Statistics are used in many areas of daily life including business, agriculture, forestry, education, ecological studies, medical studies, sports, and computer science. Some examples include using statistics to measure business performance, understand customer data, compare crop yields over time in agriculture, track changes in forest areas and species populations, analyze education spending and enrollment trends, study the impacts of pollution and more. Statistics help with data analysis, prediction, and drawing conclusions across various domains.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
Application statistics in software engineeringmd emran
This presentation introduces the application of statistics in software engineering. It begins with introducing the presenters and topic. Statistics is then defined as the science of collecting, organizing, analyzing and interpreting numerical data to make valid decisions. Software engineering is the application of engineering principles to software design, development, implementation, testing and maintenance. Some common elements between statistics and software engineering are discussed, such as data, variables, and information. Finally, the use of statistics in areas like machine learning, data science, data mining, informatics and big data is described. The presentation concludes with discussing some applications of statistics in software engineering and providing references.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
Post-science is the architect of 4 biggest (multi-trillion) businesses (The businesses are connected):
1. Infinite Spreadsheet Decision Center
2. Computer Newspaper (for collecting input data for the Infinite Spreadsheet)
3. Universal Permanent Number (for assigning globally searchable distinct integer names to permanent entities, such as DNA, people, books)
4. Completely Automated Self-Checkout (an application of Universal Permanent Number)
This document provides an overview of data science. It defines data science as using computer science, statistics, machine learning, visualization, and human-computer interaction to analyze and interact with data. The key topics covered include prerequisites for data science like computer science, statistics, machine learning and visualization. Common data science tasks are also outlined such as data analysis, modeling, engineering and prototyping. The document discusses what a data scientist does and how to tackle a data problem by consulting subject matter experts, identifying anomalies, and reducing risk and uncertainty in the data.
Statistics are used in many areas of daily life including business, agriculture, forestry, education, ecological studies, medical studies, sports, and computer science. Some examples include using statistics to measure business performance, understand customer data, compare crop yields over time in agriculture, track changes in forest areas and species populations, analyze education spending and enrollment trends, study the impacts of pollution and more. Statistics help with data analysis, prediction, and drawing conclusions across various domains.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
Application statistics in software engineeringmd emran
This presentation introduces the application of statistics in software engineering. It begins with introducing the presenters and topic. Statistics is then defined as the science of collecting, organizing, analyzing and interpreting numerical data to make valid decisions. Software engineering is the application of engineering principles to software design, development, implementation, testing and maintenance. Some common elements between statistics and software engineering are discussed, such as data, variables, and information. Finally, the use of statistics in areas like machine learning, data science, data mining, informatics and big data is described. The presentation concludes with discussing some applications of statistics in software engineering and providing references.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
Post-science is the architect of 4 biggest (multi-trillion) businesses (The businesses are connected):
1. Infinite Spreadsheet Decision Center
2. Computer Newspaper (for collecting input data for the Infinite Spreadsheet)
3. Universal Permanent Number (for assigning globally searchable distinct integer names to permanent entities, such as DNA, people, books)
4. Completely Automated Self-Checkout (an application of Universal Permanent Number)
This document provides an overview of data science. It defines data science as using computer science, statistics, machine learning, visualization, and human-computer interaction to analyze and interact with data. The key topics covered include prerequisites for data science like computer science, statistics, machine learning and visualization. Common data science tasks are also outlined such as data analysis, modeling, engineering and prototyping. The document discusses what a data scientist does and how to tackle a data problem by consulting subject matter experts, identifying anomalies, and reducing risk and uncertainty in the data.
This document discusses big data and its characteristics. It provides examples of how companies like Walmart and Facebook handle large amounts of data. It defines big data and describes the types of data: structured, unstructured, and semi-structured. The key characteristics of big data are identified as volume, variety, velocity, and variability. The document concludes that with billions more people gaining internet access, the amount of data will continue growing exponentially and we have only begun to see the potential of big data.
