So, What Does a Data Scientist do?
•
1 like•717 views
What a Data Scientist does in the music industry, and my thoughts on what a data scientist is. Presented at the March 2012 Data Science London meetup
Report
Share
Report
Share
Recommended
Hadoop at Musicmetric
Overview of how we use Hadoop at Musicmetric as part of our data processing pipeline. Presented at the April 2012 Hadoop User Group London meetup as part of Big Data Week.
Note: Regarding slide 14; we have since switched to Oozie to coordinate Hadoop workflows.
Data Science Provenance: From Drug Discovery to Fake Fans
Knowledge work adds value to raw data; how this activity is performed is critical for how reliably results can be reproduced and scrutinized. With a brief diversion into epistemology, the presentation will outline the challenges for practitioners and consumers of Big Data analysis, and demonstrate how these were tackled at Inforsense (life sciences workflow analytics platform) and Musicmetric (social media analytics for music).
The talk covers the following issues with concrete examples:
- Representations of provenance
- Considerations to allow analysis computation to be recreated
- Reliable collection of noisy data from the internet
- Archiving of data and accommodating retrospective changes
- Using linked data to direct Big Data analytics
A Data Scientist in the Music Industry
The document discusses the role of a data scientist in the music industry. It describes how music consumption has shifted online, creating large amounts of digital data. A data scientist in this field collects raw data from various online sources, derives insights from it using techniques like sentiment analysis and machine learning, and creates products like daily charts and tools for analyzing an artist's popularity, fans, and press mentions. The data scientist acts as a "jack of all trades", learning new technologies and bringing together different experts and components to solve problems.
Tools and techniques for data science
R is an open source programming language and software environment for statistical analysis and graphics. It is widely used among data scientists for tasks like data manipulation, calculation, and graphical data analysis. Some key advantages of R include that it is open source and free, has a large collection of statistical tools and packages, is flexible, and has strong capabilities for data visualization. It also has an active user community and can integrate with other software like SAS, Python, and Tableau. R is a popular and powerful tool for data scientists.
Data Science Retrospective
The document summarizes how data science has changed over the past 6 years based on the presenter's experience. Some key points that have remained the same are the continued dominance of Python and messy real-world data. Things that have changed include increased specialization within roles, more remote opportunities, improved tools and infrastructure for working with big data, and greater availability of learning resources. While the hype around data science fluctuates, the core skills remain similar with an emphasis on programming, statistics, communication and independence.
Fortune Teller API - Doing Data Science with Apache Spark
This document discusses building an API using Apache Spark and machine learning to predict happiness based on personal details. It outlines gathering survey data, analyzing it using Spark and MLlib, and creating an API to make predictions. Key points covered include formulating the problem as predicting happiness scores, gathering national health survey data, using Spark for in-memory processing and machine learning algorithms to find correlations and make predictions, and designing an API to interface with the trained model.
Big Data Science: Intro and Benefits
What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation?
Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.
Python for Data Science - TDC 2015
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Recommended
Hadoop at Musicmetric
Overview of how we use Hadoop at Musicmetric as part of our data processing pipeline. Presented at the April 2012 Hadoop User Group London meetup as part of Big Data Week.
Note: Regarding slide 14; we have since switched to Oozie to coordinate Hadoop workflows.
Data Science Provenance: From Drug Discovery to Fake Fans
Knowledge work adds value to raw data; how this activity is performed is critical for how reliably results can be reproduced and scrutinized. With a brief diversion into epistemology, the presentation will outline the challenges for practitioners and consumers of Big Data analysis, and demonstrate how these were tackled at Inforsense (life sciences workflow analytics platform) and Musicmetric (social media analytics for music).
