Introduction to Data Science 1115.pptx
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This document provides an introduction to data science, including an overview of big data challenges, the field of data science, data scientists' roles, and examples of data science applications. It defines data science as an interdisciplinary field that uses theories from computer science, mathematics, and statistics to analyze large amounts of data and extract knowledge. Data scientists work in various industries and help make decisions by discovering patterns in data. The document also outlines some common data science coursework and career paths.
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Introduction to Data Science 1114.pptx
This document provides an introduction to data science, including an overview of big data challenges, the field of data science, and the role of data scientists. It defines big data as data that is expensive to manage and difficult to extract value from due to issues of volume, velocity, variety and complexity. It also outlines different types of data including relational, text, semi-structured, graph and streaming data. The document discusses how data science uses theories and techniques from fields like computer science, mathematics and statistics to analyze large amounts of data and help decision makers. It notes the growing demand for data scientists and provides some real-life examples of data science applications.
Introduction to Data Science 1118.pptx
This document provides an introduction to data science, including an overview of big data challenges, the definition and goals of data science, examples of data types, and potential applications. It discusses how data science manages and analyzes large, complex data to extract knowledge. Data scientists are key to realizing opportunities in big data by bringing structure, finding patterns, and advising executives. The document outlines a data science program and the types of courses that could be included.
Introduction to Data Science 112.pptx
This document provides an introduction to data science, including an overview of data science concepts and applications. It discusses how data science can be used to analyze large amounts of data ("big data") collected from various sources such as websites, financial transactions, social networks, and more. The document also describes data scientists' role in managing and extracting knowledge from data using techniques like aggregation, statistics, data mining, and statistical modeling. Finally, it provides examples of how data science is applied in different fields like business, healthcare, national security, and engineering.
Introduction to Data Science 1119.pptx
This document provides an introduction to data science, including an overview of data science concepts and the role of data scientists. It discusses how data science can be used to analyze large amounts of diverse data ("big data") from various sources like the web, financial transactions, social networks, and more. The document also outlines some common tasks of data scientists, such as performing aggregation, statistics, data mining, and statistical modeling to discover patterns and extract knowledge from data. Data scientists help organizations understand and utilize insights from big data to inform products, processes, and decision-making.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science. It discusses the growth of big data from sources like the web, financial transactions, and social media. Data science aims to extract knowledge and insights from vast amounts of complex data using techniques from computer science, mathematics, and statistics. The document outlines the roles and skills of data scientists, who apply these techniques to analyze data and help organizations make better decisions. They work in many industries like national security, business, healthcare, and more. The document recommends coursework in mathematics, statistics, programming, data mining, and machine learning to prepare for a career in data science.
Introduction to Data Science 5-13.pptx
This document discusses data science and the challenges of big data. It notes that data science uses theories from fields like computer science, mathematics, and statistics to analyze large amounts of data. Data scientists are key to realizing opportunities in big data by finding patterns and insights to help decision makers. The document outlines some example courses that could be part of a data science concentration, including mathematics, statistics, programming, data mining, and machine learning.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science, including:
- The large amounts of data being collected from sources like the web, financial transactions, and social networks.
- How "big data" refers to data that is expensive to manage and hard to extract value from due to its volume, velocity, and variety.
- What data scientists do, such as finding patterns and stories in data to help decision makers, and how data science draws from fields like computer science, mathematics, and statistics.
Introduction to Data Science
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization's data. These insights can be used to guide decision making and strategic planning.
Skill Shiksha - India’s Best Online Learning Platform
Take Online Courses with India’s Best Online Learning Platform. Learn Digital Marketing, Data Science and more.
100% Placement Assistance
<a href="https://www.skillshiksha.com/"> best online learning platforms>
Recommended
Introduction to Data Science 1114.pptx
This document provides an introduction to data science, including an overview of big data challenges, the field of data science, and the role of data scientists. It defines big data as data that is expensive to manage and difficult to extract value from due to issues of volume, velocity, variety and complexity. It also outlines different types of data including relational, text, semi-structured, graph and streaming data. The document discusses how data science uses theories and techniques from fields like computer science, mathematics and statistics to analyze large amounts of data and help decision makers. It notes the growing demand for data scientists and provides some real-life examples of data science applications.
