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.

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...

Brazil data analytics market is expected to grow at a substantial rate during the forecast period 2026.

The Supply Chain Big Data Analytics Market Report is Segmented by Component (Solution, Service), End User Industry (Retail, Transportation and Logistics, Manufacturing, Healthcare, Other End-User Industries), Deployment Model (On-Premise, Cloud), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Dataset

This dataset was created by PriyankaJ7

Contents

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

Contains view count data for the top 20 pages each day on the Somerville MA city website dating back to 2020. Data is used in the City's dashboard which can be found at https://www.somervilledata.farm/.

Construction Data Analytics Market Outlook

According to our latest research, the global Construction Data Analytics market size reached USD 4.12 billion in 2024, with a robust growth trajectory fueled by the accelerating adoption of digital solutions across the construction sector. The market is expected to expand at a CAGR of 15.7% from 2025 to 2033, driving the market value to an estimated USD 14.33 billion by 2033. This significant growth is underpinned by the increasing demand for data-driven decision-making, enhanced project management efficiencies, and the necessity to mitigate risks in complex construction environments.

One of the primary growth factors in the Construction Data Analytics market is the rapid digital transformation underway in the construction industry. As construction projects become more complex and the volume of data generated onsite increases, there is a growing need for sophisticated analytics platforms capable of aggregating, processing, and interpreting this information. Companies are leveraging data analytics to optimize resource allocation, streamline workflows, and improve overall project outcomes. The integration of analytics with Building Information Modeling (BIM), Internet of Things (IoT) sensors, and mobile technologies is empowering stakeholders to make real-time, informed decisions, thereby reducing delays and cost overruns. Furthermore, the global push towards smart cities and sustainable infrastructure is further propelling the adoption of advanced analytics tools within the construction sector.

Another critical driver for the Construction Data Analytics market is the heightened focus on risk management and safety compliance. Construction remains one of the most hazardous industries, and the ability to proactively identify and address risks is paramount. Data analytics platforms are being deployed to analyze historical safety records, monitor real-time site conditions, and predict potential hazards before they escalate. This data-driven approach not only enhances worker safety but also ensures regulatory compliance and minimizes insurance liabilities for construction firms. As governments and regulatory bodies impose stricter safety mandates, the demand for robust analytics solutions is expected to surge, further bolstering market growth.

Additionally, the increasing pressure on construction companies to deliver projects on time and within budget is catalyzing the adoption of data analytics. Delays and cost overruns are perennial challenges in the industry, often stemming from poor project management, supply chain disruptions, and unforeseen risks. Advanced analytics platforms enable stakeholders to gain granular visibility into project schedules, resource utilization, and supply chain performance. By harnessing predictive analytics and machine learning, companies can anticipate potential bottlenecks, optimize procurement strategies, and ensure timely project delivery. This growing emphasis on operational efficiency and transparency is a key factor driving the expansion of the Construction Data Analytics market globally.

From a regional perspective, North America continues to command the largest share of the Construction Data Analytics market in 2024, accounting for approximately 38% of the global market value. This dominance is attributed to the early adoption of digital technologies, a strong presence of leading analytics vendors, and significant investments in infrastructure modernization. Meanwhile, the Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 18.2% through 2033, driven by rapid urbanization, government-led smart city initiatives, and increasing digitization in emerging economies such as China and India. Europe also demonstrates steady growth, supported by stringent regulatory requirements and a strong focus on sustainable construction practices.

Component Analysis

The Component segment of the Construction Data Analytics market is bifurcated i

The global data science and predictive analytics market size was more than USD 25.24 billion in 2025 and is anticipated to grow at a CAGR of over 18.8%, reaching USD 141.34 billion revenue by 2035, driven by AI and IoT technology adoption.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Monthly analytics reports for the Brisbane City Council website

Information regarding the sessions for Brisbane City Council website during the month including search terms used.

Big Data Analytics in BFSI Market Outlook

As per our latest research, the global Big Data Analytics in BFSI market size reached USD 22.7 billion in 2024, driven by the increasing digital transformation initiatives and the accelerating adoption of advanced analytics across financial institutions. The market is expected to grow at a robust CAGR of 14.8% during the forecast period, reaching an estimated USD 62.5 billion by 2033. The rapid proliferation of digital banking, heightened focus on fraud detection, and the need for personalized customer experiences are among the primary growth drivers for the Big Data Analytics in BFSI market.