The talk covers the following issues with concrete examples:
- Representations of provenance
- Considerations to allow analysis computation to be recreated
- Reliable collection of noisy data from the internet
- Archiving of data and accommodating retrospective changes
- Using linked data to direct Big Data analytics
A Data Scientist in the Music Industry
The document discusses the role of a data scientist in the music industry. It describes how music consumption has shifted online, creating large amounts of digital data. A data scientist in this field collects raw data from various online sources, derives insights from it using techniques like sentiment analysis and machine learning, and creates products like daily charts and tools for analyzing an artist's popularity, fans, and press mentions. The data scientist acts as a "jack of all trades", learning new technologies and bringing together different experts and components to solve problems.
Tools and techniques for data science
R is an open source programming language and software environment for statistical analysis and graphics. It is widely used among data scientists for tasks like data manipulation, calculation, and graphical data analysis. Some key advantages of R include that it is open source and free, has a large collection of statistical tools and packages, is flexible, and has strong capabilities for data visualization. It also has an active user community and can integrate with other software like SAS, Python, and Tableau. R is a popular and powerful tool for data scientists.
Data Science Retrospective
The document summarizes how data science has changed over the past 6 years based on the presenter's experience. Some key points that have remained the same are the continued dominance of Python and messy real-world data. Things that have changed include increased specialization within roles, more remote opportunities, improved tools and infrastructure for working with big data, and greater availability of learning resources. While the hype around data science fluctuates, the core skills remain similar with an emphasis on programming, statistics, communication and independence.
Fortune Teller API - Doing Data Science with Apache Spark
This document discusses building an API using Apache Spark and machine learning to predict happiness based on personal details. It outlines gathering survey data, analyzing it using Spark and MLlib, and creating an API to make predictions. Key points covered include formulating the problem as predicting happiness scores, gathering national health survey data, using Spark for in-memory processing and machine learning algorithms to find correlations and make predictions, and designing an API to interface with the trained model.
Big Data Science: Intro and Benefits
What is Big Data? What is Data Science? What are the benefits? How will they evolve in my organisation?
Built around the premise that the investment in big data is far less than the cost of not having it, this presentation made at a tech media industry event, this presentation will unveil and explore the nuances of Big Data and Data Science and their synergy forming Big Data Science. It highlights the benefits of investing in it and defines a path to their evolution within most organisations.
Python for Data Science - TDC 2015
In this talk, we introduce the Data Scientist role , differentiate investigative and operational analytics, and demonstrate a complete Data Science process using Python ecosystem tools, like IPython Notebook, Pandas, Matplotlib, NumPy, SciPy and Scikit-learn. We also touch the usage of Python in Big Data context, using Hadoop and Spark.
Top 8 Data Science Tools | Open Source Tools for Data Scientists | Edureka
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Introduction to Data Science
Workshop with Joe Caserta, President of Caserta Concepts, at Data Summit 2015 in NYC.
Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends and actions, may be considered the "sexiest" job of the 21st century, but it requires an understanding of many elements of data analytics. This workshop introduced basic concepts, such as SQL and NoSQL, MapReduce, Hadoop, data mining, machine learning, and data visualization.
For notes and exercises from this workshop, click here: https://github.com/Caserta-Concepts/ds-workshop.
For more information, visit our website at www.casertaconcepts.com
Big data Big Analytics
This document provides an agenda for a presentation on big data and big data analytics using R. The presentation introduces the presenter and has sections on defining big data, discussing tools for storing and analyzing big data in R like HDFS and MongoDB, and presenting case studies analyzing social network and customer data using R and Hadoop. The presentation also covers challenges of big data analytics, existing case studies using tools like SAP Hana and Revolution Analytics, and concerns around privacy with large-scale data analysis.
The evolution of data analytics
In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques.
In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing. We will introduce Spark, an engine for large-scale data processing optimized for in-memory computing.
Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
What is Big Data?
Slide presentasi ini dibawakan oleh Jony Sugianto dalam Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
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
Intro to Data Science by DatalentTeam at Data Science Clinic#11
Intro to Data Science by DatalentTeam at Data Science Clinic#11Dr.Sotarat Thammaboosadee CIMP-Data Governance
This document provides an overview of data science including:
- Definitions of data science and the motivations for its increasing importance due to factors like big data, cloud computing, and the internet of things.