Introduction to Data Science 1118.pptx
This document provides an introduction to data science, including an overview of big data challenges, the definition and goals of data science, examples of data types, and potential applications. It discusses how data science manages and analyzes large, complex data to extract knowledge. Data scientists are key to realizing opportunities in big data by bringing structure, finding patterns, and advising executives. The document outlines a data science program and the types of courses that could be included.
Introduction to Data Science 112.pptx
This document provides an introduction to data science, including an overview of data science concepts and applications. It discusses how data science can be used to analyze large amounts of data ("big data") collected from various sources such as websites, financial transactions, social networks, and more. The document also describes data scientists' role in managing and extracting knowledge from data using techniques like aggregation, statistics, data mining, and statistical modeling. Finally, it provides examples of how data science is applied in different fields like business, healthcare, national security, and engineering.
Introduction to Data Science 1119.pptx
This document provides an introduction to data science, including an overview of data science concepts and the role of data scientists. It discusses how data science can be used to analyze large amounts of diverse data ("big data") from various sources like the web, financial transactions, social networks, and more. The document also outlines some common tasks of data scientists, such as performing aggregation, statistics, data mining, and statistical modeling to discover patterns and extract knowledge from data. Data scientists help organizations understand and utilize insights from big data to inform products, processes, and decision-making.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science. It discusses the growth of big data from sources like the web, financial transactions, and social media. Data science aims to extract knowledge and insights from vast amounts of complex data using techniques from computer science, mathematics, and statistics. The document outlines the roles and skills of data scientists, who apply these techniques to analyze data and help organizations make better decisions. They work in many industries like national security, business, healthcare, and more. The document recommends coursework in mathematics, statistics, programming, data mining, and machine learning to prepare for a career in data science.
Introduction to Data Science 5-13.pptx
This document discusses data science and the challenges of big data. It notes that data science uses theories from fields like computer science, mathematics, and statistics to analyze large amounts of data. Data scientists are key to realizing opportunities in big data by finding patterns and insights to help decision makers. The document outlines some example courses that could be part of a data science concentration, including mathematics, statistics, programming, data mining, and machine learning.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science, including:
- The large amounts of data being collected from sources like the web, financial transactions, and social networks.
- How "big data" refers to data that is expensive to manage and hard to extract value from due to its volume, velocity, and variety.
- What data scientists do, such as finding patterns and stories in data to help decision makers, and how data science draws from fields like computer science, mathematics, and statistics.
Introduction to Data Science
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization's data. These insights can be used to guide decision making and strategic planning.
Skill Shiksha - India’s Best Online Learning Platform
Take Online Courses with India’s Best Online Learning Platform. Learn Digital Marketing, Data Science and more.
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<a href="https://www.skillshiksha.com/"> best online learning platforms>
Introduction to Data Science
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization's data. These insights can be used to guide decision making and strategic planning.
Skill Shiksha - India’s Best Online Learning Platform
Take Online Courses with India’s Best Online Learning Platform. Learn Digital Marketing, Data Science and more.
100% Placement Assistance
<a href="https://www.skillshiksha.com/"> best online learning platforms>
Introduction to Data Science\
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization's data. These insights can be used to guide decision making and strategic planning.
Skill Shiksha - India’s Best Online Learning Platform
Take Online Courses with India’s Best Online Learning Platform. Learn Digital Marketing, Data Science and more.
100% Placement Assistance
<a href="https://www.skillshiksha.com/"> best online learning platforms>
mkol.pptx
1) Data science is a multidisciplinary field that uses theories and techniques from computer science, mathematics, and statistics to analyze large amounts of data and extract knowledge from it.
2) The amount of data being collected is growing exponentially from sources like the web, financial transactions, social media, and more. Analyzing this "big data" presents new challenges and opportunities.
3) Data scientists play a key role in making sense of big data and advising organizations on insights that can improve products, processes and decisions across many industries. They find patterns and stories in data to help decision makers.
