Dataset

This dataset was created by Nafe Muhtasim

Released under CC0: Public Domain

Contents

The Data Analysis Services market is experiencing robust growth, driven by the exponential increase in data volume and the rising demand for data-driven decision-making across various industries. The market, estimated at $150 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an impressive $450 billion by 2033. This expansion is fueled by several key factors, including the increasing adoption of cloud-based analytics platforms, the growing need for advanced analytics techniques like machine learning and AI, and the rising focus on data security and compliance. The market is segmented by service type (e.g., predictive analytics, descriptive analytics, prescriptive analytics), industry vertical (e.g., healthcare, finance, retail), and deployment model (cloud, on-premise). Key players like IBM, Accenture, Microsoft, and SAS Institute are investing heavily in research and development, expanding their service portfolios, and pursuing strategic partnerships to maintain their market leadership. The competitive landscape is characterized by both large established players and emerging niche providers offering specialized solutions. The market's growth trajectory is influenced by various trends, including the increasing adoption of big data technologies, the growing prevalence of self-service analytics tools empowering business users, and the rise of specialized data analysis service providers catering to specific industry needs. However, certain restraints, such as the lack of skilled data analysts, data security concerns, and the high cost of implementation and maintenance of advanced analytics solutions, could potentially hinder market growth. Addressing these challenges through investments in data literacy programs, enhanced security measures, and flexible pricing models will be crucial for sustaining the market's momentum and unlocking its full potential. Overall, the Data Analysis Services market presents a significant opportunity for companies offering innovative solutions and expertise in this rapidly evolving landscape.

R code for running GLMM and BRT analysis

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

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. By the end of this lesson, you will have a portfolio-ready case study. Download the packet and reference the details of this case study anytime. Then, when you begin your job hunt, your case study will be a tangible way to demonstrate your knowledge and skills to potential employers.

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.

About the company

In 2016, Cyclistic launched a successful bike-share offering. Since then, the program has grown to a fleet of 5,824 bicycles that are geotracked and locked into a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the system anytime. Until now, Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments. One approach that helped make these things possible was the flexibility of its pricing plans: single-ride passes, full-day passes, and annual memberships. Customers who purchase single-ride or full-day passes are referred to as casual riders. Customers who purchase annual memberships are Cyclistic members. Cyclistic’s finance analysts have concluded that annual members are much more profitable than casual riders. Although the pricing flexibility helps Cyclistic attract more customers, Moreno believes that maximizing the number of annual members will be key to future growth. Rather than creating a marketing campaign that targets all-new customers, Moreno believes there is a very good chance to convert casual riders into members. She notes that casual riders are already aware of the Cyclistic program and have chosen Cyclistic for their mobility needs. Moreno has set a clear goal: Design marketing strategies aimed at converting casual riders into annual members. In order to do that, however, the marketing analyst team needs to better understand how annual members and casual riders differ, why casual riders would buy a membership, and how digital media could affect their marketing tactics. Moreno and her team are interested in analyzing the Cyclistic historical bike trip data to identify trends

Three questions will guide the future marketing program:

How do annual members and casual riders use Cyclistic bikes differently? Why would casual riders buy Cyclistic annual memberships? How can Cyclistic use digital media to influence casual riders to become members? Moreno has assigned you the first question to answer: How do annual members and casual rid...

The size of the Genomic Data Analysis Service market was valued at USD 5295 million in 2024 and is projected to reach USD 12769.01 million by 2033, with an expected CAGR of 13.4 % during the forecast period.

This dataset contains detailed information about the COVID-19 pandemic. The inspiration behind this dataset is to analyze trends, identify patterns, and understand the global impact of COVID-19 through SQL queries. It is designed for anyone interested in data exploration and real-world analytics.

The size of the Industrial Data Analysis Tools market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The Data Analysis Application Solution market is booming, projected to reach $45 billion by 2033 with a 15% CAGR. Explore key drivers, trends, and challenges shaping this rapidly evolving sector, featuring leading companies like SAP, Microsoft, and BigID. Discover market segmentation, regional insights, and future growth forecasts.

The data and analysis of the surveys to study the users' opinion about the presence of an avatar during a learning experience in Mixed Reality. Also there are demographic data and the open questions collected. This data was used in the paper Evaluating the Effectiveness of Avatar-Based Collaboration in XR for Pump Station Training Scenarios for the GeCon 2024 Conference.

Explore the booming Exploratory Data Analysis (EDA) Tools market, projected to reach $10.5 billion by 2025 with a 12.5% CAGR. Discover key drivers, trends, and market share for large enterprises, SMEs, graphical & non-graphical tools across North America, Europe, APAC, and more.