- The key skills required of data scientists and an overview of the data science process.
- Descriptions of different types of databases like relational, NoSQL, and data warehouses versus data lakes.
- An introduction to machine learning, data mining, and data visualization.
- Details on courses for learning data science. Unexpected Challenges in Large Scale Machine Learning by Charles Parker
Talk by Charles Parker (BigML) at BigMine12 at KDD12.
In machine learning, scale adds complexity. The most obvious consequence of scale is that data takes longer to process. At certain points, however, scale makes trivial operations costly, thus forcing us to re-evaluate algorithms in light of the complexity of those operations. Here, we will discuss one important way a general large scale machine learning setting may diff er from the standard supervised classification setting and show some the results of some preliminary experiments highlighting this di fference. The results suggest that there is potential for signifi cant improvement beyond obvious solutions.
Big data
This document defines big data and its characteristics using the 5 Vs model - volume, velocity, variety, veracity, and value. It discusses technologies like Hadoop, HDFS, MapReduce, Apache Pig, Hive, and Mahout that make up the Hadoop ecosystem for distributed storage and processing of large, unstructured data sets. Finally, it outlines the key skills needed for working with big data, including analytical and computer skills as well as creativity, math, communication abilities, and understanding of business objectives.
Data science e machine learning
Una breve introduzione alla data science e al machine learning con un'enfasi sugli scenari applicativi, da quelli tradizionali a quelli più innovativi. La overview copre la definizione di base di data science, una overview del machine learning e esempi su scenari tradizionali, Recommender systems e Social Network Analysis, IoT e Deep Learning
Demystifying Data Science with an introduction to Machine Learning
The document provides an introduction to the field of data science, including definitions of data science and machine learning. It discusses the growing demand for data science skills and jobs. It also summarizes several key concepts in data science including the data science pipeline, common machine learning algorithms and techniques, examples of machine learning applications, and how to get started in data science through online courses and open-source tools.
Big data deep learning: applications and challenges
This document discusses big data, deep learning, and their applications and challenges. It begins with an introduction to big data that defines it in terms of large volume, high velocity, and variety of data types. It then discusses challenges of big data like storage, transfer, privacy, and analyzing diverse data types. Applications of big data analytics include sensor data analysis, trend analysis, and network intrusion detection. Deep learning algorithms can extract patterns from large unlabeled data and non-local relationships. Applications of deep learning in big data include semantic indexing for search engines, discriminative tasks using extracted features, and transfer learning. Challenges of deep learning in big data include learning from streaming data, high dimensionality, scalability, and distributed computing.
Big Data and the Art of Data Science
This document discusses the rise of big data and data science. It notes that while data volumes are growing exponentially, data alone is just an asset - it is data scientists that create value by building data products that provide insights. The document outlines the data science workflow and highlights both the tools used and challenges faced by data scientists in extracting value from big data.
Data Science: Harnessing Open Data for High Impact Solutions
This document provides an overview of open data and applications created using open data from various government sources. It introduces Mohd Izhar Firdaus Ismail and his background working with data. Examples of open data applications from Data.gov (US) and Data.gov.uk (UK) are described that address issues like locating alternative fuel stations, planning farming activities based on weather, and choosing a college based on affordability. Tips are provided for getting started with data work, including cleaning, analyzing and visualizing data using open source tools like Python libraries, Apache Zeppelin and Hortonworks.
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.
Combining Data Mining and Machine Learning for Effective User Profiling
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
Big data analytics
Unstructured data contains different types of data that is not contained in structured databases, and makes up over 90% of social media data. Analyzing unstructured data from sources like social media, emails, and documents can provide insights into customer perceptions and improve productivity. Common types of unstructured data include text files, photos, videos, and audio. Tools for analyzing unstructured data include R, RapidMiner, Weka, Python, and Hadoop, each with different strengths and specializations. Sentiment analysis of social media data can help companies in sectors like insurance understand customer opinions.