Significant Role of Statistics in Computational SciencesEditor IJCATR
This paper is focused on the issues related to optimizing statistical approaches in the emerging fields of Computer Science
and Information Technology. More emphasis has been given on the role of statistical techniques in modern data mining. Statistics is
the science of learning from data and of measuring, controlling, and communicating uncertainty. Statistical approaches can play a vital
role for providing significance contribution in the field of software engineering, neural network, data mining, bioinformatics and other
allied fields. Statistical techniques not only helps make scientific models but it quantifies the reliability, reproducibility and general
uncertainty associated with these models. In the current scenario, large amount of data is automatically recorded with computers and
managed with the data base management systems (DBMS) for storage and fast retrieval purpose. The practice of examining large preexisting
databases in order to generate new information is known as data mining. Presently, data mining has attracted substantial
attention in the research and commercial arena which involves applications of a variety of statistical techniques. Twenty years ago
mostly data was collected manually and the data set was in simple form but in present time, there have been considerable changes in
the nature of data. Statistical techniques and computer applications can be utilized to obtain maximum information with the fewest
possible measurements to reduce the cost of data collection.
This document discusses how companies can use big data to adapt their market strategies in a volatile, uncertain, complex, and ambiguous (VUCA) world. It explains that big data allows for predictive, descriptive, and discovery analytics that can help companies anticipate issues, understand consequences, and identify opportunities. However, companies need to have an adaptable structure and decentralized data architecture to effectively leverage big data insights. Doing so will help companies better plan for alternative realities, manage risks, and foster change to remain competitive in a constantly changing environment.
Main points of this slide presentation:
1.What is statistics?
2.Application
3.Application of Statistics in Computer Science and Engineering
4.Machine learning’s Relation to statistics
5.Application of Statistics in Data mining
6.Data mining relation with Statistics
7.Outline of Applications
8.Some Outline of Application’s details are given below
Thank you
What we’re about to explore are the possibilities of deep learning which are already enabling machines to peek into the future. Driven by data the predictions come with greater confidence than ever before, and the scope of growth is quite akin to magic! Let’s take a brief look at the technology, how it actually works, and some of it’s novel uses to improve our society!
Explore : https://boxx.ai/#home
How Does AutoML Address Data Preprocessing?Auger.AI
Data preprocessing is an important aspect of automated machine learning, as generating a usable dataset for prediction and classification problems is among the most time-consuming aspects of data science problems. Most machine learning algorithms work only with well-structured data, but in reality, most real-world data needs considerable work prior to being usable.
In this 30 minute webinar, we’ll examine some of the most common data problems such as missing values, scaling feature values for algorithms that need it, handling cyclic features, and removing low variance or highly correlated features. We’ll also look at some of the ways AutoML tools put the “auto” into addressing data preprocessing issues, with a detailed look at how Auger addresses data preprocessing.
Presenter: Vladyslav Khizhanov
Application of calculus in our daily liferehan9911
Calculus is used in many fields including physics, engineering, economics, statistics, and medicine. It is used to create mathematical models and arrive at optimal solutions. For example, in physics, many concepts are based on calculus. Weather prediction also uses calculus-based computer modeling to more accurately predict weather based on variables like temperature, wind speed, and moisture levels. The spread of infectious diseases can also be modeled using calculus to determine how far and fast a disease may spread based on susceptible, infected, and recovered populations. Calculus is widely applied in engineering fields from civil to mechanical engineering to analyze structures, fluids, thermal systems, and more.
1. Statistics has played a key role in India since the 1920s when Professor Mahalanobis conducted early statistical studies and analyses that had great impact in fields like agriculture and flood control.
2. Mahalanobis influenced many talented young scholars to take up statistics and helped establish the Indian Statistical Institute.
3. Statistics is a science, technology, and art that has techniques derived from principles but also relies on inductive reasoning and the skill of statisticians. It is evolving as a "meta-science" to understand other disciplines through its methodology.
The interpretation of the relationship among big data, IoT and smart city - C...Antenna Manufacturer Coco
Big data is an intangible means of production in the information society. Its concept has been repeatedly interpreted by various sectors of the society. However, many people are unclear about the relationship between the big data, the Internet of Things, and the smart city. In this regard, Tong Fang Internet of Things Industrial Technology Division Zhao Ying made a detailed interpretation of this.
Calculus has many applications in fields like engineering, science, biology, economics and other areas. It was initially developed for navigation but is now used in building skyscrapers, bridges, vehicles, studying ecosystems, measuring growth rates, astronomy, space technology, economics, business surveys, investment planning and more. The document provides examples of how differentiation and integration from calculus are applied in various disciplines like using it for robotics, electrical systems, drug concentration in biology, Newton's laws of motion, and economic concepts. It concludes by stating calculus is also employed in mapping, solving paradoxes, and other domains.
