I took the limited data set for this project that I cleaned, organized, and transformed the data into a presentation for the fictitious company. This was to showcase my ability to analyze, clean, & visualize datasets to find insights and present them in a way that could represent a real life scenario.

This case study was the capstone project at the end of my Online course from Google in Data Analytics. This comes from a relatively large but limited data spreadsheet from a fictitious bicycle share service. In the scenario you were required to answer the first of three questions posed to the data analyst with a limited data set: “How do annual members differ from casual users?” This was to help the marketing lead build a new campaign to sign up casual users of the service to annual members based on findings from the financial analysts.

Public speaking is an important skill, the acquisition of which requires dedicated and time consuming training. In recent years, researchers have started to investigate automatic methods to support public speaking skills training. These methods include assessment of the trainee's oral presentation delivery skills which may be accomplished through automatic understanding and processing of social and behavioral cues displayed by the presenter. In this study, we propose an automatic scoring system for presentation delivery skills using a novel active data representation method to automatically rate segments of a full video presentation. While most approaches have employed a two step strategy consisting of detecting multiple events followed by classification, which involve the annotation of data for building the different event detectors and generating a data representation based on their output for classification, our method does not require event detectors. The proposed data representation is generated unsupervised using low-level audiovisual descriptors and self-organizing mapping and used for video classification. This representation is also used to analyse video segments within a full video presentation in terms of several characteristics of the presenter's performance. The audio representation provides the best prediction results for self-confidence and enthusiasm, posture and body language, structure and connection of ideas, and overall presentation delivery. The video data representation provides the best results for presentation of relevant information with good pronunciation, usage of language according to audience, and maintenance of adequate voice volume for the audience. The fusion of audio and video data provides the best results for eye contact. Applications of the method to provision of feedback to teachers and trainees are discussed.

This resource contains presentations given by CUASHI staff at a variety of venues in 2020 and 2021.

1. Bales, J.D. 2020. CUAHSI Membership Meeting, December 1, 2020.

2. Bales, J.D. 2020. 2020-05 CUAHSI Online Education Resources: This presentation was given to the UNESCO IHP on CUAHSI's resources that can help facilitate and enable online education.

3. Bales, J.D. 2020. 2020-05 ESIP Technology Webinar-Bales, Horsburgh, and Castronova: CUAHSI Community and Water Data Services: This presentation was given at an ESIP (Earth Science Information Program) with an emphasis on CUAHSI's Hydrologic Informatrion System, HydroShare, and CUAHSI Compute Resources.

4. Bales, J.D. 2020. What can you do with CUAHSI HydroShare? Presentation to UNESCO International Hydrological Programme meeting on southern Africa water issues. June 5, 2020.

5. Bales, J.D. 2020. CUAHSI Community and Water Data Services. Presentation to Soil Moisture Working Group. September 22, 2020.

6. Bales, J.D. 2020. The Critical Zone Collaborative Network Hub: Enabling, Supporting, and Communicating CZ Science. Early Career Critical Zone Working Group webinar, October 30, 2020.

7. Bales, J.D. 2020. CUAHSI Services for the Water-Education Community. NAGT – CUAHSI Webinar, November 5, 2020.

8. Bales, J.D. 2020. Agency Update: Supporting Federal Water Programs. Interagency Conference on Research in the Watershed, November 17, 2020.

9. Bales, J.D. 2021. CUAHSI: Reproducible Science, Education, Community. NASA SWOT Early Adopters Workshop, March 8, 2021. This presentation describes some of CUAHSI's capabilities for data archival and reproducible science. The presentation links to a SWOT app hosted by HydroShare for creation of synthetic SWOT discharges.

10. Bales, J.D. 2021. Global Water Futures’ Core Modelling and Forecasting Team Webinar--Collaborative and Reproducible Hydrologic Modeling.

11. Bales, J.D. 2022. Global Water Futures 2022 Annual Open Science Meeting: The Value of Open and Reproducible Data and Workflows in Water Prediction.

Context

I am new to the data science community and I am wanting to improve the skills I have obtained thus far from education and the Google Data Analytics Certificate. My goal is to gain experience by doing case studies and dabbling in topics that interest me. I am very new to this field, so by no means is this a perfect analysis or presentation, I just wanted to have fun with the skills I have and gain some experience, working with a topic I love!

Content

As this is part of my portfolio, there are a few pieces that are inside that are more than data. There is the data I cleaned from the dataset available through BigQuery, along with a documentation of the process and a slide show presentation.

Acknowledgements

Data accessed through BigQuery: big query-public-data.blackhole_database.sdss_dr7

Inspiration

I really wanted to work with a topic that interests me and that I have some background knowledge in. With a minor in astronomy, this dataset seemed like a way for me to explore the data along with my skills in a leveled, intermediate sense.

Snapshot img

AI Speech To Text Tool Market Size 2025-2029

The AI speech to text tool market size is valued to increase by USD 8.29 billion, at a CAGR of 28.8% from 2024 to 2029. Escalating enterprise demand for unstructured data analytics and operational efficiency will drive the ai speech to text tool market.

