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This document provides an introduction to data science. It discusses that data science uses computer science, statistics, machine learning, visualization, and human-computer interaction to collect, clean, analyze, visualize, and interact with data to create data products. It also describes the data science lifecycle as involving discovery, data preparation, model planning, model building, operationalizing models, and communicating results. Finally, it lists some common tools used in data science like Python, R, SQL, and Tableau.

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Ppt on data science

Data science uses data to find solutions and predict outcomes. It involves blending mathematics, business knowledge, tools, algorithms, and machine learning techniques to uncover hidden patterns in raw data. This helps with making major business decisions. Data science is used across many industries like manufacturing, e-commerce, banking, transportation, and healthcare for tasks like predicting problems, recommending products, detecting fraud, and discovering drugs. Real-world examples of data science applications include identifying online consumers, monitoring cars, and assisting in entertainment and retail brands.

Data science

The document outlines a data science roadmap that covers fundamental concepts, statistics, programming, machine learning, text mining, data visualization, big data, data ingestion, data munging, and tools. It provides the percentage of time that should be spent on each topic, and lists specific techniques in each area, such as linear regression, decision trees, and MapReduce in big data.

Data science

This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.

Data Science

The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.

Introduction to data science

This is a presentation prepared on Introduction to data science for the fulfillment of an university assignment

Introduction to data science.pptx

Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science

Data science

#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence

Introduction of Data Science

This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.

Ppt on data science

Data science uses data to find solutions and predict outcomes. It involves blending mathematics, business knowledge, tools, algorithms, and machine learning techniques to uncover hidden patterns in raw data. This helps with making major business decisions. Data science is used across many industries like manufacturing, e-commerce, banking, transportation, and healthcare for tasks like predicting problems, recommending products, detecting fraud, and discovering drugs. Real-world examples of data science applications include identifying online consumers, monitoring cars, and assisting in entertainment and retail brands.

Data science

The document outlines a data science roadmap that covers fundamental concepts, statistics, programming, machine learning, text mining, data visualization, big data, data ingestion, data munging, and tools. It provides the percentage of time that should be spent on each topic, and lists specific techniques in each area, such as linear regression, decision trees, and MapReduce in big data.

Data science

This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.

Data Science

The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.

Introduction to data science

This is a presentation prepared on Introduction to data science for the fulfillment of an university assignment

Introduction to data science.pptx

Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science

Data science

#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence

Introduction of Data Science

This document provides an overview of data science including what is big data and data science, applications of data science, and system infrastructure. It then discusses recommendation systems in more detail, describing them as systems that predict user preferences for items. A case study on recommendation systems follows, outlining collaborative filtering and content-based recommendation algorithms, and diving deeper into collaborative filtering approaches of user-based and item-based filtering. Challenges with collaborative filtering are also noted.

introduction to data science

Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio

Introduction to data science club

Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.

Introduction to Data Science

This document provides an overview of a data science course. It discusses topics like big data, data science components, use cases, Hadoop, R, and machine learning. The course objectives are to understand big data challenges, implement big data solutions, learn about data science components and prospects, analyze use cases using R and Hadoop, and understand machine learning concepts. The document outlines the topics that will be covered each day of the course including big data scenarios, introduction to data science, types of data scientists, and more.

Introduction to data science

Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.

Introduction to Data Science

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.

Data science presentation

This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.

Data science presentation 2nd CI day

A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.

Data science applications and usecases

The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.

Data science & data scientist

This document discusses the roles of data science and data scientists. It states that data science involves specialized skills in statistics, mathematics, programming, and computer science. A data scientist explores different data sources to discover hidden insights that can provide competitive advantages or address business problems. They are inquisitive individuals who can analyze data from multiple angles and recommend ways to apply findings to business challenges.

What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...

This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc

Introduction To Data Science

1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.

Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...

This document outlines an agenda for a data science training presentation. The agenda includes sections on why data science, what data science is, who a data scientist is, what they do, how to solve problems in data science, data science tools, and a demo. Key points are that data science uses tools, algorithms and machine learning to discover patterns in raw data and gain insights. It involves tasks like processing, cleaning, mining and modeling data, as well as communicating results. The problem solving process involves discovery, preparation, planning, building, operationalizing and communicating models.