The exponential growth of data generated by financial transactions, customer interactions, and regulatory requirements has created an urgent need for advanced analytics solutions in the BFSI sector. Financial institutions are leveraging Big Data Analytics to gain actionable insights, optimize operations, and enhance decision-making processes. The integration of artificial intelligence and machine learning with Big Data Analytics platforms is enabling BFSI organizations to automate risk assessment, predict customer behavior, and streamline compliance procedures. Furthermore, the surge in digital payment platforms and online banking services has resulted in an unprecedented volume of structured and unstructured data, further necessitating robust analytics solutions to ensure data-driven strategies and operational efficiency.

Another significant growth factor is the increasing threat of cyberattacks and financial fraud. As digital channels become more prevalent, BFSI organizations face sophisticated threats that require advanced analytics for real-time detection and mitigation. Big Data Analytics empowers financial institutions to monitor vast datasets, identify unusual patterns, and respond proactively to potential security breaches. Additionally, regulatory bodies are imposing stringent data management and compliance standards, compelling BFSI firms to adopt analytics solutions that ensure transparency, auditability, and adherence to global regulations. This regulatory push, combined with the competitive need to offer innovative, customer-centric services, is fueling sustained investment in Big Data Analytics across the BFSI landscape.

The growing emphasis on customer-centricity is also propelling the adoption of Big Data Analytics in the BFSI sector. Financial institutions are increasingly utilizing analytics to understand customer preferences, segment markets, and personalize product offerings. This not only enhances customer satisfaction and loyalty but also drives cross-selling and upselling opportunities. The ability to analyze diverse data sources, including social media, transaction histories, and customer feedback, allows BFSI organizations to predict customer needs and deliver targeted solutions. As a result, Big Data Analytics is becoming an indispensable tool for BFSI enterprises aiming to differentiate themselves in an intensely competitive market.

From a regional perspective, North America remains the largest market for Big Data Analytics in BFSI, accounting for over 38% of global revenue in 2024. This dominance is attributed to the presence of major financial institutions, early adoption of advanced technologies, and a mature regulatory environment. However, the Asia Pacific region is witnessing the fastest growth, with a CAGR exceeding 17% during the forecast period, driven by rapid digitization, expanding banking infrastructure, and increasing investments in analytics solutions by emerging economies such as China and India.

Component Analysis

The Big Data Analytics in BFSI market is segmented by component into Software and Services. The software segment comprises analytics platforms, data management tools, visualization software, and advanced AI-powered solutions. In 2024, the software segment accounted for the largest share

Rocket Engine Test Data Analytics Market Outlook

According to our latest research, the global rocket engine test data analytics market size in 2024 stands at USD 1.42 billion. The market is experiencing robust expansion, with a compounded annual growth rate (CAGR) of 12.8% from 2025 to 2033. By 2033, the market is forecasted to reach a value of USD 4.19 billion. This growth is primarily fueled by the increasing demand for advanced data analytics to enhance the reliability, safety, and performance of rocket engines, as well as the rising frequency of space missions and test launches across both governmental and commercial sectors.

One of the key factors propelling the growth of the rocket engine test data analytics market is the rapid technological advancement in data acquisition and processing systems. Modern rocket engine tests generate colossal volumes of data, encompassing parameters such as thrust, temperature, vibration, and fuel flow. The integration of sophisticated analytics platforms enables stakeholders to derive actionable insights from this data, facilitating real-time monitoring, anomaly detection, and root-cause analysis. This technological leap not only shortens development cycles but also significantly reduces the risk of catastrophic failures, making it indispensable for organizations aiming to maintain a competitive edge in the aerospace and defense sector.

Another significant growth driver is the escalating investment in space exploration and commercial spaceflight activities. Both government agencies like NASA and ESA, as well as private players such as SpaceX and Blue Origin, are conducting more frequent and complex test campaigns. These organizations increasingly rely on data analytics to validate engine designs, optimize test procedures, and ensure compliance with stringent safety standards. The advent of reusable rocket technology further amplifies the need for predictive maintenance and performance analytics, as understanding wear and tear across multiple launches becomes critical to mission success and cost efficiency.

The convergence of artificial intelligence (AI) and machine learning (ML) with rocket engine test data analytics is also catalyzing market expansion. Advanced algorithms are now capable of identifying subtle patterns and correlations within vast datasets, enabling predictive maintenance and early fault detection with unprecedented accuracy. This capability is particularly valuable for commercial space companies and research institutes seeking to maximize engine uptime and minimize unplanned downtimes. Moreover, the growing adoption of cloud-based analytics platforms is democratizing access to high-performance computing resources, allowing smaller organizations and emerging space nations to participate in the market and drive further innovation.