Major Market Trends & Insights

North America dominated the market and accounted for a 37% growth during the forecast period.
By Type - ASR segment was valued at USD 325.90 billion in 2023
By Content Type - Online courses segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 8293.90 million
CAGR from 2024 to 2029 : 28.8%

Market Summary

Amidst the escalating enterprise demand for unleashing insights from vast repositories of unstructured data, AI speech-to-text tools have emerged as indispensable solutions. These tools facilitate real-time transcription and analysis of spoken language, fueling operational efficiency and productivity. The market for these technologies is experiencing significant growth, with the integration of low-latency, real-time streaming Automatic Speech Recognition (ASR) gaining dominance in interactive applications. However, persistent accuracy and reliability issues in diverse acoustic and linguistic environments pose challenges. According to recent estimates, the global speech recognition market is projected to reach USD25.1 billion by 2027, underscoring its growing importance in the business landscape.
Despite these challenges, advancements in natural language processing, machine learning, and deep learning continue to drive innovation, ensuring these tools remain at the forefront of data analytics and communication technologies.

What will be the Size of the AI Speech To Text Tool Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the AI Speech To Text Tool Market Segmented ?

The AI speech to text tool 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.

Type

  ASR
  Real-time transcription systems
  Voice recognition systems
  Captioning systems
  Others


Content Type

  Online courses
  Meetings
  Podcasts
  Films


End-user

  BFSI
  Healthcare
  IT and telecom
  Education
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The ASR segment is estimated to witness significant growth during the forecast period.

Automatic Speech Recognition (ASR) technology continues to evolve, with a primary focus on enhancing transcription accuracy. Measured by Word Error Rate (WER), recent advancements have significantly reduced errors across various languages, dialects, and acoustic conditions. This progress can be attributed to the widespread adoption of large-scale transformer models. For instance, the OpenAI Whisper model, initially released open source, was refined and commercialized as an API in 2023, offering developers a robust, multilingual ASR solution. This system's improvements include data augmentation methods, intent recognition, natural language processing, and sentence error rate reduction through machine learning algorithms, language model adaptation, and neural network training.

Additionally, it features voice activity detection, grammar induction, semantic parsing, and language identification models. The API also supports offline transcription services, on-device processing, and real-time transcription with low latency. Its advanced acoustic modeling techniques, feature extraction methods, and speaker diarization methods contribute to superior speech recognition accuracy and noise reduction. With a WER of below 5%, this AI Speech-to-Text tool sets a new industry benchmark.

Request Free Sample

The ASR segment was valued at USD 325.90 billion in 2019 and showed a gradual increase during the forecast period.

Request Free Sample

Regional Analysis

North America is estimated to contribute 37% 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.

See How AI Speech To Text Tool Market Demand is Rising in North America Request Free Sample

The market exhibits a robust and dynamic nature, with North America leading the charge. Comprising the United States and Canada, this region houses the most mature and dominant market, driven by a high concentration of technology corporations, a thriving startup ecosy

Call Center Audio Recordings Dataset: 100,000+ Hours of Customer-Agent Conversations in Multiple Languages

FileMarket AI Data Labs presents an extensive call center audio recordings dataset with over 100,000 hours of customer-agent conversations across a diverse range of topics. This dataset is designed to support AI, machine learning, and speech analytics projects requiring high-quality, real-world customer interaction data. Whether you're working on speech recognition, natural language processing (NLP), sentiment analysis, or any other conversational AI task, our dataset offers the breadth and quality you need to build, train, and refine cutting-edge models.

Our dataset includes a multilingual collection of customer service interactions, recorded across various industries and service sectors. These recordings cover different call center topics such as customer support, sales and telemarketing, technical helpdesk, complaint handling, and information services, ensuring that the dataset provides rich context and variety. With support for a broad spectrum of languages including English, Spanish, French, German, Chinese, Arabic, and more, this dataset allows for training models that cater to global customer bases.

In addition to the audio recordings, our dataset includes detailed metadata such as call duration, region, language, and call type, ensuring that data is easily usable for targeted applications. All recordings are carefully annotated for speaker separation and high fidelity to meet the highest standards for audio data.

Our dataset is fully compliant with data protection and privacy regulations, offering consented and ethically sourced data. You can be assured that every data point meets the highest standards for legal compliance, making it safe for use in your commercial, academic, or research projects.

At FileMarket AI Data Labs, we offer flexibility in terms of data scaling. Whether you need a small sample or a full-scale dataset, we can cater to your requirements. We also provide sample data for evaluation to help you assess quality before committing to the full dataset. Our pricing structure is competitive, with custom pricing options available for large-scale acquisitions.

We invite you to explore this versatile dataset, which can help accelerate your AI and machine learning initiatives, whether for training conversational models, improving customer service tools, or enhancing multi-language support systems.

According to Cognitive Market Research, the global Contact Center Analytics Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Key Drivers of

Contact Center Analytics Software

Predictive Analytics and Real-Time Monitoring Propel Contact Center Analytics Growth

