Presentation from WUSS 2015:
“Data scientist” is often used as a blanket title to describe jobs that are drastically different. There are plenty of articles and discussions on the web about what data science is, what qualities define a data scientist, how to nurture them, and how you should position yourself to be a competitive applicant. There are far fewer resources out there about the steps to take in order to obtain the skills necessary to practice this elusive discipline. This presentation will explore a collection of freely accessible materials and content to jumpstart your understanding of the theory and tools of Data Science. We will also discuss some of the variable understandings that companies use to define the roles of their Data Scientists.
The document provides a description of data scientist positions at three levels - Data Scientist I, II, and III. It outlines the general characteristics and responsibilities expected for each level, with level III involving the most complex work, responsibilities for leading projects, and experience/education qualifications. Key responsibilities include data analysis, modeling, collaborating with stakeholders, and communicating results.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
What data scientists really do, according to 50 data scientistsHugo Bowne-Anderson
My talk at PyData NYC, 2018.
This is the abstract:
Hugo Bowne-Anderson, data scientist and host of the DataFramed podcast, will give you a view into the thinking of 50 leading data scientists from around the world about the trends driving the data science revolution. During his interviews with these thought leaders, Hugo discovered themes and lessons about the past, present, and future of data science.
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
How I Learned to Stop Worrying and Love Linked DataDomino Data Lab
In this presentation, Jon Loyens will share:
-Best practices for sharing context and knowledge about your data projects
-How linked data can augment your existing data science workflow and toolchain to accelerate your work
-How a social network can unlock power of Linked Data and data collaboration
-How Linked Data can help you easily combine private and Open Data for fun and profit
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
How Data Science Builds Better Products - Data Science Pop-up SeattleDomino Data Lab
The document discusses how data science can help build better products. It explains that products are initially built to quickly test ideas through lightweight and imperfect means. Data science helps understand customer value and enables continuous learning through a process of analyzing data, making discoveries, and pivoting the product based on what is learned. This contrasts with the traditional approach where functionality is locked in place. The document advocates for an adaptive software environment that allows for rapid changes based on new insights. It provides tips for building successful data products through iterative improvements informed by data.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
The document provides a description of data scientist positions at three levels - Data Scientist I, II, and III. It outlines the general characteristics and responsibilities expected for each level, with level III involving the most complex work, responsibilities for leading projects, and experience/education qualifications. Key responsibilities include data analysis, modeling, collaborating with stakeholders, and communicating results.
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Dr. Michael Wu, the Chief Scientist at Lithium, where he applies data-driven methodologies to investigate the complex dynamics of the social web.
Michael works with big data and has developed many predictive and prescriptive social analytics with actionable insights. His R&D won him the recognition as a 2010 Influential Leader by CRM Magazine.
You can see all tweets and resources here:
http://www.experian.com/blogs/news/about/data-scientists/
What data scientists really do, according to 50 data scientistsHugo Bowne-Anderson
My talk at PyData NYC, 2018.
This is the abstract:
Hugo Bowne-Anderson, data scientist and host of the DataFramed podcast, will give you a view into the thinking of 50 leading data scientists from around the world about the trends driving the data science revolution. During his interviews with these thought leaders, Hugo discovered themes and lessons about the past, present, and future of data science.
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
Data Science Tech Institute - Big Data and Data Science Conference around Dr Gregory Piatetsky-Shapiro.
Keynote - An overview on Big Data & Data Science Dr Gregory Piatetsky-Shapiro - KDnuggets.com Founder & Editor.
Paris May 23rd & Nice May 26th 2016 @ Data ScienceTech Institute (https://www.datasciencetech.institute/)
How I Learned to Stop Worrying and Love Linked DataDomino Data Lab
In this presentation, Jon Loyens will share:
-Best practices for sharing context and knowledge about your data projects
-How linked data can augment your existing data science workflow and toolchain to accelerate your work
-How a social network can unlock power of Linked Data and data collaboration
-How Linked Data can help you easily combine private and Open Data for fun and profit
Presentation at Data ScienceTech Institute campuses, Paris and Nice, May 2016 , including Intro, Data Science History and Terms; 10 Real-World Data Science Lessons; Data Science Now: Polls & Trends; Data Science Roles; Data Science Job Trends; and Data Science Future
How Data Science Builds Better Products - Data Science Pop-up SeattleDomino Data Lab
The document discusses how data science can help build better products. It explains that products are initially built to quickly test ideas through lightweight and imperfect means. Data science helps understand customer value and enables continuous learning through a process of analyzing data, making discoveries, and pivoting the product based on what is learned. This contrasts with the traditional approach where functionality is locked in place. The document advocates for an adaptive software environment that allows for rapid changes based on new insights. It provides tips for building successful data products through iterative improvements informed by data.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
Data Scientist: The Sexiest Job in the 21st CenturyLyn Fenex
The document discusses the growing field of data science. It begins by defining data science and explaining how the rise of big data and the internet of things has led to an increasing demand for data scientists. It then examines the skills and qualifications needed for different types of data science roles, including data analysts, engineers, and research scientists. Finally, it provides resources for continuing to learn about data science.