Introduction on Data Science

The document describes a 10 module data science course covering topics such as introduction to data science, machine learning techniques using R, Hadoop architecture, and Mahout algorithms. The course includes live online classes, recorded lectures, quizzes, projects, and a certificate. Each module covers specific data science topics and techniques. The document provides details on the course content, objectives, and topics covered in module 1 which includes an introduction to data science, its components, use cases, and how to integrate R and Hadoop. Examples of data science applications in various domains like healthcare, retail, and social media are also presented.

Introduction of data science

Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.

Data Science Training | Data Science Tutorial | Data Science Certification | ...

This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2

What Is Data Science? | Introduction to Data Science | Data Science For Begin...

This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields

Introduction to Data Science

This document provides an introduction to data science and analytics. It discusses why data science jobs are in high demand, what skills are needed for these roles, and common types of analytics including descriptive, predictive, and prescriptive. It also covers topics like machine learning, big data, structured vs unstructured data, and examples of companies that utilize data and analytics like Amazon and Facebook. The document is intended to explain key concepts in data science and why attending a talk on this topic would be beneficial.

Introduction to Data Science

Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)

data science

Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.

Career in Data Science

Data science is a field that focuses on analyzing large amounts of data using tools and techniques to discover patterns and make business decisions. Data scientists utilize machine learning algorithms to develop predictive models from multiple sources of data in different formats. Data has become a valuable asset like oil in the 21st century that can help organizations improve decision making. The career is expected to grow exponentially and data scientists can earn more than average IT workers.

Ch1IntroductiontoDataScience.pptx

Data Science involves extracting insights from vast amounts of data using scientific methods and algorithms. It includes concepts like Statistics, Visualization, Machine Learning, and Deep Learning. The Data Science process goes through steps like Discovery, Preparation, Modeling, and Communication. Important roles include Data Scientist, Engineer, Analyst, and Statistician. Tools include R, SQL, Python, and SAS. Applications are in search, recommendations, recognition, gaming, and pricing. The main challenge is the variety of information and data required.

Data Science- Basics.pptx

Data science involves extracting insights from vast amounts of data using scientific methods and algorithms. It includes concepts like statistics, visualization, machine learning, and deep learning. The data science process includes steps like data discovery, preparation, modeling, and operationalizing results. Important roles include data scientist, engineer, analyst, and statistician. Tools include R, SQL, Python, and SAS. Applications are in internet search, recommendations, image recognition, gaming, and price comparison. The main challenge is obtaining a high variety of information and data for accurate analysis.

introduction to data science

Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio

Introduction to data science club

Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.

Introduction to Data Science

This document provides an overview of a data science course. It discusses topics like big data, data science components, use cases, Hadoop, R, and machine learning. The course objectives are to understand big data challenges, implement big data solutions, learn about data science components and prospects, analyze use cases using R and Hadoop, and understand machine learning concepts. The document outlines the topics that will be covered each day of the course including big data scenarios, introduction to data science, types of data scientists, and more.

Introduction to data science

Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.

Introduction to Data Science

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.

Data science presentation

This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.

Data science presentation 2nd CI day

A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.

Data science applications and usecases

The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.

Data science & data scientist

This document discusses the roles of data science and data scientists. It states that data science involves specialized skills in statistics, mathematics, programming, and computer science. A data scientist explores different data sources to discover hidden insights that can provide competitive advantages or address business problems. They are inquisitive individuals who can analyze data from multiple angles and recommend ways to apply findings to business challenges.

What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...

This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc

Introduction To Data Science

1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management.
2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data.
3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.

Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...

This document outlines an agenda for a data science training presentation. The agenda includes sections on why data science, what data science is, who a data scientist is, what they do, how to solve problems in data science, data science tools, and a demo. Key points are that data science uses tools, algorithms and machine learning to discover patterns in raw data and gain insights. It involves tasks like processing, cleaning, mining and modeling data, as well as communicating results. The problem solving process involves discovery, preparation, planning, building, operationalizing and communicating models.

Introduction on Data Science

The document describes a 10 module data science course covering topics such as introduction to data science, machine learning techniques using R, Hadoop architecture, and Mahout algorithms. The course includes live online classes, recorded lectures, quizzes, projects, and a certificate. Each module covers specific data science topics and techniques. The document provides details on the course content, objectives, and topics covered in module 1 which includes an introduction to data science, its components, use cases, and how to integrate R and Hadoop. Examples of data science applications in various domains like healthcare, retail, and social media are also presented.