From Smart Buildings to Smart Cities discusses how buildings and cities can become smarter through data sharing and analytics. As more devices connect to the internet, an enormous amount of data is being generated. Smart buildings can help reduce energy usage and eventually produce energy. When buildings communicate and share data like CO2 levels, temperature, and energy consumption, it helps create smart cities. Analytics of all this building and city data can provide insights, predict future usage, and help improve efficiency.
This slide is about Application of-statistics-in-CSE.Here you can helps from statistics application.This slide is very easy to understand and very helpful for engineering student.Specially for bangladeshi student.
How to correctly estimate the effect of online advertisement(About Double Mac...Yusuke Kaneko
Double Machine Learning (Double ML) is a method for estimating treatment effects in high-dimensional data, such as online advertising data, that has many predictor variables. It involves using machine learning to first predict the outcome and treatment, and then regressing the treatment effect parameter on the residuals. This provides accurate estimates of treatment effects compared to methods like single equation regression. The document demonstrates Double ML through simulations using a large online advertising dataset, finding it outperforms other methods like naive averaging and standard lasso in estimating average treatment effects even in the presence of sampling bias.
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Data science involves applying statistics and computer science to extract valuable insights from large amounts of data. It helps businesses increase efficiency, identify opportunities, and improve marketing. Data science incorporates techniques like machine learning, predictive analytics, and data visualization. While data science focuses on correlations in data, data analytics is designed to uncover specifics from insights. A data analyst role is suited for those starting in analytics, while a data scientist role involves advanced machine learning and deep learning. Common sources of data for machine learning models include open source datasets, web scraping, and manual data generation.
This document discusses big data and its characteristics. It provides examples of how companies like Walmart and Facebook handle large amounts of data. It defines big data and describes the types of data: structured, unstructured, and semi-structured. The key characteristics of big data are identified as volume, variety, velocity, and variability. The document concludes that with billions more people gaining internet access, the amount of data will continue growing exponentially and we have only begun to see the potential of big data.
Significant Role of Statistics in Computational SciencesEditor IJCATR
This paper is focused on the issues related to optimizing statistical approaches in the emerging fields of Computer Science
and Information Technology. More emphasis has been given on the role of statistical techniques in modern data mining. Statistics is
the science of learning from data and of measuring, controlling, and communicating uncertainty. Statistical approaches can play a vital
role for providing significance contribution in the field of software engineering, neural network, data mining, bioinformatics and other
allied fields. Statistical techniques not only helps make scientific models but it quantifies the reliability, reproducibility and general
uncertainty associated with these models. In the current scenario, large amount of data is automatically recorded with computers and
managed with the data base management systems (DBMS) for storage and fast retrieval purpose. The practice of examining large preexisting
databases in order to generate new information is known as data mining. Presently, data mining has attracted substantial
attention in the research and commercial arena which involves applications of a variety of statistical techniques. Twenty years ago
mostly data was collected manually and the data set was in simple form but in present time, there have been considerable changes in
the nature of data. Statistical techniques and computer applications can be utilized to obtain maximum information with the fewest
possible measurements to reduce the cost of data collection.
This document discusses how companies can use big data to adapt their market strategies in a volatile, uncertain, complex, and ambiguous (VUCA) world. It explains that big data allows for predictive, descriptive, and discovery analytics that can help companies anticipate issues, understand consequences, and identify opportunities. However, companies need to have an adaptable structure and decentralized data architecture to effectively leverage big data insights. Doing so will help companies better plan for alternative realities, manage risks, and foster change to remain competitive in a constantly changing environment.
Main points of this slide presentation:
1.What is statistics?
2.Application
3.Application of Statistics in Computer Science and Engineering
4.Machine learning’s Relation to statistics
5.Application of Statistics in Data mining
6.Data mining relation with Statistics
7.Outline of Applications
8.Some Outline of Application’s details are given below
Thank you
What we’re about to explore are the possibilities of deep learning which are already enabling machines to peek into the future. Driven by data the predictions come with greater confidence than ever before, and the scope of growth is quite akin to magic! Let’s take a brief look at the technology, how it actually works, and some of it’s novel uses to improve our society!
Explore : https://boxx.ai/#home
How Does AutoML Address Data Preprocessing?Auger.AI
Data preprocessing is an important aspect of automated machine learning, as generating a usable dataset for prediction and classification problems is among the most time-consuming aspects of data science problems. Most machine learning algorithms work only with well-structured data, but in reality, most real-world data needs considerable work prior to being usable.
