The document discusses artificial intelligence for text analytics and natural language processing. It provides an introduction to text analytics and NLP, explaining that text analytics extracts useful information from text sources while NLP makes natural language accessible to machines. It then discusses how AI can enable applications like competitive intelligence, human resource management, and market analysis by automatically analyzing large amounts of text data. The document also provides an overview of how natural language processing works using deep learning techniques.
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
Learn about the different Job Profiles in Big Data and Why is Big Data the best career move? Learn Big Data from StackDataLabs and get certified by the Professionals!
The document discusses data analytics and its evolution from relying on past experiences to using data-driven insights. It covers the types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics summarize past data, diagnostic analytics determine factors influencing outcomes, predictive analytics make future predictions, and prescriptive analytics identify best courses of action. The document also discusses data analysis tools, natural language processing, applications of analytics, benefits of analytics for IoT, and issues with big data in IoT contexts like smart agriculture.
Python is a widely used programming language for data science. It has a comprehensive standard library and supports multiple programming paradigms. Data science involves transforming data into meaningful information through techniques like probability, statistics, and analytics to help with decision making. Data science roles can include data analyst, data scientist, data engineer, and machine learning engineer. The data science process involves gathering, cleaning, analyzing, visualizing, and modeling data to gain insights. Tools like Pandas, Seaborn, and machine learning are used in data science.
The document discusses artificial intelligence for text analytics and natural language processing. It provides an introduction to text analytics and NLP, explaining that text analytics extracts useful information from text sources while NLP makes natural language accessible to machines. It then discusses how AI can enable applications like competitive intelligence, human resource management, and market analysis by automatically analyzing large amounts of text data. The document also provides an overview of how natural language processing works using deep learning techniques.
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
Learn about the different Job Profiles in Big Data and Why is Big Data the best career move? Learn Big Data from StackDataLabs and get certified by the Professionals!
The document discusses data analytics and its evolution from relying on past experiences to using data-driven insights. It covers the types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics summarize past data, diagnostic analytics determine factors influencing outcomes, predictive analytics make future predictions, and prescriptive analytics identify best courses of action. The document also discusses data analysis tools, natural language processing, applications of analytics, benefits of analytics for IoT, and issues with big data in IoT contexts like smart agriculture.
Python is a widely used programming language for data science. It has a comprehensive standard library and supports multiple programming paradigms. Data science involves transforming data into meaningful information through techniques like probability, statistics, and analytics to help with decision making. Data science roles can include data analyst, data scientist, data engineer, and machine learning engineer. The data science process involves gathering, cleaning, analyzing, visualizing, and modeling data to gain insights. Tools like Pandas, Seaborn, and machine learning are used in data science.
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-pune/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-pune/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-chennai/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-mumbai/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-mumbai/
This document provides an overview of key concepts in data analytics including:
- The sources and nature of data as well as classifications like structured, semi-structured, and unstructured data.
- The need for data analytics to gather hidden insights, generate reports, perform market analysis, and improve business requirements.
- The stages of the data analytics lifecycle including discovery, data preparation, model planning, model building, and communicating results.
- Popular tools used in data analytics like R, Python, Tableau, and SAS.
The document discusses business analytics and the role of a business analyst. It defines key terms like business analytics, data analytics, business intelligence, big data, data science, and data mining. It describes the skills required of a business analyst like understanding the business, basic statistics, Excel, and some analytics tools. The duties of a business analyst are to understand business problems and use data to help decision making. The document also lists some common business analyst job titles and roles.
Data Analytics Course In Hyderabad-OctoberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-hyderabad/
Data Analytics Course In Bangalore-AugustDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-bangalore/
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-delhi/
Data Analytics Course In Bangalore-NovemberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-bangalore/
Data Analytics Course In Chennai-NovemberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-chennai/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-delhi/
This document provides an overview of data science, data engineering, and data stories. It defines data science as an interdisciplinary field that uses algorithms and systems to extract knowledge from structured and unstructured data. It also explains that data science involves techniques like machine learning, statistical analysis, and data mining to analyze patterns in data and make predictions. Additionally, it states that data engineering is the process of collecting, transforming, and organizing data for analysis and decision-making through tools and technologies. Finally, it briefly mentions that data stories are narratives that explain data visually to drive decisions.
Modern Analytics And The Future Of Quality And Performance ExcellenceICFAI Business School
This document discusses modern business analytics and its applications. It defines analytics as using data, technology and analysis to help managers make better decisions. It outlines common analytics tools like Excel, SPSS and R. It traces the history and evolution of analytics from the 1950s to today. It describes the three main disciplines of analytics as business intelligence, quantitative methods, and statistics. It discusses descriptive, predictive and prescriptive analytics approaches. Finally, it discusses challenges and advantages of modern analytics for quality and strategic management.
Most organizations are challenged to control costs related to employees, support changing business needs, and retain top talent. SAP software can help with workforce planning and analytics through a comprehensive library of metrics, reporting, dashboards, and tools for workforce planning, HR, payroll, and talent management. ARK Solutions provides consulting services to implement the right SAP solutions to automate workforce planning, provide better insights with key metrics, and reduce manual data access efforts.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-pune/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-pune/
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For More Details: https://datamites.com/data-analytics-certification-course-training-chennai/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-mumbai/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-mumbai/
This document provides an overview of key concepts in data analytics including:
- The sources and nature of data as well as classifications like structured, semi-structured, and unstructured data.