Introduction of data science

Data science combines fields like statistics, programming, and domain expertise to extract meaningful insights from data. It involves preparing, analyzing, and modeling data to discover useful information. Exploratory data analysis is the process of investigating data to understand its characteristics and check assumptions before modeling. There are four types of EDA: univariate non-graphical, univariate graphical, multivariate non-graphical, and multivariate graphical. Python and R are popular tools used for EDA due to their data analysis and visualization capabilities.

Data Science Training | Data Science Tutorial | Data Science Certification | ...

This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2

What Is Data Science? | Introduction to Data Science | Data Science For Begin...

This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields

Introduction to Data Science

This document provides an introduction to data science and analytics. It discusses why data science jobs are in high demand, what skills are needed for these roles, and common types of analytics including descriptive, predictive, and prescriptive. It also covers topics like machine learning, big data, structured vs unstructured data, and examples of companies that utilize data and analytics like Amazon and Facebook. The document is intended to explain key concepts in data science and why attending a talk on this topic would be beneficial.

Introduction to Data Science

Two hour lecture I gave at the Jyväskylä Summer School. The purpose of the talk is to give a quick non-technical overview of concepts and methodologies in data science. Topics include a wide overview of both pattern mining and machine learning.
See also Part 2 of the lecture: Industrial Data Science. You can find it in my profile (click the face)

data science

Data science uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. It unifies statistics, data analysis, machine learning and related methods. Data science is important for business as it can turn ideas from science fiction into reality and help make predictions and decisions using predictive analytics, machine learning and analyzing vast amounts of business data. Data science projects involve tasks like data cleaning, exploratory analysis, visualization, machine learning and communication. Data science education is evolving to produce professionals with skills in computer science, information science, and statistics.

Career in Data Science

Data science is a field that focuses on analyzing large amounts of data using tools and techniques to discover patterns and make business decisions. Data scientists utilize machine learning algorithms to develop predictive models from multiple sources of data in different formats. Data has become a valuable asset like oil in the 21st century that can help organizations improve decision making. The career is expected to grow exponentially and data scientists can earn more than average IT workers.

introduction to data science

introduction to data science

Introduction to data science club

Introduction to data science club

Introduction to Data Science

Introduction to Data Science

Introduction to data science

Introduction to data science

Introduction to Data Science

Introduction to Data Science

Data science presentation

Data science presentation

Data science presentation 2nd CI day

Data science presentation 2nd CI day

Data science applications and usecases

Data science applications and usecases

Data science & data scientist

Data science & data scientist

What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...

What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...

Introduction To Data Science

Introduction To Data Science

Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...

Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...

Introduction on Data Science

Introduction on Data Science

Introduction of data science

Introduction of data science

Data Science Training | Data Science Tutorial | Data Science Certification | ...

Data Science Training | Data Science Tutorial | Data Science Certification | ...

What Is Data Science? | Introduction to Data Science | Data Science For Begin...

What Is Data Science? | Introduction to Data Science | Data Science For Begin...

Introduction to Data Science

Introduction to Data Science

Introduction to Data Science

Introduction to Data Science

data science

data science

Career in Data Science

Career in Data Science

Ch1IntroductiontoDataScience.pptx

Data Science involves extracting insights from vast amounts of data using scientific methods and algorithms. It includes concepts like Statistics, Visualization, Machine Learning, and Deep Learning. The Data Science process goes through steps like Discovery, Preparation, Modeling, and Communication. Important roles include Data Scientist, Engineer, Analyst, and Statistician. Tools include R, SQL, Python, and SAS. Applications are in search, recommendations, recognition, gaming, and pricing. The main challenge is the variety of information and data required.

Data Science- Basics.pptx

Data science involves extracting insights from vast amounts of data using scientific methods and algorithms. It includes concepts like statistics, visualization, machine learning, and deep learning. The data science process includes steps like data discovery, preparation, modeling, and operationalizing results. Important roles include data scientist, engineer, analyst, and statistician. Tools include R, SQL, Python, and SAS. Applications are in internet search, recommendations, image recognition, gaming, and price comparison. The main challenge is obtaining a high variety of information and data for accurate analysis.