Data science vs. Data scientist by Jothi PeriasamyPeter Kua
This document discusses data science vs data scientists and outlines key competencies for data scientists. It defines data science as modernizing existing analytics and data solutions using new data sources, formats, architectures, and techniques. The document compares traditional and modern approaches to data and analytics. It also discusses the skills required of entry-level vs senior data scientists, noting that enterprise data scientists require strong industry and business process skills while focusing on data, analytics, communication and technical abilities. The document provides an overview of the roles, responsibilities and deliverables of data scientists on enterprise projects.
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.
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
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.
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Introduction to Data Science (Data Summit, 2017)Caserta
This document summarizes an introduction to data science presentation by Joe Caserta and Bill Walrond of Caserta Concepts. Caserta Concepts is an internationally recognized data innovation and engineering consulting firm. The agenda covers why data science is important, challenges of working with big data, governing big data, the data pyramid, what data scientists do, standards for data science, and a demonstration of data analysis. Popular machine learning algorithms like regression, decision trees, k-means clustering and collaborative filtering are also discussed.
First, we will explore the power of a compounding insight machine (as opposed to an ad hoc insight machine):
-Human time is focused on improving logic, rather than executing outcomes
-Less dependent on human biases or frailty
-Robust to and tested by a huge collection of scenarios
Second, we will explore the anatomy of such a machine:
-The roles you need to cast on your team and who to fill them with
-The key processes required for generating and capturing insight and, more importantly, for building upon those insights
-The technology required to enable this approach
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.
How to Become a Data Scientist – By Ryan Orban, VP of Operations and Expansio...Galvanize
This document provides information about becoming a data scientist. It discusses the perfect storm of factors driving growth in data science jobs, including abundant data, cheap storage, and competitive advantages from data. It outlines the skills needed like mathematics, statistics, computer science, machine learning, and software engineering. It recommends learning programming languages like Python and R. It also suggests demonstrating expertise through projects on sites like GitHub and DataTau. Finally, it describes an immersive data science program that provides training and connections to employers.
Is Data Scientist still the sexiest job of 21st century? Find Out!Edureka!
The document discusses data science and why it is considered the sexiest job of the 21st century. It provides an overview of data science, including what it is, the skills required, and common career paths and job roles for data scientists. Examples are given of how companies are using data science for applications like predictive analytics, recommendations, customer acquisition, and churn prevention. While data science jobs are highly sought after and pay well, there is also a lack of qualified candidates, contributing to why it is seen as such an attractive and desirable career.
Correctness in Data Science - Data Science Pop-up SeattleDomino Data Lab
Presented by: Benjamin S. Skrainka is a Principal Data Scientist and Lead Instructor at Galvanize, Inc. For several decades, he has built practical solutions to relevant problems using the best statistical and engineering tools. His expertise spans several problem domains, including sequencing DNA, estimating demand for differentiated products, measuring advertising efficacy, and forecasting for capacity planning. Ben earned an AB in Physics from Princeton University and a PhD in Economics from University College London.
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Introduction to Data Science and Large-scale Machine LearningNik Spirin
This document is a presentation about data science and artificial intelligence given by James G. Shanahan. It provides an outline that covers topics such as machine learning, data science applications, architecture, and future directions. Shanahan has over 25 years of experience in data science and currently works as an independent consultant and teaches at UC Berkeley. The presentation provides background on artificial intelligence and machine learning techniques as well as examples of their successful applications.
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: presented by Dr. Sotarat Thammaboosadee, ITM Mahidol and Datalent Team. This presentation is a part of Data Science Clinic no.9 organized by Data Science Thailand, 8 March 2017 at All Season Place, Bangkok, Thailand.
