Behind the AI curtain: Designing for trust in machine learning productsSoftware Guru
This session covers three key principles for how design and data science teams can work together better to build greater trust among users. Additionally, a case study on how a design and data science team partnered to redesign predictive analytics scores powered by machine learning will illustrate those principles in practice.
Por Crystal Yan
This is my experience of going to my first data hackathon, Govhack 2015 and what it taught me.
A Hackathon is an event where you gather a heap of resources and people, form small teams and try to deliver as fully realised solution to a set theme or problem in a short intense amount of time.
Normally a hackathon is focused on delivering working software, but in the case of a data hackathon you work from a heap of datasets and try to deliver something of value, that can be working software, but often is something else. For this reason non coders can participate in a data hack easily.
Another difference is a hackathon normally revolves around creating some sort of business (be that profit or non-profit) idea and validating it.
Data hackathons are about understanding and realising value from data, and that value can often just be delivering better access to the information the data represents.
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...Tata Consultancy Services
If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.
An introductory slide for people who are just getting into the field of Data Science to gain the understanding of why data science is important and how data scientist fits in the loop of the importance of Data Science to the industry
In this presentation from the Hurricane Electric Carrier Event, Rich Brueckner from insideBIGDATA describes what's really behind this phenomenon and why you should care.
Watch the video presentation: http://wp.me/p3RLEV-1r1
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
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.
Behind the AI curtain: Designing for trust in machine learning productsSoftware Guru
This session covers three key principles for how design and data science teams can work together better to build greater trust among users. Additionally, a case study on how a design and data science team partnered to redesign predictive analytics scores powered by machine learning will illustrate those principles in practice.
Por Crystal Yan
This is my experience of going to my first data hackathon, Govhack 2015 and what it taught me.
A Hackathon is an event where you gather a heap of resources and people, form small teams and try to deliver as fully realised solution to a set theme or problem in a short intense amount of time.
Normally a hackathon is focused on delivering working software, but in the case of a data hackathon you work from a heap of datasets and try to deliver something of value, that can be working software, but often is something else. For this reason non coders can participate in a data hack easily.
Another difference is a hackathon normally revolves around creating some sort of business (be that profit or non-profit) idea and validating it.
Data hackathons are about understanding and realising value from data, and that value can often just be delivering better access to the information the data represents.
TCS Point of View Session - Analyze by Dr. Gautam Shroff, VP and Chief Scient...Tata Consultancy Services
If insights are available from mass amounts of data, you require enormous agility across business units to act on these. Understand how your peers tackle such problems and what new approaches are available to businesses.
An introductory slide for people who are just getting into the field of Data Science to gain the understanding of why data science is important and how data scientist fits in the loop of the importance of Data Science to the industry
In this presentation from the Hurricane Electric Carrier Event, Rich Brueckner from insideBIGDATA describes what's really behind this phenomenon and why you should care.
Watch the video presentation: http://wp.me/p3RLEV-1r1
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
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.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
SkillsFuture Festival at NUS 2019- Machine Learning for HumansNUS-ISS
Presented by Mr Patrice Choong, Director, The Sandbox, Innovation & Entrepreneurship Office, Ngee Ann Polytechnic, at SkillsFuture Festival at NUS 2019
The document discusses the skills needed for the next generation of data scientists. It emphasizes that they will need a breadth of technical skills in areas like mathematics, statistics, machine learning, and coding. They also need strong soft skills, like leadership, communication, and time management. Next generation data scientists are advised to follow the scientific method, understand data properly, avoid irrelevant data, and consider the social impacts and ethics of their work. The document also provides examples of popular data science tools in areas like machine learning, querying, processing, and integrated development environments.
The document provides an overview of different career paths in data science, including data scientist, data engineer, and data analyst roles. It summarizes the typical job duties, skills required, tools used, and average salaries for each role. Additionally, it notes the large and growing demand for data science professionals, with over 215,000 open jobs in the US as of January 2017 and top hiring locations of San Francisco, New York, and Seattle.
