MATLAB led the global advanced analytics and data science industry in 2022 with a market share of 14.5 percent, closely followed by Alteryx and HubSpot Analytics who accounted for 10.82 and 6.51 percent of the market share, respectively.

Across industries, organizations are increasing their hiring efforts to build larger data science arsenals: from 2020 to 2021, the percentage of surveyed organizations that employed 50 data scientists or more increased from 30 percent to almost 60 percent. On average, the number of data scientists employed in a organization grew from 28 to 50.

The statistic displays the most wanted data science skills in the United States as of April 2019. As of the measured period, 76.13 percent of data scientist job openings on LinkedIn required a knowledge of the programming language Python.

This dataset is about book subjects and is filtered where the books is Probability and statistics for data science : Math+R+Data, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

The average annual salary of a Data Architect in India was estimated to be over two million Indian rupees per annum, the highest among other jobs in the Data Science sector in India. It was followed by data Scientist and Database Developer roles.

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets.
Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning.
Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools’ increasing popularity can be attributed to features like sophisticated modeling and predictive analytics.
A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential.
Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software.
Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques.
Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this.
Big Data Analytics’s Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data.
Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities.
Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector.
Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

Access the summary of the Data Science Platform market report, featuring key insights, executive summary, market size, CAGR, growth rate, and future outlook.

Most studies in the life sciences and other disciplines involve generating and analyzing numerical data of some type as the foundation for scientific findings. Working with numerical data involves multiple challenges. These include reproducible data acquisition, appropriate data storage, computationally correct data analysis, appropriate reporting and presentation of the results, and suitable data interpretation.Finding and correcting mistakes when analyzing and interpreting data can be frustrating and time-consuming. Presenting or publishing incorrect results is embarrassing but not uncommon. Particular sources of errors are inappropriate use of statistical methods and incorrect interpretation of data by software. To detect mistakes as early as possible, one should frequently check intermediate and final results for plausibility. Clearly documenting how quantities and results were obtained facilitates correcting mistakes. Properly understanding data is indispensable for reaching well-founded conclusions from experimental results. Units are needed to make sense of numbers, and uncertainty should be estimated to know how meaningful results are. Descriptive statistics and significance testing are useful tools for interpreting numerical results if applied correctly. However, blindly trusting in computed numbers can also be misleading, so it is worth thinking about how data should be summarized quantitatively to properly answer the question at hand. Finally, a suitable form of presentation is needed so that the data can properly support the interpretation and findings. By additionally sharing the relevant data, others can access, understand, and ultimately make use of the results.These quick tips are intended to provide guidelines for correctly interpreting, efficiently analyzing, and presenting numerical data in a useful way.

Question Paper Solutions of chapter Exploratory Data Analytics and Descriptive Statistics of Data Analytics Skills for Managers, 5th Semester , Bachelor in Business Administration 2020 - 2021

Global Data Science Platform Market Size and Forecast

Global Data Science Platform Market size was valued at USD 101.34 Billion in 2024 and is projected to reach USD 739.07 Billion by 2031 growing at a CAGR of 31.10% from 2024 to 2031.

Global Data Science Platform Market Drivers

AI and Machine Learning Integration: As AI and machine learning technologies become more widely adopted, demand for data science platforms grows. The United States Bureau of Labour Statistics predicts a 36% increase in data scientist jobs between 2021 and 2031, underlining the growing need for advanced platforms to develop and scale intelligent applications.

Demand for Business Intelligence and Analytics: As firms rely more on data-driven decision-making, there is a greater need for advanced analytics and business intelligence capabilities. Data science platforms provide critical tools for these roles, resulting in market growth, as evidenced by a predicted CAGR of 27.6% from 2022 to 2027.

Saudi Arabia data science platform market size reached USD 4.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 50.0 Billion by 2033, exhibiting a growth rate (CAGR) of 28.2% during 2025-2033. Comprehensive software and hardware infrastructures that play a pivotal role in facilitating the data science process are primarily driving the market growth across the country.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on component, application, and vertical.

International Journal of Data Science and Analytics Acceptance Rate - ResearchHelpDesk - International Journal of Data Science and Analytics - Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the data science tech stack in 2024 was Python 3.x, chosen by 15.7 percent of respondents. ETL ranked second, being used by 9.8 percent of respondents. This comes as no surprise due to Python's importance in building artificial intelligence (AI) solutions and machine learning products.

The global data science platform market size reached USD 15.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 144.9 Billion by 2033, exhibiting a growth rate (CAGR) of 27.08% during 2025-2033. The rising utilization of data science platforms in the healthcare industry, the growing demand for cloud-based programs in various business organizations, and the rising integration of advanced technologies in data science platforms represent some of the key factors driving the market.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional and country levels from 2025-2033. Our report has categorized the market based on component, application and vertical.

Question Paper Solutions of chapter Inferential Statistics of Basic Data Science, 3rd Semester , Master of Computer Applications (2 Years)

Question Paper Solutions of chapter Descriptive Statistics of Basic Data Science, 3rd Semester , Master of Computer Applications (2 Years)

Question Paper Solutions of chapter Statistical Quality Control of Data Analytics Skills for Managers, 5th Semester , Bachelor in Business Administration 2020 - 2021

Crime isn't a topic most people want to use mental energy to think about. We want to avoid harm, protect our loved ones, and hold on to what we claim is ours. So how do we remain vigilant without digging too deep into the filth that is crime? Data, of course. The focus of our study is to explore possible trends between crime and communities in the city of Calgary. Our purpose is visualize Calgary criminal behaviour in order to help increase awareness for both citizens and law enforcement. Through the use of our visuals, individuals can make more informed decisions to improve the overall safety of their lives. Some of the main concerns of the study include: how crime rates increase with population, which areas in Calgary have the most crime, and if crime adheres to time-sensative patterns.

In 2022, over 18 thousand data science job positions were available in the BFSI sector in India. An increase in the availability of data science jobs was seen over the years from 2019. E-commerce and internet followed suite with roughly 13 thousand jobs during the same time period.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution.

In the paper Adams, Matthew (2022) Integral, mean and covariance of the simplex-truncated multivariate normal distribution. PLoS One, 17(7), Article number: e0272014. https://eprints.qut.edu.au/233964/, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution.

This dataset consists of all code and results for the associated article.