Ci2004-10.doc

This document provides information about a computational intelligence and soft computing course including the instructor's contact information, class times, required text, and an overview of upcoming lectures on data mining with neural networks. It then discusses key issues in data mining such as theory, methods/algorithms, processes, applications, and tools/techniques. Several example data mining projects are also summarized along with homework and exam due dates for the course.

Introduction to Data Science: Unveiling Insights Hidden in Data

Embark on a journey into the fascinating field of Data Science and uncover the valuable insights concealed within vast datasets. In this article, we explore the fundamental concepts of Data Science and its applications. Discover how a Data science Training Institute in Jaipur, Lucknow, Indore, Mumbai, Delhi, Noida, Gurgaon and other cities in India can equip you with the knowledge and skills to analyze, interpret, and extract meaningful information from data. Explore topics such as data preprocessing, statistical analysis, machine learning, and data visualization. Join us on this enlightening exploration of the world of Data Science.

Introduction To Data Science

Data science is an interdisciplinary field (it consists of more than one branch of study) that uses statistics, computer science, and machine learning algorithms to gain insights from structured and unstructured data. CETPA INFOTECH, an ISO 9001- 2008 certified training company provides Data Science Training Course for students and professionals who want to make their mark in the world of Data Science. Cetpa is the best data science training institute in Delhi NCR.

Data Science Unit1 AMET.pdf

Data science involves extracting meaningful insights from raw data through scientific methods and algorithms. It is an interdisciplinary field that focuses on analyzing large datasets using skills from computer science, mathematics, and statistics. Python is a commonly used programming language for data science due to its powerful libraries for tasks like data analysis, machine learning, and visualization. Key Python libraries include NumPy, Pandas, Matplotlib, Scikit-learn, and SciPy. The document then discusses tools, applications, and basic concepts in data science and Python.

Introduction to Data Science.pdf

Covers the introduction of Data Science, applications of data science, role of data scientist, role of data analyst.

A Deep Dissertion Of Data Science Related Issues And Its Applications

This document summarizes a paper on data science that discusses its definition, processes, applications, and open research issues. It defines data science as extracting, collecting, and analyzing data for business or technical purposes. The paper describes the typical data science process as involving data wrangling, analysis, and communication. It discusses applications of data science in areas like business analytics, prediction, and healthcare. Finally, it outlines open research issues involving integrating data science with emerging technologies like the Internet of Things, cloud computing, and quantum computing.

Data science Nagarajan and madhav.pptx

This document summarizes a presentation on data science. It includes details about the presenters, date, time and login details for a seminar on data science. It then provides definitions and explanations of key concepts in data science including machine learning, deep learning, statistics and visualization. It describes common data science jobs and roles and lists popular tools used in data science. Finally, it discusses applications of data science and some challenges in the field.

Colloquium(7)_DataScience:ShivShaktiGhosh&MohitGarg

Huge amount of data is being collected everywhere - when we browse the web, go to the doctor's clinic, visit the supermarket, tweet or watch a movie. This plethora of data is dealt under a new realm called Data Science. Data Science is now recognized as a highly-critical growing area with impact across many sectors including science, government, finance, health care, social networks, manufacturing, advertising, retail,
and others. This colloquium will try to provide an overview as well as clarify bits and bats about this emerging field.

Best Data Science course in Delhi HTS institute

Embarking on a data science course involves delving into a comprehensive curriculum that covers fundamental theories, advanced techniques, and practical applications. It's not just about learning the theories; practical application plays a pivotal role in mastering this field. The course encompasses statistical analysis, machine learning, data visualization, and much more.

Data Science: Unlocking Insights and Transforming Industries

Data science is an interdisciplinary field that encompasses a range of techniques, algorithms, and tools to extract valuable insights and knowledge from data.

Data Science Demystified_ Journeying Through Insights and Innovations

In the digital age, data has emerged as one of the most valuable resources, driving decision-making processes across industries. Data science, the interdisciplinary field that extracts insights and knowledge from structured and unstructured data, plays a pivotal role in leveraging this resource. This section provides an overview of data science, its importance, and its applications in various domains.