The North America Next Generation Sequencing (NGS) Data Analysis report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.

Single-Cell Data Analysis Software Market Outlook

According to our latest research, the global Single-Cell Data Analysis Software market size reached USD 498.6 million in 2024, driven by increasing demand for high-resolution cellular analysis in life sciences and healthcare. The market is experiencing robust expansion with a CAGR of 15.2% from 2025 to 2033, and is projected to reach USD 1,522.9 million by 2033. This impressive growth trajectory is primarily attributed to advancements in single-cell sequencing technologies, the proliferation of precision medicine, and the rising adoption of artificial intelligence and machine learning in bioinformatics.

The growth of the Single-Cell Data Analysis Software market is significantly propelled by the rapid evolution of next-generation sequencing (NGS) technologies and the increasing need for comprehensive single-cell analysis in both research and clinical settings. As researchers strive to unravel cellular heterogeneity and gain deeper insights into complex biological systems, the demand for robust data analysis tools has surged. Single-cell data analysis software enables scientists to process, visualize, and interpret large-scale datasets, facilitating the identification of rare cell populations, novel biomarkers, and disease mechanisms. The integration of advanced algorithms and user-friendly interfaces has further enhanced the accessibility and adoption of these solutions across various end-user segments, including academic and research institutes, biotechnology and pharmaceutical companies, and hospitals and clinics.

Another key driver for market growth is the expanding application of single-cell analysis in precision medicine and drug discovery. The ability to analyze gene expression, protein levels, and epigenetic modifications at the single-cell level has revolutionized the understanding of disease pathogenesis and therapeutic response. This has led to a surge in demand for specialized software capable of managing complex, multi-omics datasets and generating actionable insights for personalized treatment strategies. Furthermore, the ongoing trend of integrating artificial intelligence and machine learning in single-cell data analysis is enabling more accurate predictions and faster data processing, thus accelerating the pace of biomedical research and clinical diagnostics.

The increasing collaboration between academia, industry, and government agencies is also contributing to market expansion. Public and private investments in single-cell genomics research are fostering innovation in data analysis software, while strategic partnerships and acquisitions are facilitating the development of comprehensive, end-to-end solutions. Additionally, the growing awareness of the potential of single-cell analysis in oncology, immunology, and regenerative medicine is encouraging the adoption of advanced software platforms worldwide. However, challenges such as data privacy concerns, high implementation costs, and the need for skilled personnel may pose restraints to market growth, particularly in low-resource settings.

From a regional perspective, North America continues to dominate the Single-Cell Data Analysis Software market, owing to its well-established healthcare infrastructure, strong presence of leading biotechnology and pharmaceutical companies, and substantial investments in genomics research. Europe follows closely, supported by robust government funding and a thriving life sciences sector. The Asia Pacific region is emerging as a lucrative market, driven by rising healthcare expenditure, expanding research capabilities, and increasing adoption of advanced technologies in countries such as China, Japan, and India. Latin America and the Middle East & Africa are also witnessing gradual growth, albeit at a slower pace, due to improving healthcare infrastructure and growing awareness of single-cell analysis applications.

Component Analysis

The Single-Cell Data Analysis Software market by component is broadly segmented into software and services, each playing a pivotal role in the overall ecosystem. Software solutions form the backbone of this market, offering a wide array of functionalities such as data preprocessing, quality control, clustering, visualization, and integration of multi-omics data. The increasing complexity and volume of single-cell datasets have driven the development of sophisticated software platforms equipped with advanced analytics, machine learning algorithms, and intuitive user interfaces. These platfo

The size of the Marketing Data Analysis Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The DART team is responsible for fulfilling ad hoc data requests that come in to the Analysis Division, FMCSA. The DART system tracks these requests, stores any coding and results, and performs internal reporting about requests received.

Raw data and electrophysiology recordings

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

List of Top Journals of Journal of Data Analysis and Information Processing sorted by citations.

Problem Statement-

Bike-sharing systems are meant to rent bicycles and return to different places for bike-sharing purposes in Washington DC.

You are provided with rental data spanning 2 years. It would help if you predicted the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period.

This is the bike rental dataset, to practice pandas profiling. This dataset contains numerical values.

Tasks to perform : 1. Perform Exploratory Data Analysis 2. Use Pandas Profiling

Compare the pandas profiling report with Exploratory Data Analysis

The genomic data analysis service market is booming, projected to reach $1769.8 million in 2025 with a 13.1% CAGR. Discover key trends, drivers, and regional insights in this comprehensive market analysis covering NGS, whole genome sequencing, personalized medicine, and leading companies like Illumina and QIAGEN.