In this 30 minute webinar, we’ll examine some of the most common data problems such as missing values, scaling feature values for algorithms that need it, handling cyclic features, and removing low variance or highly correlated features. We’ll also look at some of the ways AutoML tools put the “auto” into addressing data preprocessing issues, with a detailed look at how Auger addresses data preprocessing.
Presenter: Vladyslav Khizhanov
Application of calculus in our daily liferehan9911
Calculus is used in many fields including physics, engineering, economics, statistics, and medicine. It is used to create mathematical models and arrive at optimal solutions. For example, in physics, many concepts are based on calculus. Weather prediction also uses calculus-based computer modeling to more accurately predict weather based on variables like temperature, wind speed, and moisture levels. The spread of infectious diseases can also be modeled using calculus to determine how far and fast a disease may spread based on susceptible, infected, and recovered populations. Calculus is widely applied in engineering fields from civil to mechanical engineering to analyze structures, fluids, thermal systems, and more.
1. Statistics has played a key role in India since the 1920s when Professor Mahalanobis conducted early statistical studies and analyses that had great impact in fields like agriculture and flood control.
2. Mahalanobis influenced many talented young scholars to take up statistics and helped establish the Indian Statistical Institute.
3. Statistics is a science, technology, and art that has techniques derived from principles but also relies on inductive reasoning and the skill of statisticians. It is evolving as a "meta-science" to understand other disciplines through its methodology.
The interpretation of the relationship among big data, IoT and smart city - C...Antenna Manufacturer Coco
Big data is an intangible means of production in the information society. Its concept has been repeatedly interpreted by various sectors of the society. However, many people are unclear about the relationship between the big data, the Internet of Things, and the smart city. In this regard, Tong Fang Internet of Things Industrial Technology Division Zhao Ying made a detailed interpretation of this.
Calculus has many applications in fields like engineering, science, biology, economics and other areas. It was initially developed for navigation but is now used in building skyscrapers, bridges, vehicles, studying ecosystems, measuring growth rates, astronomy, space technology, economics, business surveys, investment planning and more. The document provides examples of how differentiation and integration from calculus are applied in various disciplines like using it for robotics, electrical systems, drug concentration in biology, Newton's laws of motion, and economic concepts. It concludes by stating calculus is also employed in mapping, solving paradoxes, and other domains.
From Smart Buildings to Smart Cities discusses how buildings and cities can become smarter through data sharing and analytics. As more devices connect to the internet, an enormous amount of data is being generated. Smart buildings can help reduce energy usage and eventually produce energy. When buildings communicate and share data like CO2 levels, temperature, and energy consumption, it helps create smart cities. Analytics of all this building and city data can provide insights, predict future usage, and help improve efficiency.
This slide is about Application of-statistics-in-CSE.Here you can helps from statistics application.This slide is very easy to understand and very helpful for engineering student.Specially for bangladeshi student.
How to correctly estimate the effect of online advertisement(About Double Mac...Yusuke Kaneko
Double Machine Learning (Double ML) is a method for estimating treatment effects in high-dimensional data, such as online advertising data, that has many predictor variables. It involves using machine learning to first predict the outcome and treatment, and then regressing the treatment effect parameter on the residuals. This provides accurate estimates of treatment effects compared to methods like single equation regression. The document demonstrates Double ML through simulations using a large online advertising dataset, finding it outperforms other methods like naive averaging and standard lasso in estimating average treatment effects even in the presence of sampling bias.
What is Machine Learning: A Business Perspective.
A Gentle Introduction to Machine Learning, by Enrique Dans, professor of innovation at IE Business School.
*MLSEV 2020: Virtual Conference.
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Data science involves applying statistics and computer science to extract valuable insights from large amounts of data. It helps businesses increase efficiency, identify opportunities, and improve marketing. Data science incorporates techniques like machine learning, predictive analytics, and data visualization. While data science focuses on correlations in data, data analytics is designed to uncover specifics from insights. A data analyst role is suited for those starting in analytics, while a data scientist role involves advanced machine learning and deep learning. Common sources of data for machine learning models include open source datasets, web scraping, and manual data generation.
