International Research Data Analysis Excellence Awards

Big data tools help you organize, store, visualize, and analyze the huge amounts of data that your customers and enterprises generate every day. Big data analytics has a lot of potential, but traditional data tools can’t handle this huge amount of complex data. That’s why several different types of Big Data software and architectural solutions were created. Big Data Tools pull and analyze data from multiple sources. They can be used for ETL, Data Visualization, Machine Learning, Cloud Computing, and more. With specially designed Big Data tools, you can use your data to discover new opportunities and business models. Why are big data tools and technologies important to data professionals? Businesses around the world are beginning to understand the importance of their data. According to Fortune Business Insights, the global market for Big Data analytics is expected to reach $549.3 billion by 2028. Business and IT services will account for half of all revenue by 2028. Many businesses are...

UK government researchers have recently found that the systems used to protect AI chatbots are not secure. They can easily bypass cyber security measures. This means that all AI-powered chatbots are more prone to provide incorrect, harmful, or explicit responses. Along with this, they can be easily spoiled by some basic instructions or they may lead to wrong conclusions. The UK’s AI Safety Institute (AISI) said the systems it tested were highly vulnerable to jailbreak. It is a hacking method that aims to circumvent the ethical safeguards of AI models and obtain information that is otherwise inaccessible or restricted. During the study, it was concluded that this could be done through simple techniques. AISI researchers tested five large language models (LLMs), but did not disclose their names. In this case, the security model could be easily dodged. “All tested LLMs are highly vulnerable to basic jailbreaking, and some will cause harmful consequences even without attempts to circumven...

Private equity (PE) firms have long relied on industry experience and partner connections to source deals and drive returns. However, these traditional methods are quickly losing effectiveness in today’s rapidly evolving landscape. Amid a growing spate of challenges like market volatility, interest rate hikes, regulatory changes and increased competition, these organizations must embrace data analytics to achieve set investment goals. Done right, this approach promises more objective decision-making and a healthier bottom line. How Data Analytics Has Changed Private Equity According to a 2023 Ernst & Young survey of CFOs and COOs, the private equity industry has grown rapidly over the last decade. Assets under management increased by roughly $2.4 trillion between 2013 and 2022. The ability to leverage timely insights from vast and diverse datasets has been fundamental to this development and will likely continue to be relevant over the coming years. For one, it facilitates data-dri...

One of the fastest-growing parts of the economy in the last ten years has been healthcare, and in light of the growing threats of pandemics like the coronavirus outbreak, the industry is set to rise once again. To stay ahead of the curve in demand for healthcare services and solutions, organizations worldwide are turning to advanced techniques like AI, machine learning, and Big Data. AI is going to be huge in healthcare. According to Acumen Research and Consulting, the global market will hit $8 billion by 2026 and there is a huge overlap of skills in AI and big data—where the processing of information is optimized to help solve business and real-world problems. AI and big data provide numerous potential benefits for individuals and companies alike, including: Empowering patient self-service with chatbots Diagnosing patients with faster computer-aided design Analyzing image data to examine the molecular structure in drug discovery, and by radiologists to analyze and diagnose patients P...

Providers who have barely come to grips with putting data into their electronic health records (EHRs) are now tasked with pulling actionable insights out of them – and applying those learnings to complicated initiatives that directly impact reimbursement. For healthcare organizations that successfully integrate data-driven insights into their clinical and operational processes, the rewards can be huge. Healthier patients, lower care costs, more visibility into performance, and higher staff and consumer satisfaction rates are among the many benefits of turning data assets into data insights. However, the road to meaningful healthcare analytics is a rocky one, filled with challenges and problems to solve. Big data are complex and unwieldy, requiring healthcare organizations to take a close look at their approaches to collecting, storing, analyzing, and presenting their data to staff members, business partners, and patients. READ MORE: The Healthcare Data Cycle: Generation, Collection, a...

Decoding data brilliance: Unveiling the best programming languages for data visualization In the dynamic landscape of data-driven decision-making, the role of data visualization is paramount. Choosing the right programming language can significantly impact the effectiveness of presenting complex data in an understandable and insightful manner. Here, we delve into some of the best programming languages for data visualization, each offering unique strengths and applications. R Programming Language: A Statistical Powerhouse R stands out as a statistical computing and graphics language. Widely acclaimed among statisticians and data scientists, R provides an extensive set of tools for data analysis and visualization. Its dedicated packages like ggplot2 enable the creation of sophisticated visualizations, making R an invaluable choice for professionals dealing with statistical data and exploratory analysis. Scala: Scalability and Concurrency for Big Data Visualization Scala seamlessly combi...

The generative AI party is still raging. This zeitgeist has rocked the business world daily in a million ways, and the ground is still shifting. Now, four months into 2024, we’re starting to see businesses, particularly those with rarified pragmatic brands, starting to demand evidence of value, of the path to the true ROI derived from AI. As pragmatic voices for value rise, how do thoughtful business leaders respond? Alteryx studied exactly this question. What are the concrete pathways to AI value? We surveyed leading CIOs and board members and found a brightly lit approach to engineering emerging AI capabilities into business outcomes. Our survey found that generative AI is already impacting the achievement of organizational goals at 80% of organizations. What led the way, as the #2 and #3 use cases, were analytics—both the creation of and the synthesis of new insights for the organization. These use cases trailed only content generation in terms of embrace. What makes analytics and g...

Introduction: A data analyst pulls in, processes, and views data to produce data models, which are used to guide operational and strategic decision-making. They are the ones who clean and organize raw data, carry out statistical analyses, and also create visualizations to convey the results. Data analysts usually engage in the data collections by using databases and statistical software tools to figure out significant insights. They collaborate with Program Stewards to identify business needs, and they use their experience to develop practical recommendations. Detail-oriented attitude and problem-solving skills are the key components for recognizing the trends and patterns in data sets. The data analytic specialists perform a very important function in ensuring that organizations come up with well-informed decisions and their operations are in their best shape to perform at their best. Having said that let us look at the top entry level data analyst jobs. Top Entry Level Data Analyst ...

Table of Contents 1. Banking and Securities 2. Communications, Media and Entertainment 3. Healthcare Providers 4. Education 5. Manufacturing and Natural ResourcesView More Industry influencers, academicians, and other prominent stakeholders certainly agree that Big Data has become a big game-changer in most, if not all, types of modern industries over the last few years. As Big Data continues to permeate our day-to-day lives, there has been a significant shift of focus from the hype surrounding it to finding real value in its use. While understanding the value of Big Data continues to remain a challenge, other practical challenges, including funding and return on investment and skills, continue to remain at the forefront for several different industries that are adopting Big Data. With that said, according to Research and Market reports, the global Big Data market size is expected to reach USD 268.4 billion by 2026. Generally, most organizations have several goals for adopting Big Dat...

Explore these top 10 online courses to master Python for Data Science Data analysis in data science means being familiar with the programming language Python, which has a reputation for being the best, for performing tasks such as data manipulation. Regardless if you are a newcomer, who wants to kick off your career in data science, or a professional, who wants to add to your current skill set, take the online course to accumulate the necessary Python skills for automation and data analysis. In this guide, we will explore the top 10 Python courses available online that can prepare people for using Python to solve data analysis, machine learning, and AI problems. In the scope of the curricula, you will go from the bottom up, mastering the fundamentals of programming to the most advanced things like deep learning and AI applications, which are suitable for people with different levels of skill. Check out that right Python program which gives a scope to learn what you want and be an expe...