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Data Science and Fastboss Application

by Feb 16, 2021Automation, Business, Business Intelligence0 comments

Data Science and Fastboss Application

Artificial Intelligence Empowers your Business

Fastboss use Data Science to analyze and predict data for business processes.   

To grow your company over time, you need to set up automation systems now and implement a smart business as soon as possible.

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  Fastboss uses data Science for Analyzing and verbal interpretation of your Business Data 

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and. Data science is related to learning and big data.

Data science is a “concept to unify statistics, data analysis, and their related methods” in order to “understand and analyze actual phenomena” with data. It uses techniques and theories drawn from many fields within the context of winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.

Data science is an interdisciplinary field focused on extracting knowledge from data sets, which are typically large. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization. As such, it incorporates skills from computer science, mathematics, statistics, information visualization, graphic design, complex systems, communication, and business. Statistician Nathan Yau, drawing on Ben Fry, also links data science to : users should be able to intuitively control and explore data.

Many statisticians have argued that data science is not a new field, but rather another name for statistics. Others argue that data science is distinct from statistics because it focuses on problems and techniques unique to digital data. Statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data (e.g. images) and emphasizes prediction and action. Data science is not distinguished from statistics by the size of datasets or use of computing, and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. Data science is an applied field growing out of traditional statistics. Data science can be therefore described as an applied branch of statistics.

The term “data science” has been traced back to 1974, when Peter Naur proposed it as an alternative name for computer science. The Chinese Academy of Sciences in Beijing suggested that statistics should be renamed data science. During the 1990s, popular terms for the process of finding patterns in datasets (which were increasingly large) included “knowledge discovery” and “data mining”.

“Data science” became more widely used in the next few years. There is still no consensus on the definition of data science and it is considered by some to be a buzzword.

Big data is very quickly becoming a vital tool for businesses and companies of all sizes. The availability and interpretation of big data have altered the business models of old industries and enabled the creation of new ones. Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations. As big data continues to have a major impact on the world, data science does as well due to the close relationship between the two.

There are a variety of different technologies and techniques that are used for data science which depend on the application. More recently, full-featured, end-to-end platforms have been developed and heavily used for data science and machine learning.

  1. Linear Regression
  2. Logistic Regression
  3. Decision tree is used as prediction models for classification and data fitting. The decision tree structure can be used to generate rules able to classify or predict target/class/label variables based on the observation attributes.
  4. Support Vector Machine (SVM)
  5. Clustering is a technique used to group data together.
  6. Dimensionality reduction is used to reduce the complexity of data computation so that it can be performed more quickly.
  7. Machine learning is a technique used to perform tasks by inference patterns from data.

 

Some of these methods are used by Fastboss to analyze and predict data for business processes. For the user to be able to make decisions based on financial data, Fastboss analyzes and interprets the financial results and exposes them to the user in a familiar way, in the way that a financial consultant would expose to come into business decision support.

Future iterations of Fastboss software development include the integration and analysis of financial data for interpretation by the user in common and easy-to-understand language. The user not only benefits from a digital assistant that helps him to generate business documents but also interprets the daily state of the business so that the user is aware of the status of his company and can make decisions based on recurring and in-depth analysis. Thus, once the company is anchored in the Fastboss system, the user receives an automated business control and analysis system.

To grow your company over time, you need to set up automation systems now and implement a smart business as soon as possible.

Now open this opportunity for your company, including artificial intelligence, to work with you to grow your business. Your business is worth it!

(Source: Wikipedia)

 

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