Machine learning algorithms analyze large amounts of data to identify patterns and make predictions. There are two main types: supervised learning predicts outcomes based on historical labeled data, while unsupervised learning identifies structure in unlabeled data to group it into categories. Effective machine learning requires choosing the right algorithm for the specific data and application, and allows creating intelligent processes that generate useful predictions for a data-driven world.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
Want Free Career Counseling?
Just fill in your details, and one of our experts will call you!
Call us: +918308103366
WhatsApp Us: https://wa.me/+918308103366
Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
How Artificial Intelligence (AI) and Stem are Shaping Our Future.pdfMindful!
As technology advances, the world is changing rapidly. Artificial Intelligence (AI) and STEM (Science, Technology, Engineering, and Mathematics) are two powerful forces driving this change.
Info-Tech Research Group is a global leader that provides IT research, advice, and tools to its clients. It helps organizations address the full spectrum of IT concerns through actionable insights and recommendations combined with ready-to-use templates and tools.
The document discusses machine learning and how it differs from traditional analytics. It notes that machine learning uses large datasets and uncleaned data to develop algorithms that can continuously learn and improve predictions over time. Key benefits include leveraging all available customer data to deliver personalized experiences and insights. While correlations found may not be causal, machine learning is valuable for solving complex problems and gaining competitive advantages through real-time insights.
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
M121SSL Business Analytics And Intelligence.docxstirlingvwriters
Machine learning and data mining techniques can be used to develop business intelligence applications in the education sector. These applications analyze student data to provide insights into administrative efficiency, academic outcomes, and workforce management. Examples include using classification and clustering on student data in reporting tools or dashboards. Case studies demonstrate using spreadsheets to track student activities, digital dashboards to view key performance indicators, and data warehouses to integrate and analyze historical student data. Business intelligence becomes more important for strategic decision making and is expected to incorporate more machine learning, integration with other systems, and data productivity tools.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
How are machine learning and artificial intelligence revolutionizing insurance?
This presentation explains it briefly, including current trends and effects on the business.
This document provides an overview of machine learning and how it can benefit businesses. It begins with defining machine learning as software that can learn from data like humans do in order to solve problems. The document then discusses myths and facts about machine learning, how it works, case studies of companies using it, and provides a guide for getting started with machine learning including adjusting mindsets, defining problems, collecting data, and finding tools. The overall message is that machine learning can provide competitive advantages and dramatically impact businesses if leveraged properly.
The document summarizes James LoBuono's interview about the growing demand for data scientists. Some key points:
- Data science skills are in high demand across industries due to increased data availability from sensors and cloud computing.
- Data scientists are needed to extract useful information from messy, unstructured data sources to aid decision making.
- Programming languages like Python and tools like machine learning are commonly used in data science roles.
- Data science can help solve business problems and unlock opportunities by making decisions based on data analysis rather than intuition alone.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Martina Pugliese gives a presentation about her background in physics and transition to a career in data science. She completed degrees in physics, including a PhD exploring how natural language evolves over time. She did a data science bootcamp to gain industry skills. Her current role involves using machine learning and data visualization to understand user behavior on a fashion app and improve personalization, retention, and other business metrics. Data science draws on her physics training in modeling reality mathematically and dealing with large datasets, combining academic rigor with an application to real-world problems.
In this presentation, Saksham introduces the topic of SMAC, trends and real life examples of deployment of the SMAC stack. His interest area is predictive analytics.
The document discusses several key trends in analytics for 2015:
1. Data security is a major concern as data volumes grow exponentially, requiring companies to quadruple down on security efforts through innovation, analytics, and tighter integration.
2. The rise of the Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating these systems.
3. Some argue that data should be monetized as an asset, but this brings risks around privacy, ethics, and real costs that companies need to consider carefully.
4. Cognitive analytics is enhancing decision-making by providing users with vast new sources of knowledge, though questions remain about how these systems will impact human roles over time
Machine learning is a method of data analysis that allows computer systems to automatically learn and improve from experience without being explicitly programmed. It works by building models from data to make predictions or decisions without relying on rule-based programming. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. There are several types of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Machine learning has many applications and is used across various industries like healthcare, retail, finance, government and transportation to extract insights from data.
Similar to Statistics vs machine learning: which is more powerful (20)
Understanding between Visual Studio vs Visual Studio Code may depend on your work style and features and the language support you need. Here's the difference.