Paving The Way To Data Driven
Demystifying Big Data, exploring what are Data Engineering & Data Science and a framework for Big Data adoption.
Making Open the Default
Keynote address at OpenCon 2015 in Brussels, Belgium. Video here:
https://www.youtube.com/watch?v=3rK-tt3UlYs
Data analytics using the cloud challenges and opportunities for india
- Data analytics is transitioning from traditional paradigms like SAS and SPSS to newer paradigms using open source tools like R and Python, and distributed frameworks like Hadoop.
- Cloud computing provides on-demand access to computing resources and is enabling data analytics through services like IaaS, PaaS and SaaS. However, most cloud infrastructure is based in the US raising privacy and access concerns.
- India has an opportunity to leverage its engineering talent and build domestic cloud infrastructure to ensure data sovereignty, but needs to develop strong data privacy regulations and address gaps in domain expertise and entrepreneurial ecosystems.
Music Recommendation 2018
The current revolution in the music industry represents great opportunities and challenges for music recommendation systems. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.).
Trends in Music Recommendations 2018
This document provides an overview of music recommendation research challenges in 2018. It discusses how the music industry is transitioning from a "discover and own" model to an "access" model with the rise of streaming services. It also discusses various data sources and algorithms used for music recommendation, including collaborative filtering, content-based approaches using audio features, and incorporating additional information like text, images, and user context. Finally, it outlines new challenges for music information retrieval research in further improving music discovery and recommendation.
More Related Content
What's hot
Top 8 Data Science Tools | Open Source Tools for Data Scientists | Edureka
** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Introduction to Data Science
Workshop with Joe Caserta, President of Caserta Concepts, at Data Summit 2015 in NYC.
Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends and actions, may be considered the "sexiest" job of the 21st century, but it requires an understanding of many elements of data analytics. This workshop introduced basic concepts, such as SQL and NoSQL, MapReduce, Hadoop, data mining, machine learning, and data visualization.
For notes and exercises from this workshop, click here: https://github.com/Caserta-Concepts/ds-workshop.
For more information, visit our website at www.casertaconcepts.com
Big data Big Analytics
This document provides an agenda for a presentation on big data and big data analytics using R. The presentation introduces the presenter and has sections on defining big data, discussing tools for storing and analyzing big data in R like HDFS and MongoDB, and presenting case studies analyzing social network and customer data using R and Hadoop. The presentation also covers challenges of big data analytics, existing case studies using tools like SAP Hana and Revolution Analytics, and concerns around privacy with large-scale data analysis.
The evolution of data analytics
In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques.
In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing. We will introduce Spark, an engine for large-scale data processing optimized for in-memory computing.
Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
What is Big Data?
Slide presentasi ini dibawakan oleh Jony Sugianto dalam Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDO pada tanggal 14 Mei 2016.
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
Intro to Data Science by DatalentTeam at Data Science Clinic#11
Intro to Data Science by DatalentTeam at Data Science Clinic#11Dr.Sotarat Thammaboosadee CIMP-Data Governance
This document provides an overview of data science including:
- Definitions of data science and the motivations for its increasing importance due to factors like big data, cloud computing, and the internet of things.
- The key skills required of data scientists and an overview of the data science process.
- Descriptions of different types of databases like relational, NoSQL, and data warehouses versus data lakes.
- An introduction to machine learning, data mining, and data visualization.
- Details on courses for learning data science. Unexpected Challenges in Large Scale Machine Learning by Charles Parker
Talk by Charles Parker (BigML) at BigMine12 at KDD12.