Untitled document.pdf

Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov

data science

Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from data. It unifies statistics, data analysis, machine learning and related methods. Data science is the future of artificial intelligence and can add value to businesses by turning ideas seen in movies into reality. It involves working with large data sets and machine learning. Data science is primarily used for decisions, predictions, and machine learning by uncovering findings from data. Data science and technology delivers methods for solving data-intensive problems ranging from research to software deployment. Feature engineering is selecting or generating useful columns for modeling. Data cleaning takes up most of a data scientist's time along with exploratory analysis, visualization, machine learning, and communication. Data science education

Lecture_1_Intro_toDS&AI.pptx

The document provides an introduction to a course on data science and artificial intelligence. The course objectives are to expose students to fundamental concepts of data science using Python programming, introduce required mathematics foundations, explore data pre-processing techniques, summarize exploratory data analysis, and understand AI approaches in data science. It lists textbooks and references for the course and provides introductory information on topics like big data, the data science workflow, data science jobs and skills, challenges in data science, and what data scientists actually do in their work.

Data science course in ameerpet Hyderabad

Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.

best data science course institutes in Hyderabad

Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.

Data Science course in Hyderabad .

Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.

Data Science course in Hyderabad .Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.

Ch1IntroductiontoDataScience.pptx

Ch1IntroductiontoDataScience.pptx

Data Science- Basics.pptx

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Ci2004-10.doc

Ci2004-10.doc

Introduction to Data Science: Unveiling Insights Hidden in Data

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Data Science Unit1 AMET.pdf

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Introduction to Data Science.pdf

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A Deep Dissertion Of Data Science Related Issues And Its Applications

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Best Data Science course in Delhi HTS institute

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data science

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Lecture_1_Intro_toDS&AI.pptx

Lecture_1_Intro_toDS&AI.pptx

Data science course in ameerpet Hyderabad

Data science course in ameerpet Hyderabad

best data science course institutes in Hyderabad

best data science course institutes in Hyderabad

Data Science course in Hyderabad .

Data Science course in Hyderabad .

Data Science course in Hyderabad .

Data Science course in Hyderabad .

2001_Book_HumanChromosomes - Genéticapdf

Livro sobre Cromossomos Humanos / Genética

Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...

A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!

Pests of Storage_Identification_Dr.UPR.pdf

InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
100speciesofinsectpestsofstoredproductscauseeconomiclosses

Sustainable Land Management - Climate Smart Agriculture

Sustainable Land Management - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office

PPT on Sustainable Land Management presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Direct Seeded Rice - Climate Smart Agriculture

Direct Seeded Rice - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office

PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Gadgets for management of stored product pests_Dr.UPR.pdf

Insectsplayamajorroleinthedeteriorationoffoodgrainscausingbothquantitativeandqualitativelosses
Wellprovedthatnogranariescanbefilledwithgrainswithoutinsectsastheharvestedproducecontainegg(or)larvae(or)pupae(or)adultinsectinthembecauseoffieldcarryoverinfestationwhichcannotbeavoidedindevelopingcountrieslikeIndia
Simpletechnologiesfortimelydetectionofinsectsinthestoredproduceandtherebyplantimelycontrolmeasures

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team

Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.Alternate Wetting and Drying - Climate Smart Agriculture

Alternate Wetting and Drying - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office

PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024. Male reproduction physiology by Suyash Garg .pptx

Physiology of Male reproduction.
Video mentioned at page no. 23 as summary for better understanding

CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)

Description:
Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
Key topics covered include:
Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties.
Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties.
Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications.
Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism.
Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena.
With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation

Signatures of wave erosion in Titan’s coasts

The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.

Randomised Optimisation Algorithms in DAPHNE

Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/

BIOTRANSFORMATION MECHANISM FOR OF STEROID

BIOTRANSFORMATION MECHANISM FOR OF STEROID

Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf

Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.

Farming systems analysis: what have we learnt?.pptx

Presentation given at the official farewell of Prof Ken Gillet at Wageningen on 13 June 2024

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Methods of grain storage Structures in India.pdf

•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate

Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...

Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...

2001_Book_HumanChromosomes - Genéticapdf

2001_Book_HumanChromosomes - Genéticapdf

Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...

Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...