The growing demand for predictive analytics and real-time monitoring is expanding the contact center analytics market. Predictive analytics involves leveraging historical data and statistical algorithms to predict future outcomes, while real-time monitoring allows businesses to track and respond to customer interactions instantaneously. Together, these technologies help contact centers identify opportunities for improvement, optimize operations, and enhance customer satisfaction. For instance, predictive analytics can help anticipate customer needs and offer personalized solutions, leading to better customer retention rates. A report from VentureBeat in October 2022 noted that nearly 95% of businesses have incorporated AI-powered predictive analytics into their marketing strategies. (https://venturebeat.com/business/report-95-of-businesses-have-a-customer-success-function/) The ability to predict customer behavior, identify issues before they arise, and tailor strategies accordingly is driving the increased adoption of these technologies in contact centers. Real-time monitoring enables immediate response to customer issues, ensuring timely resolutions and a higher level of service. As businesses continue to realize the power of these tools, the demand for contact center analytics solutions is poised to increase, propelling market growth. AI Revolutionizes Contact Center Analytics Driving Market Expansion AI is revolutionizing how contact centers operate by automating data analysis, providing real-time insights, and enhancing customer interactions. Through AI-powered solutions like natural language processing (NLP) and machine learning, contact centers can understand customer sentiments, automate responses, and make data-driven decisions more efficiently. AI also enables predictive analytics, helping contact centers anticipate customer needs and improve engagement strategies. For instance, in May 2022, IBM reported that 35% of businesses globally were using AI, marking a 4% increase from the previous year. (https://newsroom.ibm.com/2022-05-19-Global-Data-from-IBM-Shows-Steady-AI-Adoption-as-Organizations-Look-to-Address-Skills-Shortages,-Automate-Processes-and-Encourage-Sustainable-Operations#:~:text=Global%20AI%20adoption%20is%20growing%20steadily%2C%20and%20most,42%25%20of%20companies%20report%20they%20are%20exploring%20AI.) Companies like Amazon and Microsoft are incorporating AI-driven solutions to improve their contact center operations, reducing wait times and providing personalized customer experiences. The adoption of AI in contact centers not only enhances customer satisfaction but also drives operational efficiency, allowing businesses to scale their operations while maintaining high service standards. As AI technology continues to evolve, its role in contact center analytics will only become more integral, further driving market expansion. In conclusion, predictive analytics, real-time monitoring, and AI are pivotal drivers of growth in the contact center analytics market. These technologies enable businesses to optimize customer interactions, streamline operations, and improve overall efficiency. With the increasing adoption of AI and the rising demand for advanced data-driven insights, the cont...

Collected in this dataset are the slideset and abstract for a presentation on Toward a Reproducible Research Data Repository by the depositar team at International Symposium on Data Science 2023 (DSWS 2023), hosted by the Science Council of Japan in Tokyo on December 13-15, 2023. The conference was organized by the Joint Support-Center for Data Science Research (DS), Research Organization of Information and Systems (ROIS) and the Committee of International Collaborations on Data Science, Science Council of Japan. The conference programme is also included as a reference.

Title

Toward a Reproducible Research Data Repository

Author(s)

Cheng-Jen Lee, Chia-Hsun Ally Wang, Ming-Syuan Ho, and Tyng-Ruey Chuang

Affiliation of presenter

Institute of Information Science, Academia Sinica, Taiwan

Summary of Abstract

The depositar (https://data.depositar.io/) is a research data repository at Academia Sinica (Taiwan) open to researhers worldwide for the deposit, discovery, and reuse of datasets. The depositar software itself is open source and builds on top of CKAN. CKAN, an open source project initiated by the Open Knowledge Foundation and sustained by an active user community, is a leading data management system for building data hubs and portals. In addition to CKAN's out-of-the-box features such as JSON data API and in-browser preview of uploaded data, we have added several features to the depositar, including sourcing from Wikidata as dataset keywords, a citation snippet for datasets, in-browser Shapefile preview, and a persistent identifier system based on ARK (Archival Resource Keys). At the same time, the depositar team faces an increasing demand for interactive computing (e.g. Jupyter Notebook) which facilitates not just data analysis, but also for the replication and demonstration of scientific studies. Recently, we have provided a JupyterHub service (a multi-tenancy JupyterLab) to some of the depositar's users. However, it still requires users to first download the data files (or copy the URLs of the files) from the depositar, then upload the data files (or paste the URLs) to the Jupyter notebooks for analysis. Furthermore, a JupyterHub deployed on a single server is limited by its processing power which may lower the service level to the users. To address the above issues, we are integrating the BinderHub into the depositar. BinderHub (https://binderhub.readthedocs.io/) is a kubernetes-based service that allows users to create interactive computing environments from code repositories. Once the integration is completed, users will be able to launch Jupyter Notebooks to perform data analysis and vsualization without leaving the depositar by clicking the BinderHub buttons on the datasets. In this presentation, we will first make a brief introduction to the depositar and BinderHub along with their relationship, then we will share our experiences in incorporating interactive computation in a data repository. We shall also evaluate the possibility of integrating the depositar with other automation frameworks (e.g. the Snakemake workflow management system) in order to enable users to reproduce data analysis.

Keywords

BinderHub, CKAN, Data Repositories, Interactive Computing, Reproducible Research

Retail Execution Monitoring AI Market Outlook

According to our latest research, the global Retail Execution Monitoring AI market size stood at USD 1.46 billion in 2024, and is projected to reach USD 9.87 billion by 2033, growing at an impressive CAGR of 23.8% during the forecast period. The market’s robust growth is primarily driven by the increasing demand for real-time data analytics, automation of retail processes, and the rising need for enhanced in-store execution to improve customer experience and operational efficiency. As per the latest research, retailers worldwide are rapidly adopting AI-powered monitoring solutions to streamline store operations, optimize shelf management, and gain actionable insights for better decision-making in an increasingly competitive retail landscape.

One of the key growth factors propelling the Retail Execution Monitoring AI market is the growing emphasis on data-driven decision-making in retail operations. As retailers face mounting pressures to enhance customer satisfaction and minimize operational inefficiencies, AI-powered retail execution monitoring tools have emerged as a vital enabler. These solutions leverage advanced machine learning algorithms and computer vision to provide real-time insights into shelf conditions, product availability, and planogram compliance. This technological advancement allows retailers to quickly identify out-of-stock situations, pricing discrepancies, and promotional compliance issues, leading to improved sales performance and reduced revenue leakage. The integration of AI with IoT devices and cloud platforms further amplifies the ability of retailers to collect, analyze, and act upon vast amounts of data, transforming traditional retail execution into a highly optimized, data-centric process.

