabilities and riders who can’t use a standard two-wheeled bike. The majority of riders opt for traditional bikes; about 8% of riders use the assistive options. Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day.

Lily Moreno: The director of marketing and your manager. Moreno is responsible for the development of campaigns and initiatives to promote the bike-share program. These may include email, social media, and other channels.

Cyclistic marketing analytics team: A team of data analysts who are responsible for collecting, analyzing, and reporting data that helps guide Cyclistic marketing strategy. You joined this team six months ago and have been busy learning about Cyclistic’s mission and business goals — as well as how you, as a junior data analyst, can help Cyclistic achieve them.

Cyclistic executive team: The notoriously detail-oriented executive team will decide whether to approve the recommended marketing program.

Data overview

ride_id: It is a distinct identifier assigned to each individual ride. rideable_type: This column indicates the type of bikes used for each ride. started_at: This column denotes the timestamp when a particular ride began. ended_at: This column represents the timestamp when a specific ride concluded. start_station_name: This column contains the name of the station where the bike ride originated. start_station_id: This column represents the unique identifier for the station where the bike ride originated. end_station_name: This column contains the name of the station where the bike ride concluded. end_station_id: This column represents the unique identifier for the station where the bike ride concluded. start_lat: This column denotes the latitude coordinate of the starting point of the bike ride. start_lng: This column denotes the longitude coordinate of the starting point of the bike ride. end_lat: This column denotes the latitude coordinate of the ending point of the bike ride. end_lng: This column denotes the longitude coordinate of the ending point of the bike ride. member_casual: This column indicates whether the rider is a member or a casual user.

The share of organizations using big data analytics in market research worldwide steadily increased from 2014 to 2021, despite a slight drop in 2019. During the 2021 survey, 46 percent of respondents mentioned they used big data analytics as a research method.

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets.
Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning.
Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools’ increasing popularity can be attributed to features like sophisticated modeling and predictive analytics.
A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential.
Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software.
Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques.
Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this.
Big Data Analytics’s Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data.
Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities.
Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector.
Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

Stair descent analysis has been typically limited to laboratory staircases of 4 or 5 steps. To date there has been no report of gait parameters during unconstrained stair descent outside of the laboratory, and few motion capture datasets are publicly available. We aim to collect a dataset and perform gait analysis for stair descent outside of the laboratory. We aim to measure basic kinematic and kinetic gait parameters and foot placement behavior. We present a public stair descent dataset from 101 unimpaired participants aged 18-35 on an unconstrained 13-step staircase collected using wearable sensors. The dataset consists of kinematics (full-body joint angle and position), kinetics (plantar normal forces, acceleration), and foot placement for 30,609 steps. This is the first quantitative observation of gait data from a large number (n = 101) of participants descending an unconstrained staircase outside of a laboratory. The dataset is a public resource for understanding typical stair descent.

Embark on a transformative journey with our Data Cleaning Project, where we meticulously refine and polish raw data into valuable insights. Our project focuses on streamlining data sets, removing inconsistencies, and ensuring accuracy to unlock its full potential.

Through advanced techniques and rigorous processes, we standardize formats, address missing values, and eliminate duplicates, creating a clean and reliable foundation for analysis. By enhancing data quality, we empower organizations to make informed decisions, drive innovation, and achieve strategic objectives with confidence.

Join us as we embark on this essential phase of data preparation, paving the way for more accurate and actionable insights that fuel success."

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

Process Analytics Market size was valued at USD 1864.26 Million in 2023 and is projected to reach USD 46769.11 Million by 2030, growing at a CAGR of 49.60% during the forecast period 2024-2030.

Global Process Analytics Market Drivers

The market drivers for the Process Analytics Market can be influenced by various factors. These may include:

Initiatives for Digital Transformation: Businesses in a variety of industries are going through a digital transformation to increase production, customer happiness, and efficiency. Understanding and improving company processes is made easier with the aid of process analytics tools, which is essential for a successful digital transformation.
Growing Process Mining Adoption: Process mining solutions are becoming more and more well-liked since they facilitate the analysis of business processes using event logs. The need for process analytics solutions is being driven by these tools, which offer insights into process inefficiencies and bottlenecks.
Growing Requirement for Risk Management and Compliance: Process analytics adoption is being driven by regulatory regulations and the necessity of strong risk management frameworks in organisations. These solutions offer transparency and traceability in company processes, which aids in guaranteeing adherence to norms and regulations.
Technological Developments in Big Data and AI: Big data analytics and artificial intelligence (AI) are enhanced when combined with process analytics technologies, allowing for more precise and timely analysis. The development of technology is one of the main factors propelling the market.
Growth of Data-Driven: Judging Decision-making in business is becoming more and more dependent on data-driven insights. Process analytics solutions offer comprehensive insights into process performance and opportunities for enhancement, which helps to make better decisions.
Need for Enhanced Operational Effectiveness: Businesses are always searching for methods to cut expenses and increase operational effectiveness. Process optimisation and identification of inefficiencies result in improved resource usage and cost savings thanks to process analytics.
The expansion of cloud computing: Organisations may more easily implement and employ process analytics tools because to the scalability, flexibility, and cost advantages that come with adopting cloud-based solutions. The market for process analytics is being driven by the expansion of cloud computing.
Growing Intricacy of Business Procedures: Globalisation, mergers and acquisitions, changing market dynamics, and other factors are making business processes increasingly complicated, necessitating the use of sophisticated technologies for process analysis and management.
Improved Management of Customer Experience: Businesses are concentrating on enhancing the customer experience, and process analytics solutions aid in comprehending interactions and touchpoints with customers. This realisation facilitates process simplification for increased customer satisfaction.
Advantage of Competition: Businesses are using process analytics to improve productivity, acquire a competitive edge, and increase their ability to adapt to changes in the market.