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.
Thinkful - Intro to Data Science - Washington DCTJ Stalcup
This document discusses an introductory session on data science. It begins with introductions and an outline of the session's goals, which are to define what a data scientist is, how the field has emerged, and how to become one. It then discusses the growing demand and high salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn, Netflix, and for fighting Ebola. Key aspects of data science like big data, Hadoop, MapReduce, and machine learning algorithms are explained. The document concludes by discussing the data science process and tools used, and encourages the audience that it is possible for them to become data scientists with the right knowledge, skills, and learning approach.
Top 10 Myths Regarding Data Scientists Roles in India | EdurekaEdureka!
This document discusses common myths about data scientists. It addresses 10 myths, including that a PhD is always needed, that data scientists will be replaced by AI, and that more data always translates to higher accuracy. Each myth is presented with a number and graphic debunking it by explaining the reality of that aspect of the data science field.
2017 06-14-getting started with data scienceThinkful
The document provides an overview of getting started with a career in data science. It introduces the author Jasjit Singh and discusses what a data scientist does, how the field has emerged to analyze big data. Examples are given of how companies like LinkedIn and Uber use data science. The data science process is explained through the steps of framing a question, collecting and processing data, exploring patterns in the data, and communicating findings. Tools used include SQL, data visualization software, and machine learning algorithms. The document encourages the reader that becoming a data scientist is achievable through learning statistics, algorithms, and software skills.
Myths and Mathemagical Superpowers of Data ScientistsDavid Pittman
1) The document discusses 10 myths about data scientists and provides realities to counter each myth.
2) Some myths include claims that data scientists are mythical beings, elitist academics, or a fading trend. However, the realities note data science requires hands-on work with data and has experienced steady growth.
3) Other myths suggest data scientists are just statisticians or BI specialists, but the realities indicate data scientists come from varied backgrounds and tackle business problems through experimentation and analysis.
Myths and Mathemagical Superpowers of Data ScientistsIBM Analytics
Some people think data scientists are mythical beings, like unicorns, or they are some sort of nouveau fad that will quickly fade. Not true, says IBM big data evangelist James Kobielus. In this engaging presentation, with artwork created by Angela Tuminello, Kobielus debunks 10 myths about data scientists and their role in analytics and big data. You might also want to read the full blog by Kobielus that spawned this presentation: "Data Scientists: Myths and Mathemagical Superpowers" - http://ibm.co/PqF7Jn
For more information, visit http://www.ibmbigdatahub.com
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 is a multidisciplinary field that uses statistics, programming, and machine learning to extract knowledge and insights from large amounts of data. It has various applications like email spam detection, medical diagnosis, predicting stock prices, and self-driving cars. The document discusses how the size of data is rapidly increasing and will continue to do so, with an estimated 463 exabytes of new data generated daily by 2025. It also outlines common tasks performed by data scientists like understanding business problems, analyzing and visualizing data, making recommendations, and predicting future values. Theoretical and practical aspects of data science are also covered, along with examples of how it relates to other fields.
The document discusses putting "magic" into data science. It provides several tricks or techniques for data science, including collecting novel data sources, dimensionality reduction, Bayesian methods, bootstrapping statistics, and matrix factorizations. It also emphasizes the importance of reliability, latency/interactivity, simplicity/modularity, and unexpectedness to solve the "last mile" problem of getting people to actually use data science tools and models. Specific Facebook tools like Planout, Deltoid, ClustR, Prophet, and Hive/Presto/Scuba are presented as examples.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
The document defines data science as incorporating machine learning, data mining, capturing and cleaning unstructured data from sources like social media, using big data technologies to store and process large datasets, and considering ethics and regulation. It lists the key skills required of a data scientist as including communication, statistics, computer science, machine learning, data wrangling, visualization, and domain expertise. Common data science techniques are described as clustering, classification, association rule mining, and outlier detection.
iTrain Malaysia: Data Science by Tarun SukhaniiTrain
The document provides an overview of data science and opportunities in the field. It discusses what data science and big data are, key components of data science like the "4 V's" of big data, what a data scientist's skills and roles are. It also covers demand and opportunities in data science, giving examples of applications in different industries. It proposes an education framework for learning skills like coding, mathematics, statistics, machine learning and software engineering needed for a career in data science.