Another significant driver for the Retail Execution Monitoring AI market is the intensifying competition in the retail sector, which has compelled retailers to focus on operational excellence and personalized customer experiences. With the proliferation of omnichannel retailing, consumers now expect seamless shopping experiences across physical and digital touchpoints. AI-powered retail execution monitoring solutions enable retailers to maintain consistent product availability, optimal shelf presentation, and accurate pricing across all channels. These solutions also facilitate predictive analytics, allowing retailers to anticipate demand fluctuations, optimize inventory levels, and tailor promotions to specific customer segments. As a result, retailers can achieve higher conversion rates, foster brand loyalty, and gain a competitive edge in a rapidly evolving market environment.

The surge in adoption of cloud-based AI solutions is another pivotal growth factor for the Retail Execution Monitoring AI market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling retailers of all sizes to implement advanced AI-driven monitoring tools without significant upfront investments in IT infrastructure. The cloud model also supports seamless integration with other enterprise systems, such as ERP and CRM, enabling a unified view of store operations and customer interactions. Moreover, cloud-based solutions facilitate rapid updates and deployment of new features, ensuring that retailers can quickly adapt to changing market dynamics and regulatory requirements. The growing ecosystem of AI solution providers, coupled with increasing investments in digital transformation initiatives, is expected to further accelerate the adoption of cloud-based retail execution monitoring solutions worldwide.

From a regional perspective, North America currently holds the largest share of the global Retail Execution Monitoring AI market, driven by the presence of leading retail chains, high technology adoption rates, and a mature digital infrastructure. Europe follows closely, with significant investments in retail automation and AI-driven analytics. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, fueled by rapid urbanization, expanding retail networks, and increasing penetration of AI technologies in emerging economies such as China and India. Latin America and the Middle East & Africa are also expected to experience steady market growth, supported by growing retail modernization initiatives and rising consumer demand for enhanced shopping experiences. Overall, the global landscape for Retail Execution Monitoring AI is characterized by dynamic regional trends and a strong trajectory for future expansion.<br /

AI in Contact Center Automation Market Outlook

According to our latest research, the global AI in Contact Center Automation market size reached USD 3.7 billion in 2024, demonstrating significant momentum in the adoption of artificial intelligence technologies across customer engagement channels. The market, as per our analysis, is expected to register a robust CAGR of 22.8% from 2025 to 2033, propelling the market to an estimated value of USD 30.6 billion by 2033. This remarkable growth is primarily driven by the increasing demand for personalized customer experiences, operational efficiency, and cost reduction in contact center operations, as organizations globally invest in AI solutions to transform their customer service delivery models.

One of the primary growth factors fueling the expansion of the AI in Contact Center Automation market is the rapid evolution of customer expectations in the digital era. Consumers now expect instant, seamless, and personalized interactions across multiple channels, placing immense pressure on organizations to modernize their customer engagement strategies. AI-powered tools such as chatbots, virtual assistants, and speech analytics are revolutionizing the way contact centers operate by enabling 24/7 support, reducing response times, and providing tailored solutions based on real-time data. These advancements are not only improving customer satisfaction but also empowering agents with actionable insights, thereby enhancing the overall efficiency and effectiveness of contact center operations.

Another significant driver is the growing emphasis on cost optimization and resource efficiency within enterprises. Traditional contact centers are often burdened by high operational costs, repetitive manual tasks, and workforce management challenges. The integration of AI technologies, such as predictive analytics and workforce optimization solutions, is enabling organizations to automate routine inquiries, optimize staffing levels, and proactively address customer issues. This results in substantial cost savings, reduced churn rates, and improved agent productivity. Furthermore, the scalability and flexibility offered by cloud-based AI solutions are making it easier for companies of all sizes to deploy and manage sophisticated contact center automation tools without the need for substantial upfront investments.

The proliferation of advanced technologies, including natural language processing (NLP), machine learning, and speech recognition, is further accelerating the adoption of AI in contact center automation. These technologies are enhancing the accuracy and contextual understanding of AI-driven systems, enabling more human-like interactions and deeper customer insights. Additionally, the ongoing digital transformation initiatives across industries such as BFSI, healthcare, retail, and telecommunications are creating new opportunities for AI vendors to deliver specialized solutions tailored to industry-specific requirements. The convergence of AI with omnichannel communication platforms is also facilitating seamless integration across voice, chat, email, and social media channels, thereby enhancing the overall customer journey.

From a regional perspective, North America continues to dominate the AI in Contact Center Automation market, accounting for the largest share in 2024. This leadership position is attributed to the presence of major technology providers, early adoption of advanced AI solutions, and a highly competitive business environment that prioritizes customer experience. Europe and Asia Pacific are also witnessing significant growth, with increasing investments in digital transformation and rising demand for intelligent automation solutions in emerging markets. The Asia Pacific region, in particular, is expected to register the fastest CAGR during the forecast period, driven by the rapid expansion of the IT and telecommunications sector, growing e-commerce activities, and increasing focus on customer-centric business strategies.