Introduction to Data Science
Introduction to Data Science
Data science is a field that involves using statistical and computational techniques to extract insights and knowledge from data. It encompasses a wide range of tasks, including data cleaning and preparation, data visualization, statistical modeling, machine learning, and more.
hjol.pptx
Data science is an interdisciplinary field that uses techniques from computer science, statistics, mathematics and domain knowledge to extract insights from vast amounts of data. Data science has become highly desirable due to the abundance of data being collected from various sources such as the web, financial transactions, social networks, and scientific projects. Data scientists analyze this 'big data' to discover patterns and make predictions to help organizations make better decisions. They bring structure to data through techniques such as data mining, machine learning, and statistical modeling.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science, including an overview of big data challenges, what data science is, why it is an important field, and what data scientists do. It discusses the large amount of data being collected from various sources and the need to extract knowledge and value from this data. The document also outlines some example courses that could be included in a data science concentration to provide students with the necessary mathematics, statistics, programming, and machine learning skills to work in this field.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science, including an overview of big data challenges, what data science is, why it is an important field, and what data scientists do. It discusses the large amount of data being collected from various sources and the need to extract value and insights from this data. The document also outlines some example courses that could be included in a data science concentration to provide students with the necessary mathematical, statistical, programming, and machine learning skills to work in this field.
Opportunities in Data Science.ppt
This document discusses opportunities in the field of data science. It begins by outlining how large amounts of data are being collected from various sources like the web, financial transactions, social networks, and more. It then defines what constitutes "big data" in terms of volume, velocity, and variety. The document introduces data science and explains that it is a multidisciplinary field that uses theories and techniques from computer science, mathematics, and statistics to analyze large datasets and extract knowledge. It provides examples of how data science is used in national security, business analytics, healthcare, and other domains. Finally, it outlines the types of concentrations and skills needed for a career in data science, such as mathematics, statistics, programming, data mining,
Introduction to Data Science 1121.pptx
This document provides an introduction to data science. It discusses the challenges posed by big data and how data science addresses these challenges by managing, manipulating, extracting, and interpreting knowledge from large amounts of data. Data science is a multidisciplinary field that applies to both big and small data. The document also discusses data scientists, what they do, and examples of data science applications in different industries like business, healthcare, and national security.
Introduction to Data Science 1113.pptx
Data science is a multidisciplinary field that uses theories and techniques from computer science, mathematics, and statistics to extract knowledge from large amounts of data. It has become an important area due to the exponential growth of data from sources like the web, financial transactions, social networks, and scientific projects. Data scientists analyze this "big data" to discover patterns and make decisions in many domains including business, science, engineering, healthcare, politics and more. They are key to unlocking the value within large and complex datasets. The field of data science has become very popular in recent years and is projected to be in high demand, leading to it being called "the sexiest job of the 21st century."
Big Data Ethics
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
Semantic Web Investigation within Big Data Context
This document discusses how the semantic web can help address challenges associated with big data. It describes the 5 V's of big data: volume, variety, velocity, veracity, and value. For each V, it outlines related challenges in data acquisition, integration, and analysis. The document argues that semantic web concepts like ontologies, linked data, and reasoning can help solve problems of data heterogeneity, scale, and timeliness across different phases of the big data analysis pipeline, in order to ultimately extract value from data.
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
Why is Data Science a Popular Career Choice.pdf
Do you want to become the backbone of big corporates and giant business groups around the world? Beginning your career trajectory by grabbing the perfect spot in the data science certification courses provided around the world. The US Bureau of Labor Statistics projects 35.8% employment growth for data scientists till 2031, over a decade period beginning 2021. The growing use of machine learning and artificial intelligence technologies is another factor driving the demand for professionals skilled in data science.
Brimming with humungous career opportunities, the data science industry is set in motion to yield multitudinous growth opportunities across diversified industries worldwide. By automating procedures, increasing effectiveness, and allowing predictive capabilities, Artificial intelligence and machine learning algorithms hold the ability to change the entire landscape.
Data Science has become a fascinating career choice that calls for working closely with cutting-edge technology and addressing challenges. If you are someone who wishes to work with humungous data, has a passion for numbers, and has a clear vision of setting their career on a thriving path; data science is the right pick for you!
A diversified array of organizations is actively looking for data-hungry professionals who are coarsely skilled at data science to analyze data and churn out business decisions for the greater good of the company. Today is the ripe time to get started with a data science career, that promises an elevated trajectory and nothing else.
With the rise of technological innovations and industrial evolution, massive datasets become unmanageable. The future of such a massive explosion of data calls for an urgent appointment of qualified data scientists; enabling bigger business moves. This is where getting certified in the field makes sense.