In machine learning, scale adds complexity. The most obvious consequence of scale is that data takes longer to process. At certain points, however, scale makes trivial operations costly, thus forcing us to re-evaluate algorithms in light of the complexity of those operations. Here, we will discuss one important way a general large scale machine learning setting may diff er from the standard supervised classification setting and show some the results of some preliminary experiments highlighting this di fference. The results suggest that there is potential for signifi cant improvement beyond obvious solutions.
Big data
This document defines big data and its characteristics using the 5 Vs model - volume, velocity, variety, veracity, and value. It discusses technologies like Hadoop, HDFS, MapReduce, Apache Pig, Hive, and Mahout that make up the Hadoop ecosystem for distributed storage and processing of large, unstructured data sets. Finally, it outlines the key skills needed for working with big data, including analytical and computer skills as well as creativity, math, communication abilities, and understanding of business objectives.
Data science e machine learning
Una breve introduzione alla data science e al machine learning con un'enfasi sugli scenari applicativi, da quelli tradizionali a quelli più innovativi. La overview copre la definizione di base di data science, una overview del machine learning e esempi su scenari tradizionali, Recommender systems e Social Network Analysis, IoT e Deep Learning
Demystifying Data Science with an introduction to Machine Learning
The document provides an introduction to the field of data science, including definitions of data science and machine learning. It discusses the growing demand for data science skills and jobs. It also summarizes several key concepts in data science including the data science pipeline, common machine learning algorithms and techniques, examples of machine learning applications, and how to get started in data science through online courses and open-source tools.
Big data deep learning: applications and challenges
This document discusses big data, deep learning, and their applications and challenges. It begins with an introduction to big data that defines it in terms of large volume, high velocity, and variety of data types. It then discusses challenges of big data like storage, transfer, privacy, and analyzing diverse data types. Applications of big data analytics include sensor data analysis, trend analysis, and network intrusion detection. Deep learning algorithms can extract patterns from large unlabeled data and non-local relationships. Applications of deep learning in big data include semantic indexing for search engines, discriminative tasks using extracted features, and transfer learning. Challenges of deep learning in big data include learning from streaming data, high dimensionality, scalability, and distributed computing.
Big Data and the Art of Data Science
This document discusses the rise of big data and data science. It notes that while data volumes are growing exponentially, data alone is just an asset - it is data scientists that create value by building data products that provide insights. The document outlines the data science workflow and highlights both the tools used and challenges faced by data scientists in extracting value from big data.
Data Science: Harnessing Open Data for High Impact Solutions
This document provides an overview of open data and applications created using open data from various government sources. It introduces Mohd Izhar Firdaus Ismail and his background working with data. Examples of open data applications from Data.gov (US) and Data.gov.uk (UK) are described that address issues like locating alternative fuel stations, planning farming activities based on weather, and choosing a college based on affordability. Tips are provided for getting started with data work, including cleaning, analyzing and visualizing data using open source tools like Python libraries, Apache Zeppelin and Hortonworks.
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.
Combining Data Mining and Machine Learning for Effective User Profiling
Slide presentasi ini dibawakan oleh Anne Regina pada Seminar & Workshop Pengenalan & Potensi Big Data & Machine Learning yang diselenggarakan oleh KUDIO pada tanggal 14 Mei 2016.
Big data analytics
Unstructured data contains different types of data that is not contained in structured databases, and makes up over 90% of social media data. Analyzing unstructured data from sources like social media, emails, and documents can provide insights into customer perceptions and improve productivity. Common types of unstructured data include text files, photos, videos, and audio. Tools for analyzing unstructured data include R, RapidMiner, Weka, Python, and Hadoop, each with different strengths and specializations. Sentiment analysis of social media data can help companies in sectors like insurance understand customer opinions.
Paving The Way To Data Driven
Demystifying Big Data, exploring what are Data Engineering & Data Science and a framework for Big Data adoption.