Component Analysis

The AI in Contact Center Automation market is segmented by component into software and services, each playing a pivotal role in the overall ecosystem. The software segment encompasses a wide array of AI-powered applications, including chatbots, virtual assistants, predictive analytics, and speech analytics platforms. These solutions are at the forefront of driving automation and intelligence within contact centers, enabling organizations to deliver personalized, efficient

In our personal and professional lives, we have numerous opportunities to communicate our ideas to others. Therefore, the importance of presentation goes beyond academic success. However, efforts to improve presentation skills may be hampered by a variety of barriers, both personal and social, creating educational challenges. For example, the importance of presentation and communication skills is widely recognized among professionals in western countries, but this is not the case in some other countries. Overcoming these barriers would improve presentation skills and help individuals from diverse backgrounds effectively communicate their ideas. This would raise the visibility of underrepresented people, thereby increasing their recognition in both academic and non-academic settings, making the community as a whole more inclusive. Toward these goals, a pilot training workshop on presentations titled “Let’s Speak, Communicate, and Connect!” was held at the annual meeting of the Japanese Society of Plant Physiologists in March 2024. The participants expressed great interest in improving their presentation styles and receiving feedback, highlighting the need for such training programs. In the post-workshop survey, 16 of the 17 respondents indicated that the workshop helped them improve their presentations. These results suggest that the workshop lowered barriers to presentation and allowed for improvement. Improved presentations can also benefit outreach efforts. Altogether, we believe that cross-community support for improving presentation skills not only helps members in their personal development but also contributes to strengthening connections between the academic community and society.

Presentation Skills Training Market Outlook

According to our latest research, the global Presentation Skills Training market size reached USD 2.14 billion in 2024, reflecting a robust demand for professional development resources across industries. The market is set to advance at a CAGR of 8.1% from 2025 to 2033, fueled by rising emphasis on effective communication in the workplace, digital transformation of learning platforms, and the growing need for soft skills in an increasingly competitive business environment. By the end of 2033, the market is forecasted to reach USD 4.36 billion, underscoring the expanding investment in training programs that enhance presentation proficiency and confidence.

The primary growth driver for the Presentation Skills Training market is the escalating recognition of communication as a critical skill for organizational success. As businesses become more globalized and cross-functional collaboration intensifies, the ability to present ideas with clarity and persuasion has become essential for professionals at all levels. This shift is particularly pronounced in sectors such as finance, consulting, technology, and healthcare, where client-facing roles and internal knowledge sharing demand high-impact presentations. Furthermore, the proliferation of remote and hybrid work models has heightened the need for virtual presentation skills, prompting organizations to invest in both online and blended training solutions. The increased focus on employee development and leadership grooming further propels the demand for comprehensive presentation skills programs.

Another significant growth factor is the integration of advanced technologies into training methodologies. The adoption of artificial intelligence, interactive simulations, and real-time feedback systems has transformed traditional presentation skills training into highly personalized and engaging learning experiences. These innovations not only facilitate skill acquisition but also enable continuous improvement through data-driven insights and performance analytics. The rise of e-learning platforms and mobile applications has democratized access to high-quality training, allowing individuals and organizations to upskill at their own pace and convenience. As technology continues to evolve, the market is expected to witness the emergence of immersive and adaptive training formats that cater to diverse learning preferences.

Demographic shifts and the changing nature of the workforce are also shaping the trajectory of the Presentation Skills Training market. The influx of millennials and Gen Z professionals, who prioritize personal development and digital literacy, has spurred demand for contemporary training solutions that blend interactivity with practical relevance. Educational institutions and government agencies are increasingly incorporating presentation skills into their curricula and professional development programs, recognizing their importance in fostering employability and civic engagement. Additionally, the growing gig economy and entrepreneurial culture have led to a surge in individual learners seeking to enhance their public speaking and pitching abilities, further expanding the market’s addressable base.

From a regional perspective, North America continues to dominate the Presentation Skills Training market, accounting for over 36% of the global revenue in 2024. This leadership is attributed to the region’s mature corporate training ecosystem, high adoption rates of digital learning platforms, and a strong culture of professional development. Europe follows closely, with substantial investments in workforce upskilling and educational innovation. Meanwhile, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid economic development, increasing corporate activity, and a burgeoning demand for English-language communication skills. Latin America and the Middle East & Africa are also witnessing steady growth, supported by government initiatives and expanding access to online training resources.

Training Type Analysis

The Presentation Skills Training market is segmented by training type into In-Person, Online, and Blended formats. In-Person training has traditionally been the cornerstone of this market, offering participants direct interaction with trainers, real-time feedback, and immersive role-playing scenarios. Such face-to-face engagement fosters a collaborative learning environm

# Institute For the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset:

The ISCA project has compiled this dataset using an annotation portal, which was used to label tweets as either antisemitic or non-antisemitic, among other labels. Please note that the annotation was done with live data, including images and the context, such as threads. The original data was sourced from annotationportal.com.

# Content:
This dataset contains 6,941 tweets that cover a wide range of topics common in conversations about Jews, Israel, and antisemitism between January 2019 and December 2021. The dataset is drawn from representative samples during this period with relevant keywords. 1,250 tweets (18%) meet the IHRA definition of antisemitic messages.

The dataset has been compiled within the ISCA project using an annotation portal to label tweets as either antisemitic or non-antisemitic. The original data was sourced from annotationportal.com.

The tweets' distribution of all messages by year is as follows: 1,499 (22%) from 2019, 3,716 (54%) from 2020, and 1,726 (25%) from 2021. 4,605 (66%) contain the keyword "Jews," 1,524 (22%) include "Israel," 529 (8%) feature the derogatory term "ZioNazi*," and 283 (4%) use the slur "K---s." Some tweets may contain multiple keywords.