Top 8 Different Types Of Charts In Statistics And Their UsesStat Analytica
This document discusses different types of charts used in statistics to visually represent data, including bar charts, line charts, pie charts, histograms, scatter plots, exponential graphs, and trigonometric graphs. Bar charts and line charts are useful for comparing data across categories and showing trends over time. Pie charts show proportions of data as slices of a circle. Histograms group data into bins to summarize continuous or discrete measurements. Scatter plots show the relationship between two numeric variables using positioned dots. Exponential and trigonometric graphs visually represent their respective functions and are used in engineering and research.
Do you need Excel homework help? Hire our MS Excel experts to get the best Help With Excel Homework. Ask us to do my Excel Homework at affordable prices.
Most prominent methods of how to find outliers in statisticsStat Analytica
This document discusses two prominent methods for finding outliers in statistics: the interquartile range (IQR) method and the Tukey method. Both methods use quartiles to determine a range of values that are not outliers, and then identify outliers as any data points that fall above or below this range. The document provides examples of each method being applied to sample data sets to identify outlier values. It concludes by encouraging the use of these IQR and Tukey methods to solve problems involving outliers.
Statistics for economics its benefits and limitationsStat Analytica
The document discusses the results of a study on the impact of COVID-19 lockdowns on greenhouse gas emissions. The strict lockdowns and travel restrictions implemented by many countries led to a temporary yet significant drop in global carbon dioxide emissions of up to 17% compared to 2019 levels. However, this reduction is expected to be short-lived as emissions are projected to rise again as economic activity picks up unless countries make permanent shifts toward greener energy and transportation systems.
Top 7 types of Statistics Graphs for Data RepresentationStat Analytica
Are you struggling with choosing the right type of graph to represent your data set? if yes then have a look at this presentation to choose the best statistics graph to represent your data set.
The Comprehensive Guide on Branches of MathematicsStat Analytica
Are you struggling to get all the branches of mathematics? If yes then here is the best ever presentation that will help you to get all the branches of math. Here we have mentioned the basic mathematics branches to the advanced level.
Top 10 importance of mathematics in everyday lifeStat Analytica
Would you like to know the importance of mathematics? If yes, then have a look at this presentation to explore the top uses of mathematics in our daily life. Watch the presentation till the end to explore the importance of mathematics.
The document discusses data classification, which involves organizing data into categories to make it easier to analyze and retrieve. It covers the objectives of classification like arranging large volumes of data and highlighting similarities. The key types are one-way, two-way, and multi-way classification. Classification provides benefits like confidentiality, integrity, and availability of data. Methods involve scanning, identifying, separating data, and creating a classification policy.
Analysis of variance (ANOVA) everything you need to knowStat Analytica
Most of the students may struggle with the analysis of variance (ANOVA). Here in this presentation you can clear all your doubts in analysis of variance with suitable examples.
The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
Top tips on how to learn math with these simple waysStat Analytica
Finding it difficult to learn math? If yes, then here are the best ever tips on how to learn math from basic to the advanced level. Follow all these tips to start leaning math and get decent command over math.
What are the uses of excel in our daily life?Stat Analytica
Facebook owns Instagram and Pinterest. It acquired Instagram in 2012 for $1 billion and the image sharing platform has over 1 billion monthly users. Pinterest is an online pinboard and image sharing website that was founded in 2010 and allows users to save and share images, videos and recipes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
2. Topics to Discuss
A brief outline
Statistics
Machine learning
Difference Between Statistics vs Machine Learning
Industries using statistics
Industries using machine learning
Statistics vs Machine Learning
They belong to different schools
They came up in different eras
Types of data they deal with
Predictive Power and Human Effort
Conclusion
3. Overview
STATISTICS VS. MACHINE LEARNING IS ALWAYS A SIGNIFICANT
ISSUE THAT THE STATISTICS STUDENTS FACE. THEY ARE STILL
NOT ABLE TO DIFFERENTIATE BETWEEN MACHINE LEARNING AND
STATISTICAL MODELING.THE OBJECTIVE OF STATISTICS AND
MACHINE LEARNING IS ALMOST THE SAME. BUT THE SIGNIFICANT
DIFFERENCE BETWEEN BOTH IS THE VOLUME OF DATA AND
HUMAN INVOLVEMENT FOR BUILDING A MODEL.IN THIS BLOG, I
AM GOING TO SHARE WITH YOU THE DIFFERENCE BETWEEN
STATISTICS VS. MACHINE LEARNING. BEFORE WE GET STARTED,
LET’S HAVE A LOOK AT THE DEFINITION OF MACHINE LEARNING
AND STATISTICS.