From a regional perspective, North America continues to dominate the rocket engine test data analytics market, accounting for over 43% of the global revenue in 2024. This leadership is attributed to the presence of major aerospace companies, robust government funding, and a vibrant ecosystem of technology providers. However, Asia Pacific is emerging as the fastest-growing region, with countries like China and India ramping up their space programs and investing heavily in indigenous rocket engine development and testing infrastructure. Europe also remains a significant market, driven by collaborative initiatives and strong research capabilities. The Middle East & Africa and Latin America, while still nascent, are expected to witness steady growth as regional space ambitions intensify.

Component Analysis

The component segment of the rocket engine test data analytics market is categorized into software, hardware, and services. The software component is witnessing the highest growth, driven by the increasing demand for advanced analytics platforms capable of handling large-scale, high-velocity data streams generated during engine tests. These so

DATA ANALYTICS 2

## Overview

DATA ANALYTICS 2 is a dataset for object detection tasks - it contains TRAFFIC LIGHTS Gztl annotations for 8,579 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Big Data Analytics in Banking Market is Segmented by Type of Solutions (Data Discovery and Visualization (DDV) and Advanced Analytics (AA)), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD Million) for all the Above Segments.

Snapshot img

Generative AI In Data Analytics Market Size 2025-2029

The generative ai in data analytics market size is valued to increase by USD 4.62 billion, at a CAGR of 35.5% from 2024 to 2029. Democratization of data analytics and increased accessibility will drive the generative ai in data analytics market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Deployment - Cloud-based segment was valued at USD 510.60 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 621.84 million 
Market Future Opportunities 2024: USD 4624.00 million
CAGR from 2024 to 2029 : 35.5%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to unlock new insights from their data through advanced technologies. This trend is driven by the democratization of data analytics and increased accessibility of AI models, which are now available in domain-specific and enterprise-tuned versions. Generative AI, a subset of artificial intelligence, uses deep learning algorithms to create new data based on existing data sets. This capability is particularly valuable in data analytics, where it can be used to generate predictions, recommendations, and even new data points. One real-world business scenario where generative AI is making a significant impact is in supply chain optimization. In this context, generative AI models can analyze historical data and generate forecasts for demand, inventory levels, and production schedules. This enables businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. However, the adoption of generative AI in data analytics also presents challenges, particularly around data privacy, security, and governance. As businesses continue to generate and analyze increasingly large volumes of data, ensuring that it is protected and used in compliance with regulations is paramount. Despite these challenges, the benefits of generative AI in data analytics are clear, and its use is set to grow as businesses seek to gain a competitive edge through data-driven insights.

What will be the size of the Generative AI In Data Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleGenerative AI, a subset of artificial intelligence, is revolutionizing data analytics by automating data processing and analysis, enabling businesses to derive valuable insights faster and more accurately. Synthetic data generation, a key application of generative AI, allows for the creation of large, realistic datasets, addressing the challenge of insufficient data in analytics. Parallel processing methods and high-performance computing power the rapid analysis of vast datasets. Automated machine learning and hyperparameter optimization streamline model development, while model monitoring systems ensure continuous model performance. Real-time data processing and scalable data solutions facilitate data-driven decision-making, enabling businesses to respond swiftly to market trends. One significant trend in the market is the integration of AI-powered insights into business operations. For instance, probabilistic graphical models and backpropagation techniques are used to predict customer churn and optimize marketing strategies. Ensemble learning methods and transfer learning techniques enhance predictive analytics, leading to improved customer segmentation and targeted marketing. According to recent studies, businesses have achieved a 30% reduction in processing time and a 25% increase in predictive accuracy by implementing generative AI in their data analytics processes. This translates to substantial cost savings and improved operational efficiency. By embracing this technology, businesses can gain a competitive edge, making informed decisions with greater accuracy and agility.

Unpacking the Generative AI In Data Analytics Market Landscape

In the dynamic realm of data analytics, Generative AI algorithms have emerged as a game-changer, revolutionizing data processing and insights generation. Compared to traditional data mining techniques, Generative AI models can create new data points that mirror the original dataset, enabling more comprehensive data exploration and analysis (Source: Gartner). This innovation leads to a 30% increase in identified patterns and trends, resulting in improved ROI and enhanced business decision-making (IDC).