483 out of the 4,605 tweets with the keyword "Jews" (11%) and 203 out of the 1,524 tweets with the keyword "Israel" (13%) were classified as antisemitic. 97 out of the 283 tweets using the antisemitic slur "K---s" (34%) are antisemitic. Interestingly, many tweets featuring the slur "K---s" actually call out its usage. In contrast, the majority of tweets with the derogatory term "ZioNazi*" are antisemitic, with 467 out of 529 (88%) being classified as such.

File Description:

The dataset is provided in a csv file format, with each row representing a single tweet, including replies, quotes, and retweets. The file contains the following columns:

‘TweetID’: Represents the tweet ID.

‘Username’: Represents the username who published the tweet.
‘Text’: Represents the full text of the tweet.

‘CreateDate’: Represents the date the tweet was created.

‘Biased’: Represents the labeled by our annotations if the tweet is antisemitic or non-antisemitic.

‘Keyword’: Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username.

Licences

Data is published under the terms of the "Creative Commons Attribution 4.0 International" licence (https://creativecommons.org/licenses/by/4.0)

R code is published under the terms of the "MIT" licence (https://opensource.org/licenses/MIT) ‘

Acknowledgements

We are grateful for the support of Indiana University’s Observatory on Social Media (OSoMe) (Davis et al. 2016) and the contributions and annotations of all team members in our Social Media & Hate Research Lab at Indiana University’s Institute for the Study of Contemporary Antisemitism, especially Grace Bland, Elisha S. Breton, Kathryn Cooper, Robin Forstenhäusler, Sophie von Máriássy, Mabel Poindexter, Jenna Solomon, Clara Schilling, and Victor Tschiskale.

This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

This is an extended abstract for an industry paper at Australian Software Engineering Conference (ASWEC 2014), Sydney Australia. In this presentation, the authors discuss the role of research data in the future of software engineering, and describe the new paradigm of data intensive scientific discovery and its impact on software engineering research. This presentation addresses three questions: What is the research data management? How does it impact the future of software engineering? And why the role of software engineering is significant in the new paradigm of data intensive research? In addition, the authors present some of the new developments in the research data management technologies at the national and international levels.

Citation information in bibtex format: @CONFERENCE{aryani2014aswec,author = {Amir Aryani and Heinz Schmidt},title = {Research Data and the Future of Software Engineering},booktitle = {Australian Software Engineering Conference (ASWEC)},month = {April},year = {2014},address = {Sydney, Australia},doi = {dx.doi.org/10.6084/m9.figshare.956086}}

Case Study: How Can a Wellness Technology Company Play It Smart?

This is my first case study as a data analyst using Excel, Tableau, and R. This case study is a part of my Google Data Analytics Professional Certification. I know there may be some insights presented differently or any insights might not be covered as per the point of view of the reader who can provide feedback. Feedback will be appreciated.

Scenario: The Bellabeat data analysis case study! In this case, the study is to perform the real-world tasks of a junior data analyst. Bellabeat is a high-tech manufacturer of health-focused products for women. Bellabeat is a successful small company, but they have the potential to become a larger player in the global smart device market. Urška Sršen, co-founder and Chief Creative Officer of Bellabeat, believes that analyzing smart device fitness data could help unlock new growth opportunities for the company. You have been asked to focus on one of Bellabeat’s products and analyze smart device data to gain insight into how consumers are using their smart devices. The insights you discover will then help guide marketing strategy for the company and present analysis to the Bellabeat executive team along with your high-level recommendations for Bellabeat’s marketing strategy. The Case Study Roadmap followed, 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. Ask: Sršen asks you to analyze smart device usage data in order to gain insight into how consumers use non-Bellabeat smart devices. She then wants you to select one Bellabeat product to apply these insights to in your presentation. These questions will guide your analysis: 1. What are some trends in smart device usage? 2. How could these trends apply to Bellabeat customers? 3. How could these trends help influence Bellabeat's marketing strategy? To produce a report with the following deliverables: 1. A clear summary of the business task 2. A description of all data sources used 3. Documentation of any cleaning or manipulation of data 4. A summary of your analysis 5. Supporting visualizations and key findings 6. Your top high-level content recommendations based on your analysis Prepare: includes Dataset used, Accessibility and privacy of data, Information about our dataset, Data organization and verification, Data credibility and integrity. The dataset used for analysis is from Kaggle, which is considered a reliable source. Dataset owner Sršen encourages to use of public data that explores smart device users’ daily habits. She points you to a specific data set: Fitbit Fitness Tracker Data (CC0: Public Domain, dataset made available through Mobius): This Kaggle data set contains personal fitness trackers from thirty Fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits. Sršen tells that this data set might have some limitations, and encourages us to consider adding other data to help address those limitations by beginning to work more with this data. But, this analysis only confined primarily to the present dataset and has not yet been done analysis by adding other data to address any limitations of this dataset. I may take up later to collect additional datasets based on the availability of those datasets for individual analyst circumstances since companies provide datasets that are needed, may be available on a subscription basis or I need to search and access for similar product datasets. That is my limitation to confine my analysis to this dataset only. Process Phase: 1. Tools used for Analysis: Excel, Tableau, R studio, Kaggle 2. Cleaning of Data: includes removal of duplication of data but data itself by its nature includes Id, dates include repetition and also there are zero values by nature of recording since human beings are body and mind are complex, so the possibility of zero values inherent in data or any other reason yet to be known but an analysis done based on available data though which is not correct for live projects where someone available to discuss them. 3. Analysis was done based on available variables. Analyze Phase: Id Avg.VeryActiveDistance Avg.ModerateActiveDistance Avg.LightActiveDistance
TotalDistance Avg.Calories 1927972279 0.09580645 0.031290323 0.050709677
2026352035 0.006129032 0.011290322 3.43612904
3977333714 1.614999982 2.75099979 3.134333344
8053475328 8.514838742 0.423870965 2.533870955
8877689391 6.637419362 0.337741935 6.188709674 3420.258065 409.5...