Overview

GMAT is a feature rich system containing high fidelity space system models, optimization and targeting,
built in scripting and programming infrastructure, and customizable plots, reports and data
products, to enable flexible analysis and solutions for custom and unique applications. GMAT can
be driven from a fully featured, interactive GUI or from a custom script language. Here are some
of GMAT’s key features broken down by feature group.

Dynamics and Environment Modelling

• High fidelity dynamics models including harmonic gravity, drag, tides, and relativistic corrections
• High fidelity spacecraft modeling
• Formations and constellations
• Impulsive and finite maneuver modeling and optimization
• Propulsion system modeling including tanks and thrusters
• Solar System modeling including high fidelity ephemerides, custom celestial bodies, libration points, and barycenters
• Rich set of coordinate system including J2000, ICRF, fixed, rotating, topocentric, and many others
• SPICE kernel propagation
• Propagators that naturally synchronize epochs of multiple vehicles and avoid fixed step integration
• and interpolation

Plotting, Reporting and Product Generation

• Interactive 3-D graphics
• Customizable data plots and reports
• Post computation animation
• CCSDS, SPK, and Code-500 ephemeris generation

Optimization and Targeting

• Boundary value targeters
• Nonlinear, constrained optimization
• Custom, scriptable cost functions
• Custom, scriptable nonlinear equality and inequality constraint functions
• Custom targeter controls and constraints

Programming Infrastructure

• User defined variables, arrays, and strings
• User defined equations using MATLAB syntax. (i.e. overloaded array operation)
• Control flow such as If, For, and While loops for custom applications
• Matlab interface
• Built in parameters and calculations in multiple coordinate systems

Interfaces

• Fully featured, interactive GUI that makes simple analysis quick and easy
• Custom scripting language that makes complex, custom analysis possible
• Matlab interface for custom external simulations and calculations
• File interface for the TCOPS Vector Hold

Database to

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.

Hello! Welcome to the Capstone project I have completed to earn my Data Analytics certificate through Google. I chose to complete this case study through RStudio desktop. The reason I did this is that R is the primary new concept I learned throughout this course. I wanted to embrace my curiosity and learn more about R through this project. In the beginning of this report I will provide the scenario of the case study I was given. After this I will walk you through my Data Analysis process based on the steps I learned in this course:

1. Ask
2. Prepare
3. Process
4. Analyze
5. Share
6. Act

The data I used for this analysis comes from this FitBit data set: https://www.kaggle.com/datasets/arashnic/fitbit

" This dataset generated by respondents to a distributed survey via Amazon Mechanical Turk between 03.12.2016-05.12.2016. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. "

We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and cost-efficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Sentiment scores and behavioral metrics leveraging natural language processing from company transcripts.

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

Analytical Method Development is a crucial process in the field of scientific research and quality control. It involves creating and optimizing techniques to accurately and precisely analyze substances, compounds, or materials of interest. The primary goal is to establish reliable methods that can identify, quantify, and characterize various components within a sample. During the method development phase, scientists carefully choose suitable instruments, such as chromatographs, spectrometers, or titrators, and develop specific procedures to achieve the desired results. The process often requires iterative experimentation and data analysis to fine-tune the parameters and ensure robustness and reproducibility. Accurate analytical methods are essential in various industries, including pharmaceuticals, environmental monitoring, food safety, and more. They play a vital role in ensuring product quality, safety, and compliance with regulatory standards. In summary, analytical method development is an indispensable aspect of scientific investigations, enabling researchers to derive meaningful data and make informed decisions based on the analysis of complex samples. https://www.silverscreenandroll.com/users/sterinlab https://www.ridiculousupside.com/users/sterinlab https://www.sonicsrising.com/users/sterinlab https://www.swishappeal.com/users/sterinlab https://www.bringonthecats.com/users/sterinlab https://www.burntorangenation.com/users/sterinlab https://www.crimsonandcreammachine.com/users/sterinlab https://www.frogsowar.com/users/sterinlab https://www.ourdailybears.com/users/sterinlab https://www.rockchalktalk.com/users/sterinlab https://www.smokingmusket.com/users/sterinlab https://www.vivathematadors.com/users/sterinlab https://www.widerightnattylite.com/users/sterinlab https://www.musiccitymiracles.com/users/sterinlab https://www.stampedeblue.com/users/sterinlab https://www.celticsblog.com/users/sterinlab https://www.libertyballers.com/users/sterinlab https://www.netsdaily.com/users/sterinlab https://www.postingandtoasting.com/users/sterinlab https://www.blazersedge.com/users/sterinlab