This document discusses data scientist profiles and provides guidance on building data scientist teams. It begins by establishing the importance of analytics for businesses. It then discusses the term "data scientist" and characterizes data scientists as having diverse backgrounds but being curious and asking important questions. The document outlines skills of data scientists and notes that while backgrounds vary, soft skills are very important. It provides tips for recruiting data scientists and emphasizes getting started with an analytical team even without perfect conditions.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
SkillsFuture Festival at NUS 2019- Machine Learning for HumansNUS-ISS
Presented by Mr Patrice Choong, Director, The Sandbox, Innovation & Entrepreneurship Office, Ngee Ann Polytechnic, at SkillsFuture Festival at NUS 2019
The document discusses the skills needed for the next generation of data scientists. It emphasizes that they will need a breadth of technical skills in areas like mathematics, statistics, machine learning, and coding. They also need strong soft skills, like leadership, communication, and time management. Next generation data scientists are advised to follow the scientific method, understand data properly, avoid irrelevant data, and consider the social impacts and ethics of their work. The document also provides examples of popular data science tools in areas like machine learning, querying, processing, and integrated development environments.
The document provides an overview of different career paths in data science, including data scientist, data engineer, and data analyst roles. It summarizes the typical job duties, skills required, tools used, and average salaries for each role. Additionally, it notes the large and growing demand for data science professionals, with over 215,000 open jobs in the US as of January 2017 and top hiring locations of San Francisco, New York, and Seattle.
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.
Thinkful - Intro to Data Science - Washington DCTJ Stalcup
This document discusses an introductory session on data science. It begins with introductions and an outline of the session's goals, which are to define what a data scientist is, how the field has emerged, and how to become one. It then discusses the growing demand and high salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn, Netflix, and for fighting Ebola. Key aspects of data science like big data, Hadoop, MapReduce, and machine learning algorithms are explained. The document concludes by discussing the data science process and tools used, and encourages the audience that it is possible for them to become data scientists with the right knowledge, skills, and learning approach.
Top 10 Myths Regarding Data Scientists Roles in India | EdurekaEdureka!
This document discusses common myths about data scientists. It addresses 10 myths, including that a PhD is always needed, that data scientists will be replaced by AI, and that more data always translates to higher accuracy. Each myth is presented with a number and graphic debunking it by explaining the reality of that aspect of the data science field.
2017 06-14-getting started with data scienceThinkful
The document provides an overview of getting started with a career in data science. It introduces the author Jasjit Singh and discusses what a data scientist does, how the field has emerged to analyze big data. Examples are given of how companies like LinkedIn and Uber use data science. The data science process is explained through the steps of framing a question, collecting and processing data, exploring patterns in the data, and communicating findings. Tools used include SQL, data visualization software, and machine learning algorithms. The document encourages the reader that becoming a data scientist is achievable through learning statistics, algorithms, and software skills.
Myths and Mathemagical Superpowers of Data ScientistsDavid Pittman
1) The document discusses 10 myths about data scientists and provides realities to counter each myth.
2) Some myths include claims that data scientists are mythical beings, elitist academics, or a fading trend. However, the realities note data science requires hands-on work with data and has experienced steady growth.
3) Other myths suggest data scientists are just statisticians or BI specialists, but the realities indicate data scientists come from varied backgrounds and tackle business problems through experimentation and analysis.