This dataset contains over 500 Donald Trump Speeches from the years of 2015-2024. This dataset was made public so that voters can have more context before casting their 2024 US Presidential Election votes. Historians, Linguists and Language Analysts can also use this data in years to come for their research purposes. The data is unbiased, strictly Donald Trump speech, and from a diverse range of times, topics and contexts. Please make good use of this carefully and meticulously crafted dataset and don't forget to share your findings! One last thing…..Don't forget to vote in 2024!

Background Information

Sprocket Central Pty Ltd (fictional company), a medium size bikes & cycling accessories organization based in Australia.

Primarily, Sprocket Central Pty Ltd needs help with its customer and transactions data. The organization has a large dataset relating to its customers, but their team is unsure how to effectively analyze it to help optimize its marketing strategy.

The client provided KPMG with 2 datasets:

• Customer Demographic: customer_id, first_name, last_name, gender, past_3_years_bike_related_purchases, DOB, job_title, job_industry_category, wealth_segment, deceased_indicator, default, owns_car, tenure

• Transactions data in 2017: transaction_id, product_id, customer_id, transaction_date, online_order, order_status, brand, product_line, product_class, product_size, list_price, standard_cost, product_first_sold_date

This is a project from my KPMG data analytics virtual internship program where work as a Data Analyst to performed three tasks:

Data Quality Assessment: Assessment of data quality and completeness in preparation for analysis. Data Insights: Targeting high value customers based on customers demographic and attributes. Data Insights and Presentation: Using visualizations to present insights.

Data Quality Assessment

During this task, assessment of data quality and completeness was done on the three (3) datasets and recommendations are provided in preparation for analysis using Excel Spreadsheet.

Some of the data issues:

• Quality issues

• Transaction dataset

Online order, brand, product line, product class, product size, list price, standard cost, and product first sold date fields contains blank cells. Inconsistent data type for Product first sold date field. Duplicated values in Customer Id field.

• Customer Demographic dataset

Last name, DOB, Job title, tenure fields contains blank cells. Incorrect data (U, F, M, Femal) in the Gender field. Incorrect data in the DOB field

Data Insights

During this task, the existing 2 datasets (Customer demographic and transactions) was used as a labeled dataset to provide recommendations on which of the 1,000 new customers dataset should be targeted to drive the most value for the organization by following 3 phases:

Data Exploration Data Model Development Data Interpretation.

Data Insights and Presentations

During this task, created a dashboard using Excel Spreadsheet to communicate and convey key findings from the analysis and also give recommendations to the stakeholders of the organization.

Customer Data Analytics on the Telecom

In this project, you are going to analyze the USA market for an Australia-based telecom provider company, FREECELL, which is looking to enter into the USA market. FREECELL is one of the major telecom provider based out of Australia. Now they are looking to expand their services across the globe and as part of this process, they are looking into the USA market. But before they actually expand their services, they wanted to understand the market and the user behavior. To this end, the company has hired you as the lead business analyst and they are looking for your help with some of the questions that they have about the USA Telecom Market. The analysis for this project is going to span across 5 broad areas: Market Analysis Churn Analysis Customer Segmentation People Analytics Presentation and Communication of Analysis

Task 1

Business roles at AgroStar require a baseline of analytical skills, and it is also critical that we are able to explain complex concepts in a simple way to a variety of audiences. This test is structured so that someone with the baseline skills needed to succeed in the role should be able to complete this in under 4 hours without assistance.

Use the data in the included sheet to address the following scenario...

Since its inception, AgroStar has been leveraging an assisted marketplace model. Given that the market potential is huge and that the target customer appreciates a physical store nearby, we have taken a call to explore the offline retail model to drive growth. The primary objective is to get a larger wallet share for AgroStar among existing customers.

Assume you are back in time, in August 2018 and you have been asked to determine the location (taluka) of the first AgroStar offline retail store. 1. What are the key factors you would use to determine the location? Why? 2. What taluka (across three states) would you look open in? Why?

Guidelines:

-- (1) Please mention any assumptions you have made and the underlying thought process -- (2) Please treat the assignment as standalone (it should be self-explanatory to someone who reads it), but we will have a follow-up discussion with you in which we will walk through your approach to this assignment. -- (3) Mention any data that may be missing that would make this study more meaningful -- (4) Kindly conduct your analysis within the spreadsheet, we would like to see the working sheet. If you face any issues due to the file size, kindly download this file and share an excel sheet with us -- (5) If you would like to append a word document/presentation to summarize, please go ahead. -- (6) In case you use any external data source/article, kindly share the source.

Task 4 Cohort

The file CDNOW_master.txt contains the entire purchase history up to the end of June 1998 of the cohort of 23,570 individuals who made their first-ever purchase at CDNOW in the first quarter of 1997. This CDNOW dataset was first used by Fader and Hardie (2001).

Each record in this file, 69,659 in total, comprises four fields: the customer's ID, the date of the transaction, the number of CDs purchased, and the dollar value of the transaction.