Without wasting any further time, it is an advisable move to get certified in key data science skills that are sure to rage in the industry worldwide. Begin with the most trusted names in the data science certifications providers industry today!
https://www.usdsi.org/data-science-insights/why-is-data-science-a-popular-career-choice
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The term 'Data Scientist' arose fairly recently to express the specialised recruitment needs of certain well-known data-driven Silicon Valley firms. It signifies a mix of diverse and rare talents, mostly drawing from Computer Science (with emphasis on Big Data), Statistics and Machine Learning. In this talk, we will attempt to briefly survey the state-of-the-art both in terms of problems and solutions at the vanguard of Data Science. We will cover both novel developments, as well as centuries-old best practices, in an attempt to demonstrate that Data Science is indeed a Science, in the full sense of the word. This talk represents part of a seminar series that the speaker has given across the world, including Google (Mountainview), Cisco (San Jose) and Aviva Headquarters (London), and represents joint work with Professor David Hand (OBE).
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1) Data science is a multidisciplinary field that uses theories and techniques from computer science, mathematics, and statistics to analyze large amounts of data and extract knowledge from it.
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hjol.pptx
Data science is an interdisciplinary field that uses techniques from computer science, statistics, mathematics and domain knowledge to extract insights from vast amounts of data. Data science has become highly desirable due to the abundance of data being collected from various sources such as the web, financial transactions, social networks, and scientific projects. Data scientists analyze this 'big data' to discover patterns and make predictions to help organizations make better decisions. They bring structure to data through techniques such as data mining, machine learning, and statistical modeling.
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This document provides an introduction to data science, including an overview of big data challenges, what data science is, why it is an important field, and what data scientists do. It discusses the large amount of data being collected from various sources and the need to extract knowledge and value from this data. The document also outlines some example courses that could be included in a data science concentration to provide students with the necessary mathematics, statistics, programming, and machine learning skills to work in this field.
Introduction to Data Science 5-13.pptx
This document provides an introduction to data science, including an overview of big data challenges, what data science is, why it is an important field, and what data scientists do. It discusses the large amount of data being collected from various sources and the need to extract value and insights from this data. The document also outlines some example courses that could be included in a data science concentration to provide students with the necessary mathematical, statistical, programming, and machine learning skills to work in this field.
Opportunities in Data Science.ppt
This document discusses opportunities in the field of data science. It begins by outlining how large amounts of data are being collected from various sources like the web, financial transactions, social networks, and more. It then defines what constitutes "big data" in terms of volume, velocity, and variety. The document introduces data science and explains that it is a multidisciplinary field that uses theories and techniques from computer science, mathematics, and statistics to analyze large datasets and extract knowledge. It provides examples of how data science is used in national security, business analytics, healthcare, and other domains. Finally, it outlines the types of concentrations and skills needed for a career in data science, such as mathematics, statistics, programming, data mining,
Introduction to Data Science 1121.pptx
This document provides an introduction to data science. It discusses the challenges posed by big data and how data science addresses these challenges by managing, manipulating, extracting, and interpreting knowledge from large amounts of data. Data science is a multidisciplinary field that applies to both big and small data. The document also discusses data scientists, what they do, and examples of data science applications in different industries like business, healthcare, and national security.
Introduction to Data Science 1113.pptx
Data science is a multidisciplinary field that uses theories and techniques from computer science, mathematics, and statistics to extract knowledge from large amounts of data. It has become an important area due to the exponential growth of data from sources like the web, financial transactions, social networks, and scientific projects. Data scientists analyze this "big data" to discover patterns and make decisions in many domains including business, science, engineering, healthcare, politics and more. They are key to unlocking the value within large and complex datasets. The field of data science has become very popular in recent years and is projected to be in high demand, leading to it being called "the sexiest job of the 21st century."
Big Data Ethics
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
Semantic Web Investigation within Big Data Context
This document discusses how the semantic web can help address challenges associated with big data. It describes the 5 V's of big data: volume, variety, velocity, veracity, and value. For each V, it outlines related challenges in data acquisition, integration, and analysis. The document argues that semantic web concepts like ontologies, linked data, and reasoning can help solve problems of data heterogeneity, scale, and timeliness across different phases of the big data analysis pipeline, in order to ultimately extract value from data.