- The need for data analytics to gather hidden insights, generate reports, perform market analysis, and improve business requirements.
- The stages of the data analytics lifecycle including discovery, data preparation, model planning, model building, and communicating results.
- Popular tools used in data analytics like R, Python, Tableau, and SAS.
The document discusses business analytics and the role of a business analyst. It defines key terms like business analytics, data analytics, business intelligence, big data, data science, and data mining. It describes the skills required of a business analyst like understanding the business, basic statistics, Excel, and some analytics tools. The duties of a business analyst are to understand business problems and use data to help decision making. The document also lists some common business analyst job titles and roles.
Data Analytics Course In Hyderabad-OctoberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-hyderabad/
Data Analytics Course In Bangalore-AugustDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-bangalore/
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-delhi/
Data Analytics Course In Bangalore-NovemberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-bangalore/
Data Analytics Course In Chennai-NovemberDataMites
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-chennai/
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-delhi/
This document provides an overview of data science, data engineering, and data stories. It defines data science as an interdisciplinary field that uses algorithms and systems to extract knowledge from structured and unstructured data. It also explains that data science involves techniques like machine learning, statistical analysis, and data mining to analyze patterns in data and make predictions. Additionally, it states that data engineering is the process of collecting, transforming, and organizing data for analysis and decision-making through tools and technologies. Finally, it briefly mentions that data stories are narratives that explain data visually to drive decisions.
Modern Analytics And The Future Of Quality And Performance ExcellenceICFAI Business School
This document discusses modern business analytics and its applications. It defines analytics as using data, technology and analysis to help managers make better decisions. It outlines common analytics tools like Excel, SPSS and R. It traces the history and evolution of analytics from the 1950s to today. It describes the three main disciplines of analytics as business intelligence, quantitative methods, and statistics. It discusses descriptive, predictive and prescriptive analytics approaches. Finally, it discusses challenges and advantages of modern analytics for quality and strategic management.
Most organizations are challenged to control costs related to employees, support changing business needs, and retain top talent. SAP software can help with workforce planning and analytics through a comprehensive library of metrics, reporting, dashboards, and tools for workforce planning, HR, payroll, and talent management. ARK Solutions provides consulting services to implement the right SAP solutions to automate workforce planning, provide better insights with key metrics, and reduce manual data access efforts.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
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ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
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Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
2. DEFINITION
• BA is the combination of skills, technologies, application and
processes used by organizations to gain insight in their business
based on data and statistics to drive business planning.
• Artificial Intelligence is the simulation of human intelligence
processes by machines, especially computer systems which include
speech recognition and machine vision
• Machine learning is a scientific study of algorithms and statistical
models that computer systems use in order to perform a specific task
effectively without using explicit instructions, relying on pattern. It’s a
subset of AI.
• Deep learning is subset of machine learning in AI that has networks
capable of learning unsupervised data that is unstructured or
unlabelled. Which is also known as deep neural learning.
3. PROGRAMMING LANGUAGES
• R:It is a programming languages used for statistical computing,
graphics and also for data miners for developing statistical software
and data analysis, which is free.
• Python: It is an interpreted, high level, general purpose programming
languages supports multiple programming paradigms, including
procedural, object-oriented and functional programming.
• Julia: It is garbage collected, uses eager evaluation, and includes
efficient libraries for floating point calculations , linear algebra,
random number generation and regular expression matching.
• Scala and JS are other examples
7. MARKETING
• Business analytics will be useful in sales, research, manufacturing
product, advertising, promotion.
• It helps companies target customer needs by focusing their
messaging or timing of a certain product or service on what is best
for the consumer.
• Spss is a software platform used for statistical analysis used in market
researchers, health researchers, survey researchers, marketing
organizations and data miners
• SAS developed for advanced analytics , business analytics, data
management and predictive analysis
• Understanding the big data works allows marketers to predict
purchases, analyze customer behaviour and better understand the
people buying their product or service, how much to spend the
money, optimise the ROI.
8. HR
• Business analytics in HR can help predict the hiring needs of an
organization.
• Using Hr analytics, one can predict the skills and positions which
are needed to improve the business performance.
• Big data refers to the use of many data sources to evaluate and
enhance practices including recruitment, training and
development, performance, compensation and overall business
performance.
• Analytics can help manage the and analysis the employee data,
new hiring information, reason behind people leaving the job
etc.
9. FINANCE
• It helps to set the goal for business, taking financial decisions,
improve the profitability, cash flow and value of your business.
• Analytics also helpful for providing the information about
investors, fund raisers, government agencies in analysing the
taxation owed to the firm.
• It also helpful for estimating the revenue of a particular product
of a business, employee salary of an organisation, to increase
business revenue, minimize the waste of the business.
10. OVERALL BENEFITS
• To make faster reporting analysis or planning.
• To make business decision.
• To improve data quality.
• Helps to develop marketing campaign
• Helps to use predictive insights to take action
• Taking historical data and forecasting for future.