Pests of Storage_Identification_Dr.UPR.pdf

Pests of Storage_Identification_Dr.UPR.pdf

Sustainable Land Management - Climate Smart Agriculture

Sustainable Land Management - Climate Smart Agriculture

Direct Seeded Rice - Climate Smart Agriculture

Direct Seeded Rice - Climate Smart Agriculture

Gadgets for management of stored product pests_Dr.UPR.pdf

Gadgets for management of stored product pests_Dr.UPR.pdf

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...

ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...

Alternate Wetting and Drying - Climate Smart Agriculture

Alternate Wetting and Drying - Climate Smart Agriculture

Male reproduction physiology by Suyash Garg .pptx

Male reproduction physiology by Suyash Garg .pptx

CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)

CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Signatures of wave erosion in Titan’s coasts

Signatures of wave erosion in Titan’s coasts

Randomised Optimisation Algorithms in DAPHNE

Randomised Optimisation Algorithms in DAPHNE

BIOTRANSFORMATION MECHANISM FOR OF STEROID

BIOTRANSFORMATION MECHANISM FOR OF STEROID

Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf

Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf

Farming systems analysis: what have we learnt?.pptx

Farming systems analysis: what have we learnt?.pptx

Clinical periodontology and implant dentistry 2003.pdf

Clinical periodontology and implant dentistry 2003.pdf

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

Methods of grain storage Structures in India.pdf

Methods of grain storage Structures in India.pdf

- 1. Data Science(502A) Introduction to Data Science Presented by Sourav Sadhukhan Student Code-BWU/MCA/18/050
- 2. Data Science Data Science is the science which uses computer science, statistics and machine learning, visualization and human- computer interactions to collect, clean, integrate, analyze, visualize, interact with data to create data products.
- 3. Introduction to Data Science Data science is a deep study of the massive amount of data, which involves extracting meaningful insights from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms. It is a multidisciplinary field that uses tools and techniques to manipulate the data so that you can find something new and meaningful. Data science uses the most powerful hardware, programming systems, and most efficient algorithms to solve the data related problems. It is the future of artificial intelligence.
- 4. Data Science Lifecycle 1.Discovery: The first phase is discovery, which involves asking the right questions. 2. Data preparation: Data preparation is also known as Data Munging. In this phase, we need to perform the following tasks Data cleaning, Data Reduction, Data integration, Data transformation, 3. Model Planning: SQL Analysis Services,R,SAS,Python 4. Model-building: SAS Enterprise Miner WEKA,SPCS Modeler,MATLAB 5. Operationalize: In this phase, we will deliver the final reports of the project, along with briefings, code, and technical documents. 6. Communicate results: In this phase, we will check if we reach the goal, which we have set on the initial phase.
- 5. Data Science Components 1. Statistics: Statistics is one of the most important components of data science. Statistics is a way to collect and analyze the numerical data in a large amount and finding meaningful insights from it. 2. Domain Expertise: In data science, domain expertise binds data science together. Domain expertise means specialized knowledge or skills of a particular area. In data science, there are various areas for which we need domain experts. 3. Data engineering: Data engineering is a part of data science, which involves acquiring, storing, retrieving, and transforming the data. Data engineering also includes metadata (data about data) to the data. 4. Visualization: Data visualization is meant by representing data in a visual context so that people can easily understand the significance of data. Data visualization makes it easy to access the huge amount of data in visuals. 5. Advanced computing: Heavy lifting of data science is advanced computing. Advanced computing involves designing, writing, debugging, and maintaining the source code of computer programs.
- 6. Prerequisite for Data Science Non-Technical Prerequisite: Curiosity Critical Thinking Communication skills Technical Prerequisite: Machine learning Mathematical modeling Statistics Computer programming Databases
- 7. Applications of Data Science: Image recognition and speech recognition Gaming world Internet search Transport Healthcare Recommendation systems Risk detection
- 8. Tools for Data Science Data Analysis tools: R, Python, Statistics, SAS, Jupyter, R Studio, MATLAB, Excel, RapidMiner. Data Warehousing: ETL, SQL, Hadoop, Informatica/Talend, AWS Redshift Data Visualization tools: R, Jupyter, Tableau, Cognos. Machine learning tools: Spark, Mahout, Azure ML studio.
- 9. Thank You