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
Why is Data Science a Popular Career Choice.pdf
Do you want to become the backbone of big corporates and giant business groups around the world? Beginning your career trajectory by grabbing the perfect spot in the data science certification courses provided around the world. The US Bureau of Labor Statistics projects 35.8% employment growth for data scientists till 2031, over a decade period beginning 2021. The growing use of machine learning and artificial intelligence technologies is another factor driving the demand for professionals skilled in data science.
Brimming with humungous career opportunities, the data science industry is set in motion to yield multitudinous growth opportunities across diversified industries worldwide. By automating procedures, increasing effectiveness, and allowing predictive capabilities, Artificial intelligence and machine learning algorithms hold the ability to change the entire landscape.
Data Science has become a fascinating career choice that calls for working closely with cutting-edge technology and addressing challenges. If you are someone who wishes to work with humungous data, has a passion for numbers, and has a clear vision of setting their career on a thriving path; data science is the right pick for you!
A diversified array of organizations is actively looking for data-hungry professionals who are coarsely skilled at data science to analyze data and churn out business decisions for the greater good of the company. Today is the ripe time to get started with a data science career, that promises an elevated trajectory and nothing else.
With the rise of technological innovations and industrial evolution, massive datasets become unmanageable. The future of such a massive explosion of data calls for an urgent appointment of qualified data scientists; enabling bigger business moves. This is where getting certified in the field makes sense.
Without wasting any further time, it is an advisable move to get certified in key data science skills that are sure to rage in the industry worldwide. Begin with the most trusted names in the data science certifications providers industry today!
https://www.usdsi.org/data-science-insights/why-is-data-science-a-popular-career-choice
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Introduction to Data Science 1115.pptx
- 1. Introduction to Data Science Kamal Al Nasr, Matthew Hayes and Jean-Claude Pedjeu Computer Science and Mathematical Sciences College of Engineering Tennessee State University
- 2. Outline Data, Big Data and Challenges Data Science Introduction Why Data Science Data Scientists What do they do? Major/Concentration in Data Science What courses to take.
- 3. Data All Around Lots of data is being collected and warehoused Web data, e-commerce Financial transactions, bank/credit transactions Online trading and purchasing Social Network
- 4. Big Data Big Data is any data that is expensive to manage and hard to extract value from Volume The size of the data Velocity The latency of data processing relative to the growing demand for interactivity Variety and Complexity the diversity of sources, formats, quality, structures.
- 5. Big Data
- 6. Types of Data We Have Relational Data (Tables/Transaction/Legacy Data) Text Data (Web) Semi-structured Data (XML) Graph Data Social Network, Semantic Web (RDF), … Streaming Data You can afford to scan the data once
- 7. What To Do With These Data? Aggregation and Statistics Data warehousing and OLAP Indexing, Searching, and Querying Keyword based search Pattern matching (XML/RDF) Knowledge discovery Data Mining Statistical Modeling
- 8. What is Data Science? An area that manages, manipulates, extracts, and interprets knowledge from tremendous amount of data Data science (DS) is a multidisciplinary field of study with goal to address the challenges in big data Data science principles apply to all data – big and small https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/
- 9. What is Data Science? Theories and techniques from many fields and disciplines are used to investigate and analyze a large amount of data to help decision makers in many industries such as science, engineering, economics, politics, finance, and education Computer Science Pattern recognition, visualization, data warehousing, High performance computing, Databases, AI Mathematics Mathematical Modeling Statistics Statistical and Stochastic modeling, Probability.
- 10. Why is it sexy? Gartner’s 2014 Hype Cycle
- 11. Data Science
- 12. Data Science
- 13. Real Life Examples Companies learn your secrets, shopping patterns, and preferences For example, can we know if a woman is pregnant, even if she doesn’t want us to know? Target case study Data Science and election (2008, 2012) 1 million people installed the Obama Facebook app that gave access to info on “friends”
- 14. Data Scientists Data Scientist The Sexiest Job of the 21st Century They find stories, extract knowledge. They are not reporters
- 15. Data Scientists Data scientists are the key to realizing the opportunities presented by big data. They bring structure to it, find compelling patterns in it, and advise executives on the implications for products, processes, and decisions
- 16. What do Data Scientists do? National Security Cyber Security Business Analytics Engineering Healthcare And more ….