Making Open the Default
Keynote address at OpenCon 2015 in Brussels, Belgium. Video here:
https://www.youtube.com/watch?v=3rK-tt3UlYs
Data analytics using the cloud challenges and opportunities for india
- Data analytics is transitioning from traditional paradigms like SAS and SPSS to newer paradigms using open source tools like R and Python, and distributed frameworks like Hadoop.
- Cloud computing provides on-demand access to computing resources and is enabling data analytics through services like IaaS, PaaS and SaaS. However, most cloud infrastructure is based in the US raising privacy and access concerns.
- India has an opportunity to leverage its engineering talent and build domestic cloud infrastructure to ensure data sovereignty, but needs to develop strong data privacy regulations and address gaps in domain expertise and entrepreneurial ecosystems.
What's hot (20)
Top 8 Data Science Tools | Open Source Tools for Data Scientists | Edureka
Top 8 Data Science Tools | Open Source Tools for Data Scientists | Edureka
Intro to Data Science by DatalentTeam at Data Science Clinic#11
Intro to Data Science by DatalentTeam at Data Science Clinic#11
Unexpected Challenges in Large Scale Machine Learning by Charles Parker
Unexpected Challenges in Large Scale Machine Learning by Charles Parker
Demystifying Data Science with an introduction to Machine Learning
Demystifying Data Science with an introduction to Machine Learning
Big data deep learning: applications and challenges
Big data deep learning: applications and challenges
Data Science: Harnessing Open Data for High Impact Solutions
Data Science: Harnessing Open Data for High Impact Solutions
Combining Data Mining and Machine Learning for Effective User Profiling
Combining Data Mining and Machine Learning for Effective User Profiling
Data analytics using the cloud challenges and opportunities for india
Data analytics using the cloud challenges and opportunities for india
Similar to So, What Does a Data Scientist do?
Music Recommendation 2018
The current revolution in the music industry represents great opportunities and challenges for music recommendation systems. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.).
Trends in Music Recommendations 2018
This document provides an overview of music recommendation research challenges in 2018. It discusses how the music industry is transitioning from a "discover and own" model to an "access" model with the rise of streaming services. It also discusses various data sources and algorithms used for music recommendation, including collaborative filtering, content-based approaches using audio features, and incorporating additional information like text, images, and user context. Finally, it outlines new challenges for music information retrieval research in further improving music discovery and recommendation.
Music data analysis big data presentation
Music accounts for a significant chunk of interest among
various online activities. This is reflected by wide array of
alternatives offered in music related web/mobile apps, information portals, featuring millions of artists, songs and
events attracting user activity at similar scale. Availability of large scale structured and unstructured data has attracted similar level of attention by data science community. This paper attempts to offer current state-of-the-art
in music related analysis. Various approaches involving machine learning, information theory, social network analysis,
semantic web and linked open data are represented in the
form of taxonomy along with data sources and use cases
addressed by the research community.
Dmitry Bugaychenko - Smart.Data@ОК.ru. How to make the world a bit better usi...
Dmitry Bugaychenko - Smart.Data@ОК.ru
How to make the world a bit better using a petabyte of data and a couple of good books
Introduction to Data Science.pptx
This document provides an introduction to data science. It defines data science as a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. The document discusses the importance and impact of data science on organizations and society. It also outlines common applications of data science and the roles and skills required for a career in data science.
Data Science Intro.pptx
This document provides an introduction to data science. It defines data science as a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. The document discusses the importance and impact of data science on organizations and society. It also outlines common applications of data science and the roles and skills required for a career in data science.
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
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Analytics in media and entertainment industry
The purpose of this presentation is to understand how analytics is used in the Media and Entertainment Industry. Examples of Netflix, Spotify and BookMyShow have been considered to look at the same
Introduction Data Science.pptx
The document provides an overview of data science, big data, data mining, and data mining techniques. It defines data science as a multi-disciplinary field that uses scientific methods to extract knowledge from structured and unstructured data. Big data is described as large, diverse datasets that are too large for traditional databases to handle. Common data mining tasks like prediction, classification, clustering and association rule mining are summarized. Finally, specific techniques like decision trees, k-means clustering, and association rule mining are overviewed.