4. Statistics
What is Statistics?
Statistics is all about the study of
collection, analysis, interpretation,
presentation, and organization of data.
Whenever we use statistics in scientific, and
industrial problem, we begin the process by
deciding a statistical model
process.Statistics plays a crucial role in
human activity. It means that with the help
of statistics, we can track human activities.
5. Machine Learning
What is machine learning?
Machine learning is the future technology.
It is developing at a rapid pace. During the
last few years, machine learning has
reached the next level. It is used in various
fields like fraud detection, web search
results, real-time ads on web pages and
mobile devices, image recognition, robotics,
and many other areas.Machine learning is
a part of computer science. It has been
evolved from the study of computational
learning and theory in artificial
intelligence.
6. Difference Between
Statistics vs Machine
Learning
Nowadays, data is the key to success for the business. But
data is constantly changing and evolving at a rapid pace.
Therefore the business needs some techniques to convert the
raw data into valuable one. For this they take help of
machine learning and statistics.Data is collected in the
organization from everyday operations. The companies
always need to convert the data into valuable data;
otherwise, the data is no more than the garbage.
7. INDUSTRIES USING
STATISTICS
Almost every industry use the statistics. Because without
statistics, we can’t get the conclusion from the data.
Nowadays, statistics is crucial for various fields like
eCommerce, trade, psychology, chemistry, and much
more.
8. Industries
using
statistics
BUSINESS
Statistics is one of the significant aspects
of companies.
ECONOMICS
Statistics is the base of Economics. It is
playing a crucial role in economics.
MATHEMATICS
Statistics is also an integrated part of
mathematics. Statistics help in describing
measurements in a precise manner.
BANKING
Statistics plays an essential part in the
banking sector. Banks require statistics for
the number of different reasons.
STATE MANAGEMENT
Statistics is an essential aspect of the
development of the country.
9. INDUSTRIES USING MACHINE
LEARNING
The evolution of computer and technologies has produced
machine learning. Machine learning has changed the way
we live our lives. There are lots of industries which are using
machine learning.
10. INDUSTRIES USING MACHINE
LEARNING
BUSINESS
Brands are using
machine learning to
create various models
to examine their
performance. Machine
learning allows the
brands to create
thousands of model in
a week.
DECISION
MAKING
Machine learning is also
helpful in decision
making. It helps to
reproduce the known
patterns and
knowledge.These
patterns automatically
applied to the data.
NEURAL
NETWORKS
Neural networks were
used for data mining
applications. But after the
evolution of machine
learning, it is possible to
create multiple neural
networks that are having
many layers.
11. They belong to different
schools
Machine Learning
Machine learning is a subset of computer science and
artificial intelligence. It deal with building a system
that can learn from the data instead of learning from
the pre-programmed instructions.
Statistics
Statistics is a subset of mathematics. It deals with
finding the relationship between variables to
predict the outcome.
12. They came up in different
eras
Statistics is quite older than machine learning. On the
other hand, machine learning got into existence a few
years ago. Machine learning comes into existence in the
1990s, but it was not getting that much popular.But
after the computing becomes cheaper, then the data
scientist moves into the development of machine
learning. The number of growing data and complexity of
big data has increased the need for machine learning.
13. Types of data they deal
with
Machine learning offers a wide range of tools. For
prediction, the data on the fly, we use online learning
tools. These tools are the most potent tools and
capable of learning from a trillion of observation on one
by one basis.But the prediction and lean can do
simultaneously.
On the other hand, statistical models are generally
applied for smaller data with fewer attributes.It is quite
overwhelming to process the massive amount of data
using statistics.
14. Predictive Power and
Human Effort
It is a common thing that nature will not assume
anything before forcing an event to occur. So the less
the assumption in the predictive model, the higher will
be the predictive power.Machine learning is used to
reduce human efforts. Machine learning is based on the
iteration where the algorithms try to find the pattern in
On the other hand, the statistical model is based on
mathematics intensive and coefficient estimation.
15. Conclusion
Now you make have get the
precise comparison between
statistics vs machine
learning. One more last
thing I would like to
mentioned here that machine
learning without statistics is
nothing.If you’re a statistics
student then you can have
the best statistics homework
help at nominal charges.