CustID = CDNOW_master(:,1); % customer id Date = CDNOW_master(:,2); % transaction date Quant = CDNOW_master(:,3); % number of CDs purchased Spend = CDNOW_master(:,4); % dollar value (excl. S&H)

See "Notes on the CDNOW Master Data Set" (http://brucehardie.com/notes/026/) for details of how the 1/10th systematic sample (http://brucehardie.com/datasets/CDNOW_sample.zip) used in many papers was created.

Reference:

Fader, Peter S. and Bruce G.,S. Hardie, (2001), "Forecasting Repeat Sales at CDNOW: A Case Study," Interfaces, 31 (May-June), Part 2 of 2, S94-S107.

Task 6 Zupee.csv

I have merged all three datasets into one file and also did some feature engineering.
Available Data: You will be given anonymized user gameplay data in the form of 3 csv files. Fields in the data are as described below: Gameplay_Data.csv contains the following fields: * Uid: Alphanumeric unique Id assigned to user * Eventtime: DateTime on which user played the tournament * Entry_Fee: Entry Fee of tournament * Win_Loss: ‘W’ if the user won that particular tournament, ‘L’ otherwise * Winnings: How much money the user won in the tournament (0 for ‘L’) * Tournament_Type: Type of tournament user played (A / B / C / D) * Num_Players: Number of players that played in this tournament

Wallet_Balance.csv contains following fields: * Uid: Alphanumeric unique Id assigned to user * Timestamp: DateTime at which user’s wallet balance is given * Wallet_Balance: User’s wallet balance at given time stamp

Demographic.csv contains following fields: * Uid: Alphanumeric unique Id assigned to user * Installed_At: Timestamp at which user installed the app * Connection_Type: User’s internet connection type (Ex: Cellular / Dial Up) * Cpu_Type: Cpu type of device that the user is playing with * Network_Type: Network type in encoded form * Device_Manufacturer: Ex: Realme * ISP: Internet Service Provider. Ex: Airtel * Country * Country_Subdivision * City * Postal_Code * Language: Language that user has selected for gameplay * Device_Name * Device_Type

Build a basic recommendation system which is able to rank/recommend relevant tournaments and entry prices to the user. The main objectives are: 1. A user should not have to scroll too much before selecting a tournament of their preference 2. We would like the user to play as high an entry fee tournament as possible

Context

Law 190 2012 requires public administrations and public controlled companies to publish data regarding the contracting tendering procedures. The data must be published on the institution’s website (or in the case of a publicly owned company that does not have a website on the website of the parent administration) within the sections “Amministrazione Trasparente” or “Società Trasparente”, for public administrations and publicly owned or controlled corporations respectively in XML format.

The main defect of the regulation is that the data are not collected in a single database but remain disseminated on the websites of public administrations, which hinders large-scale analysis. The following database wants to fill this gap by aggregating all the information provided by all the subjects to the law 190/2012.

Data Collection and Exploration

The aforementioned set of regulations requires public administrations and public-controlled companies to publish data regarding the contracting tendering procedures. The law gives indications of a content nature, leaving the technical aspects to the implementing decrees. In general, it is important to notice that the data must be published on the institution’s website (or in the case of a publicly owned company that does not have a website on the website of the parent administration) within the sections “Amministrazione Trasparente” or “Società Trasparente”, for public administrations and publicly owned or controlled corporations respectively.

The problem with the current data management system

The legislator indicates the XML as the format to be used. The use of XML, which is the same used for the electronic invoicing system recently implemented, presents several advantages like the fact that it is platform and programming language independent, supporting technological changes when that happens. Moreover, the data stored and transported using XML can be changed at any point in time without affecting the data presentation. Although the technical advantages brought by XML, from a citizen perspective (with very low data literacy) it presents some drawbacks. Despite XML format being human-readable and some browsers can open it, it is harder to manipulate and query compared to a more popular format like an excel file. To overcome this obstacle it is desirable that each website of the public administration is equipped with an interface that allows citizens to view the information contained in these files and that to conduct simple queries. Unfortunately, from a high-level overview, this best practice seems not yet widespread.

Another important aspect is that the data are not collected in a single database but remain disseminated on the websites of public administrations. This organizational framework allows citizens and investigative journalists to inspect the data only within the perimeter of a single institution (public administration or publicly owned company) while extending the analysis on other dimensions remains out of reach for the average user. For example, while it is relatively easy to see all the tenders with the relative winner tenderers of a given institution it is not possible without further processing to see all the winning bids of a tenderer, or the details of all published tenders in a given geographical region. In general, a systematic analysis of the state of health of public tenders is not possible without intensive prior aggregation and processing activity.

From here derives the need for a comprehensive database that contains all the information provided by all the subjects to the law 190/2012. Unfortunately, this database is not provided by the public authority nor by the controlling authority ANAC. Hence the creation of this dataset

File download

As we already mentioned ANAC does not collect and organize the data required by the 190/2012 law in a single database, however, each law’s subject has to send each year a pec email (certified email) to ANAC that contains the link of the requested XML file stored in the institutional website or in a third-party provider.

ANAC conducts an access test to check that the link provided by the public administration is functioning and that the file complies with the prescribed XML structure. Then ANAC published the result of this activity in its open data section by showing the generalities of the institutions, the result of the test (pass/failed), and the link to the file.

It is noteworthy that ANAC does not actively persecute those institutions that present irregularities in the data provided nor check whether all the institutions provide the requested data. ANAC perimeter of action in this sense is limited to the publication of test data while it is the responsibility of the public administration to check the outcome and resolve any rep...