Dataset

This dataset was created by Nafe Muhtasim

Released under CC0: Public Domain

Contents

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.

Copies of Anaconda 3 Jupyter Notebooks and Python script for holistic and clustered analysis of "The Impact of COVID-19 on Technical Services Units" survey results. Data was analyzed holistically using cleaned and standardized survey results and by library type clusters. To streamline data analysis in certain locations, an off-shoot CSV file was created so data could be standardized without compromising the integrity of the parent clean file. Three Jupyter Notebooks/Python scripts are available in relation to this project: COVID_Impact_TechnicalServices_HolisticAnalysis (a holistic analysis of all survey data) and COVID_Impact_TechnicalServices_LibraryTypeAnalysis (a clustered analysis of impact by library type, clustered files available as part of the Dataverse for this project).

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.

The data analytic market size is projected to grow from USD 69.40 billion in the current year to USD 877.12 billion by 2035, representing a CAGR of 25.93%, during the forecast period till 2035.

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.

R code for running GLMM and BRT analysis

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.

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...

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. "

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.

This study aimed to explore the views of healthcare professionals regarding the barriers and facilitators for a Fracture Liaison Service (FLS) in Malaysia. The qualitative study was conducted from February to December 2021 at a tertiary hospital in Malaysia. Doctors, nurses, pharmacists, and policymakers were recruited via purposive sampling. Semi-structured in-depth interviews were conducted until thematic saturation was achieved. Data were transcribed verbatim and analysed using thematic analysis. Thirty participants [doctors (n = 13), nurses (n = 8), pharmacists (n = 8), and policymakers (n = 1)] with 2–28 years of working experience were recruited. Three themes emerged: 1) Current delivery of secondary fracture prevention; 2) Importance of secondary fracture prevention, and 3) FLS sustainability. Some participants reported that the current post-hip fracture care was adequate, whilst some expressed concerns about the lack of coordination and continuity of care, especially in non-hip fragility fracture care. Most participants recognised the importance of secondary fracture prevention as fracture begets fracture, highlighting the need for a FLS to address this care gap. However, some were concerned about competing priorities. To ensure the sustainability of a FLS, cost-effectiveness data, support from relevant stakeholders, increased FLS awareness among patients and healthcare professionals, and a FLS coordinator were required. Training and financial incentives may help address the issue of low confidence and encourage the nurses to take on the FLS coordinator role. Overall, all participants believed that there was a need for a FLS to improve the delivery of secondary fracture prevention. Addressing concerns such as lack of confidence among nurses and lack of awareness can help improve FLS sustainability.

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 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.

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.

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.

Global Data Analysis Software market size 2021 was recorded $47.176 Billion whereas by the end of 2025 it will reach $72.3 Billion. According to the author, by 2033 Data Analysis Software market size will become $169.815. Data Analysis Software market will be growing at a CAGR of 11.264% during 2025 to 2033.

Chemical concentration, exposure, and health risk data for U.S. census tracts from National Scale Air Toxics Assessment (NATA). This dataset is associated with the following publication: Huang, H., R. Tornero-Velez, and T. Barzyk. Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 27(6): 544-550, (2017).

Japan Data Center Power Market is Segmented by Component (Electrical Solutions, Services), Data Center Type (Hyperscaler/Cloud Service Providers, Colocation Providers, and More), Data Center Size (Small Size Data Centers, Medium Size Data Centers, Large Size Data Centers and More), Tier Type (Tier I and II, Tier III, Tier IV). The Market Forecasts are Provided in Terms of Value (USD).