Data security protocols are paramount in this context, with Classification Algorithms and Clustering Algorithms ensuring data privacy and compliance alignment. Machine Learning Pipelines and Deep Learning Frameworks facilitate seamless integration with Predictive Modeling Tools and Automated Report Generation on Cloud

The dataset is designed to facilitate the development and evaluation of machine learning models for managing and distributing traditional cultural and educational resources, leveraging IoT data and deep learning techniques. The goal is to provide a rich set of features for exploring user interactions with cultural artifacts and historical sites, with the support of IoT-enabled sensors and virtual learning experiences.

Columns Overview: User_ID: A unique identifier for each user interacting with the system. This helps track individual behavior patterns and interactions.

Artifact_ID: The unique identifier for each artifact (e.g., a cultural artifact or historical item) that is part of the learning experience.

Artifact_Name: The name of the artifact. This could range from physical objects like sculptures, to digital representations of ancient items or sites.

Artifact_Type: A categorical field indicating the type of artifact, such as sculpture, musical instrument, religious artifact, etc.

Artifact_Location: The physical or virtual location of the artifact. This could be a museum, historical site, or an online platform.

Artifact_Historical_Significance: A textual description of the cultural and historical importance of the artifact. This can be used to provide more context for the artifact’s significance.

Digital_Representation_URL: A URL pointing to the digital representation of the artifact, which could be an image, video, or audio file related to the artifact.

Artifact_Tags: Tags that describe the artifact in terms of categories like "Ancient", "Religious", "Musical", etc. These can be used for classification or recommendations.

Site_ID: The unique identifier for each site (e.g., the location where a cultural artifact is located or a historical site).

Site_Name: The name of the site, such as "Great Wall of China" or "Colosseum".

Site_Location: The geographical coordinates (latitude and longitude) of the site.

Site_Cultural_Importance: A description of the cultural importance of the site.

Virtual_Tour_URL: A URL linking to a virtual tour of the site or artifact, offering an interactive learning experience.

IoT_Sensor_ID: The identifier of the IoT sensor collecting data from the artifact or site. These sensors track various environmental factors such as temperature, humidity, and motion.

IoT_Sensor_Type: The type of sensor used to collect data, such as "Temperature", "Humidity", "Motion", or "Light".

IoT_Sensor_Location: The location of the IoT sensor in relation to the artifact or site.

Sensor_Timestamp: The date and time when the sensor data was recorded.

Sensor_Reading: The actual value recorded by the IoT sensor. This could represent temperature, humidity, motion detection, or other environmental factors.

User_Interaction_Type: The type of interaction the user has with the artifact or site. This can include actions like "View", "Rate", or "Click".

User_Interaction_Timestamp: The timestamp of when the user interaction took place. This allows us to track the time of day and the frequency of interactions.

Interaction_Duration: The duration of the user’s interaction with the artifact or site, measured in minutes. This helps in analyzing user engagement levels.

User_Feedback: The feedback provided by the user after interacting with the artifact or site, such as "Liked", "Not Interested", or "Neutral".

Target_Column: The target variable for machine learning models. This column indicates whether the user had a positive interaction (1) or a negative interaction (0). A positive interaction is defined as a "view" action, while negative interactions include other actions like "rate" or "click".

Data analytics by size class of enterprise

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

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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.
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• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

The Data Analytics in Insurance market is experiencing robust growth, projected to reach $12.01 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.2% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of insurance data, coupled with the need for enhanced risk assessment and fraud detection, are driving the adoption of advanced analytics solutions. Furthermore, regulatory compliance requirements and the demand for personalized customer experiences are compelling insurers to leverage data analytics for improved operational efficiency and customer retention. The market's segmentation reflects this complexity, encompassing solutions for underwriting, claims processing, risk management, and customer relationship management (CRM). Leading players like Deloitte, Verisk Analytics, IBM, and SAP AG are at the forefront, offering comprehensive solutions tailored to the specific needs of insurance companies. The competitive landscape is characterized by both established players and emerging technology providers, fostering innovation and driving down costs. This growth trajectory is further supported by emerging trends like the increasing use of artificial intelligence (AI), machine learning (ML), and big data technologies to analyze vast datasets and extract meaningful insights. The integration of these technologies is enabling insurers to develop more accurate predictive models, automate processes, and enhance their decision-making capabilities. Despite the growth potential, the market faces challenges such as data security concerns, the need for skilled professionals, and the high initial investment required for implementing advanced analytics solutions. However, the long-term benefits of improved efficiency, reduced costs, and enhanced customer satisfaction are expected to outweigh these challenges, sustaining the market's upward trend throughout the forecast period.

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).