Myths and Mathemagical Superpowers of Data ScientistsIBM Analytics
Some people think data scientists are mythical beings, like unicorns, or they are some sort of nouveau fad that will quickly fade. Not true, says IBM big data evangelist James Kobielus. In this engaging presentation, with artwork created by Angela Tuminello, Kobielus debunks 10 myths about data scientists and their role in analytics and big data. You might also want to read the full blog by Kobielus that spawned this presentation: "Data Scientists: Myths and Mathemagical Superpowers" - http://ibm.co/PqF7Jn
For more information, visit http://www.ibmbigdatahub.com
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 is a multidisciplinary field that uses statistics, programming, and machine learning to extract knowledge and insights from large amounts of data. It has various applications like email spam detection, medical diagnosis, predicting stock prices, and self-driving cars. The document discusses how the size of data is rapidly increasing and will continue to do so, with an estimated 463 exabytes of new data generated daily by 2025. It also outlines common tasks performed by data scientists like understanding business problems, analyzing and visualizing data, making recommendations, and predicting future values. Theoretical and practical aspects of data science are also covered, along with examples of how it relates to other fields.
The document discusses putting "magic" into data science. It provides several tricks or techniques for data science, including collecting novel data sources, dimensionality reduction, Bayesian methods, bootstrapping statistics, and matrix factorizations. It also emphasizes the importance of reliability, latency/interactivity, simplicity/modularity, and unexpectedness to solve the "last mile" problem of getting people to actually use data science tools and models. Specific Facebook tools like Planout, Deltoid, ClustR, Prophet, and Hive/Presto/Scuba are presented as examples.
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
The document defines data science as incorporating machine learning, data mining, capturing and cleaning unstructured data from sources like social media, using big data technologies to store and process large datasets, and considering ethics and regulation. It lists the key skills required of a data scientist as including communication, statistics, computer science, machine learning, data wrangling, visualization, and domain expertise. Common data science techniques are described as clustering, classification, association rule mining, and outlier detection.
iTrain Malaysia: Data Science by Tarun SukhaniiTrain
The document provides an overview of data science and opportunities in the field. It discusses what data science and big data are, key components of data science like the "4 V's" of big data, what a data scientist's skills and roles are. It also covers demand and opportunities in data science, giving examples of applications in different industries. It proposes an education framework for learning skills like coding, mathematics, statistics, machine learning and software engineering needed for a career in data science.
This document discusses data scientist profiles and provides guidance on building data scientist teams. It begins by establishing the importance of analytics for businesses. It then discusses the term "data scientist" and characterizes data scientists as having diverse backgrounds but being curious and asking important questions. The document outlines skills of data scientists and notes that while backgrounds vary, soft skills are very important. It provides tips for recruiting data scientists and emphasizes getting started with an analytical team even without perfect conditions.
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The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
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5. Reality #2
Strong fundamentals,
hard work and lots of
patience would make a
data scientist.
“A data scientist is
someone who is better at
statistics than any software
engineer and better at
software engineering than
any statistician.”
8. Myth #4
A Data Scientist needs to
master SAS, Excel,
Hadoop, WEKA and
whatever they keep
dumping
9. Reality #4
Tools are used to reduce
the effort of a Data
Scientist. Focus should
be on applying the
techniques and methods
properly.
We believe 5% tools &
95% data techniques
10. Myth #5
You need a specialist to
teach each part of Data
Science
11. Reality #5
A Data Scientist is a
person who understands
well enough to combine
and apply the various
aspects of data
techniques.
12. Myth #6
A Data Scientist keeps
giving mind blowing
insights
13. Reality #6
In real-world projects
there are a team of data
scientists building
several trial and error
models. Most of these
fail! You just get keep
getting better…
14. Myth #7
A Data Scientist only
needs to worry about the
numbers and need not
understand the business
domain
15. Reality #7
A Data Scientist answers
business questions.
Understanding business
is part of figuring out the
nature of data.
16. Myth #8
You can become a data
scientist by just joining a
“Training Institute” for 3
months!
17. Reality #8
According to our
experience, “Guided
Learning by Doing” is
the only way in which
you can be on the path of
a data scientist
18. hank You
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