Data Science.pptx
This document provides an introduction to data science, including definitions of data science, its impact and importance. It discusses how data science affects organizations and provides competitive advantages. Examples of data science applications are given across various domains like banking, healthcare, transportation and more. The document also outlines the road to becoming a data scientist and what skills are required, such as learning to code, mathematics, machine learning techniques and software engineering. In summary, data science uses scientific methods to extract knowledge and insights from data, it benefits society in areas like healthcare, transportation and environment, and becoming a data scientist requires strong coding and analytical skills.
DATA SCINCE.pptx
This document discusses data science and the role of data scientists. It explains that data scientists collect and analyze raw data to generate meaningful insights that help with decision making. They understand phenomena and help organizations make decisions. The document also outlines the steps to learn data science, including learning programming languages like Python and R, statistics, data visualization with libraries like Matplotlib and Seaborn, machine learning algorithms, and completing projects.
Exploring Data Preparation and Visualization Tools for Urban Forestry
This webinar was held on December 12, 2012 and provided an overview of free and low-cost tools for cleaning and preparing data and building useful and beautiful data visualizations.
SKILLWISE-BIGDATA ANALYSIS
General outline created by SKILLWISE about BIGDATA, which gives you a clear idea about what is bigdata?
Digital Marketing & Discoverability for the Performing Arts
Digital Marketing & Discoverability for the Performing ArtsCanadian Association for the Performing Arts / Association canadienne des organismes artistiques
This document discusses digital discoverability strategies for performing arts organizations. It defines discoverability as the ability for something to be discovered online through search and recommendation engines. It outlines various methods of online discovery like advertising, publicity, niche communities, social networks and search/recommendation engines. It provides best practices for search engine optimization, including strategic language use, backlinking, semantic optimization, localization and structured data. It also discusses the benefits of using linked open data sources like Wikidata to enrich arts discoverability.SuanIct-Bigdata desktop-final
The document discusses big data, its history, technologies, and uses. It begins with an introduction to big data and defines it using the 3Vs/4Vs model, describing the volume, velocity, variety and increasingly veracity of data. It then discusses big data technologies like Hadoop, databases, reporting, dashboards and real-time analytics. Examples are given of how big data is used, such as understanding customers, optimizing business processes, improving health outcomes, and improving security and law enforcement. Requirements for big data analytics are also mentioned, including data management, analytics applications, and business interpretation.
STI Summit 2011 - Intro
This document provides an agenda for a summit on semantic technologies and the semantic web. The summit will include discussions on managing large amounts of semantic data, applying semantic web technologies to specific domains, social semantics, and making linked data work in applications. Presentations will provide overviews on these topics to spur open-ended discussion among participants on the state, future opportunities, and challenges relating to the semantic web.
Big data
This document discusses big data, including key enablers like increased storage and processing power. It notes that 90% of data today was created in the last two years. Big data comes from sources like mobile devices, sensors, and social media. The challenge is managing and analyzing large amounts of diverse data in a timely way. Common big data types include structured, unstructured, semi-structured, text, graph, and streaming data. Big data analytics can provide value across many domains. Issues include privacy, regulation, and ensuring analysis solves meaningful problems. The big data industry is large and growing rapidly.
Building Effective Frameworks for Social Media Analysis
This document outlines an analytic framework for effectively analyzing social media data. It discusses common pitfalls to avoid, such as relying too heavily on metrics without understanding context. The framework involves capturing data from multiple social media sources, reporting insights through visualizations, and iteratively analyzing the data to test hypotheses and make recommendations. A case study applies this framework to understand public sentiment toward a new video game. The document emphasizes adapting to changing data and focusing analysis on addressing specific operational needs.
