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Jyoti Tejpal
Cloud-based On-premise programming Modeling & simulation platform, Create algorithms, Algorithm development, Data analysis, Visualization, Numerical computation
On-premise Statistical solution Improves efficiency Predictive analysis Easily accessible Manage & analyze data sets-without previous statistics virtual ability
User-friendly Comprehensive Affordable Highly Able Add-on to Excel with 200 statistical features General/field-oriented Data visualization Statistical modeling
Statistical analysis software provides companies to collect, organize, interpret and present data in meaningful ways. It offers companies to analyze existing data using mathematical models and simulation techniques, and create business-relevant insights to make the facility able to get new opportunities, understand past sales trends, and forecast future demand curves.
Statistical analysis is the step-wise procedure for generating statistics from collected and stored data, thereby analyzing the results to conclude or deduce meaning about the underlying data set or the realistic fact that it tries to explain.
Statistics is defined as “…the study of the collection, analysis, interpretation, presentation, and organization of data.” That’s generally the same as the definition of data science. The term data science was initially coined in 2001 by Purdue statistician William S. Cleveland in the title to his paper “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.”
So you might say that data science importantly is statistical analysis, together with its growing technical functions for collecting source data and representing in due course, the analyzed information.
Statistical analysis may be used to:
Any data analyst will spend much of his/her time performing some or all of those functions.
Statistical Analysis as a Component of Data Science
Data analyst programs focus heavily on getting statistical analysis techniques not only to providing companies with particular tools for their future trade, but also assisting companies with mathematical thinking patterns. Data analyst look at data problems in a varied way than most ordinary people, both because they have the tools to break-up the given questions down in critical and mathematically valid approaches, and also because analyst have an advanced understanding of probability theory thru various tools provided by the software.
Probability theory doesn’t just find use in college classrooms and computer labs. It helps as an exercise in thinking that changes the way companies look at the competitive world. By acknowledging the mathematical likelihood of crucial statistical outputs, the data analyst can tell at a glance whether they see a new and critical insight emerge from companies analysis, or merely a natural distribution of productions for the companies governed by the laws of chance. Moreover, Statistical Analysis software can perform the calculations on those results to prove whether they are random or relevant.
Statistical analysis, and the firm mathematical foundation it rests on, is one significant reason why data analysis is not merely a career that a talented college drop-out can learn through self-study and hard work. A data analyst with statistics and mathematics can effectively lead the way to a vital subject that matters the most for any company …. profits in future.
Statistical analysis will assist the core aspects of business by analyzing using tools and techniques like:
Most Statistical analysis software will also be exposed to the R programming language as part of their statistical analysis. R is a highly specialized language for data analysis and has many built-in tools for statistical analysis and data visualization. It’s also highly extensible, so companies get even more esoteric analytical techniques fro their future businesses.
Managing large volumes of data: Statistical analysis software can get through a series of large amounts of data, and different data types. Statistical analysis programs can help the processing of structured, unstructured, sensory, mobile, web, and social media data faster and more accurately than traditional data management tools and/or with manual efforts.
Projecting valuable business insights: Statistical analysis tools allow companies to collect and analyze data to deliver actionable insights, which can provide businesses with exploring new revenue streams, identifying gaps in current workflows, and studying past patterns to predict future needs. Thoroughly analyzed data can give insights that quicken decision-making and optimize existing workflows.
Data visualization: Analyzing raw data and presenting insights via interactive charts and graphs.
Reporting/Analytics: Tracking and analyzing data, and organizing it into informational summaries that can be printed and exported in a different format.
Regression analysis: Establishing a relationship between two or more variables of interest and understanding how independent variables are related to the dependent variable.
Multivariate analysis: Observing and analyzing multiple dependent variables simultaneously concerning different variables.
Time-series: Capturing sequences occurring at various points within a specified period and making predictions based on past trends.
Statistical simulation: Creating mathematical models to duplicate operations of a real-world procedure or system.
Most statistical analysis tools available in the market are priced on a “per user, per year” subscription basis. They can be divided into three pricing tiers based on their starting price. A premium product is priced higher and includes additional features like decision trees and forecasting, third-party integrations, and enterprise-level support.
Business requirements: Modern statistical analysis software can perform a lot, from simple calculations and charting to mathematical modelling and spectrum analysis. However, licensing costs shoot up along with advanced analytics features. Users must carefully check their unique business needs, data forms, and types of models required in analyzing the business data before starting their statistical analysis software research. Identifying specific needs can assist users in selecting software that well-matched with their requirements within budget, and avoid paying for specifications they may never utilize.
Learning curve: It can take time to learn statistical analysis software and become familiar with its features, coding, and interface. While it depends on the software’s ease-of-use and user learning speed, consider testing out several statistical analysis tools to help in obtaining an overall idea of the learning curve for every aspect before buying anything.
AI and machine learning-augmented analytics: Artificial intelligence and machine learning augmented analytics is the next big thing in the data analytics field. Instead of using regular statistical models and adopting traditional approaches, statistical analysis software will interact with the data and help to share insights and discoveries. Most data analysis tasks will be automated and will be allowing users to produce a higher volume of advanced analysis.
Self-service analytics will scale up: Self-service analytics is a form of data analytics in which business professionals are encouraged to run statistical analysis queries on their own without any assistance from IT or data analytical teams. This trend is on the rise, especially in the small and midsize business segment, which always have a problem of resource crunch and is always seeking employees with multiple skill sets. This is a critical factor that will drive statistical analysis software sellers to shift their products from coding-based to graphical user interface (GUI)-oriented tools. They will be designed to assist non-technical users in doing their data-backed analysis without depending on cumbersome codes or programming languages.