Similar to So, What Does a Data Scientist do? (20)
Dmitry Bugaychenko - Smart.Data@ОК.ru. How to make the world a bit better usi...
Dmitry Bugaychenko - Smart.Data@ОК.ru. How to make the world a bit better usi...
Introduction to Data Science Introduction to Data Science .pptx
Introduction to Data Science Introduction to Data Science .pptx
Exploring Data Preparation and Visualization Tools for Urban Forestry
Exploring Data Preparation and Visualization Tools for Urban Forestry
Digital Marketing & Discoverability for the Performing Arts
Digital Marketing & Discoverability for the Performing Arts
Building Effective Frameworks for Social Media Analysis
Building Effective Frameworks for Social Media Analysis
Recently uploaded
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Epistemic Interaction - tuning interfaces to provide information for AI support
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsUiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Recently uploaded (20)
By Design, not by Accident - Agile Venture Bolzano 2024
By Design, not by Accident - Agile Venture Bolzano 2024
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
So, What Does a Data Scientist do?
- 1. So, What Does a Data Scientist do? A Data Scientist in the Music Industry Dr Jameel Syed March 2012 http://jasyed.com/datascience/
- 2. Overview – Musicmetric CTO – InforSense founding member • PhD in Workflows for Life Sciences Analysis – Co-organiser Big Data London meetup
- 4. Music has moved online • The world has changed – Do you buy vinyl/tapes/CDs of music? – Do you buy music downloads? – Do you download illegal content from bittorrent? – Do you listen to music on YouTube? – Do you “like” bands on Facebook? – Do you subscribe to Spotify? – Do you listen on the radio to the weekly charts on a Sunday afternoon? • What’s happening online?
- 5. How popular am I?
- 6. Who are my fans?
- 7. Where are my fans?
- 8. What is the press saying?
- 10. A Data Scientist in the Music Industry • Raw Data -> Derived Data -> Insight – Who is popular right now/in the immediate future? – What was the effect of appearing at a festival? – Which artists are (becoming) popular with listeners with certain demographics (in a region)? • Data processing, machine learning & statistical methods – Sentiment analysis – Named Entity Recognition – Ranking – Segmentation • One-offs – Infographics and microsites for events – Brand alignment via demographics – Music Hack Days • Product – Daily charts – Sentiment scoring web crawled reviews
- 11. What is a Data Scientist?
- 12. Have we been here before? • Statistician • Data Analyst • Quantitative analyst • Bioinformatician • Data Miner • Business Intelligence consultant • Computational physicst
- 13. A Life Sciences digression...
- 14. What’s new? • Data provides the opportunity – Old: Collect and store data presupposing how it will be used – New: Collect raw data & explore which derivations are interesting; integrating data from multiple online sources. – Big Data technology to cope with data volume • Programming is essential – APIs – Heterogeneous environment(s) • Method of presentation – Infographics – Interactive (web) applications – (Raw data)
- 15. Data Scientist • “Jack of all trades” – “Hacker” mentality: learn new technology and approaches for a project on short notice – Creative self-starters – Work alongside other experts (data, domain, software engineering)
- 16. A Data Scientist is good at knitting? • Not building from scratch, knitting together pre-existing parts • Data – Databases (relational/NoSQL) – Files – APIs • Algorithms – Open source libraries – Off the shelf tools • Compute – Linux – AWS? • Languages – Many, especially “scripting” languages
Editor's Notes
- http://jasyed.com/datascience/
- http://meetup.com/big-data-london/
- Long infographic is long: http://www.musicmetric.com/musicmetric-south-by-south-west-infographic/
- As of this writing there does not exist a "Data Scientist" entryin Wikipedia although there is one for http://en.wikipedia.org/wiki/Big_data
- Microarray image from http://en.wikipedia.org/wiki/DNA_microarray
- https://twitter.com/#!/DEVOPS_BORAT/status/174602033872109569
- Sewing a quilt probably doesn’t involve knitting