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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
Ordinary people included anyone who is not a Geek like myself. This book is written for ordinary people. That includes manager, marketers, technical writers, couch potatoes and so on.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
This document discusses big data analytics. It provides links to resources on big data from different views, the roles in big data, and the data analytics lifecycle. It also gives tips for optimizing the use of big data, including moving big data out of IT silos, separating dirty and clean data, focusing on predictive analytics, and developing skills. Additionally, it lists 8 trends in big data analytics such as big data in the cloud, Hadoop as the new data operating system, big data lakes without prior database design, more predictive analytics, SQL on Hadoop, more and better NoSQL, deep learning, and in-memory analytics. The document concludes with an invitation for questions.
Big Data and Data Science are hot buzzwords right now. The buzzwords might go away but the ideas will not. This talk will explain the buzzwords, and it will cover some of the best resources for attaining data science skills.
Data science is the new thing! How to be a data scientist? See here.
This was originally was written by the team behind DataCamp, - the online interactive learning platform for data science!
Data Scientist: The Sexiest Job in the 21st CenturyLyn Fenex
The document discusses the growing field of data science. It begins by defining data science and explaining how the rise of big data and the internet of things has led to an increasing demand for data scientists. It then examines the skills and qualifications needed for different types of data science roles, including data analysts, engineers, and research scientists. Finally, it provides resources for continuing to learn about data science.
Data science vs. Data scientist by Jothi PeriasamyPeter Kua
This document discusses data science vs data scientists and outlines key competencies for data scientists. It defines data science as modernizing existing analytics and data solutions using new data sources, formats, architectures, and techniques. The document compares traditional and modern approaches to data and analytics. It also discusses the skills required of entry-level vs senior data scientists, noting that enterprise data scientists require strong industry and business process skills while focusing on data, analytics, communication and technical abilities. The document provides an overview of the roles, responsibilities and deliverables of data scientists on enterprise projects.
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.
This session describes the roles and skill sets required when building a Data Science team, and starting a data science initiative, including how to develop Data Science capabilities, select suitable organizational models for Data Science teams, and understand the role of executive engagement for enhancing analytical maturity at an organization.
Objective 1: Understand the knowledge and skills needed for a Data Science team and how to acquire them.
After this session you will be able to:
Objective 2: Learn about the different organizational models for forming a Data Science team and how to choose the best for your organization.
Objective 3: Understand the importance of Executive support for Data Science initiatives and role it plays in their successful deployment.
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.
data scientist the sexiest job of the 21st centuryFrank Kienle
Invited talk, describing the exciting work at Blue Yonder (www.blue-yonder.com),
'congress smart services - new business models' in Aachen, Germany 2015
Introduction to Data Science (Data Summit, 2017)Caserta
This document summarizes an introduction to data science presentation by Joe Caserta and Bill Walrond of Caserta Concepts. Caserta Concepts is an internationally recognized data innovation and engineering consulting firm. The agenda covers why data science is important, challenges of working with big data, governing big data, the data pyramid, what data scientists do, standards for data science, and a demonstration of data analysis. Popular machine learning algorithms like regression, decision trees, k-means clustering and collaborative filtering are also discussed.
First, we will explore the power of a compounding insight machine (as opposed to an ad hoc insight machine):
-Human time is focused on improving logic, rather than executing outcomes
-Less dependent on human biases or frailty
-Robust to and tested by a huge collection of scenarios
Second, we will explore the anatomy of such a machine:
-The roles you need to cast on your team and who to fill them with
-The key processes required for generating and capturing insight and, more importantly, for building upon those insights
-The technology required to enable this approach
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.
How to Become a Data Scientist – By Ryan Orban, VP of Operations and Expansio...Galvanize
This document provides information about becoming a data scientist. It discusses the perfect storm of factors driving growth in data science jobs, including abundant data, cheap storage, and competitive advantages from data. It outlines the skills needed like mathematics, statistics, computer science, machine learning, and software engineering. It recommends learning programming languages like Python and R. It also suggests demonstrating expertise through projects on sites like GitHub and DataTau. Finally, it describes an immersive data science program that provides training and connections to employers.
Is Data Scientist still the sexiest job of 21st century? Find Out!Edureka!
The document discusses data science and why it is considered the sexiest job of the 21st century. It provides an overview of data science, including what it is, the skills required, and common career paths and job roles for data scientists. Examples are given of how companies are using data science for applications like predictive analytics, recommendations, customer acquisition, and churn prevention. While data science jobs are highly sought after and pay well, there is also a lack of qualified candidates, contributing to why it is seen as such an attractive and desirable career.
Correctness in Data Science - Data Science Pop-up SeattleDomino Data Lab
Presented by: Benjamin S. Skrainka is a Principal Data Scientist and Lead Instructor at Galvanize, Inc. For several decades, he has built practical solutions to relevant problems using the best statistical and engineering tools. His expertise spans several problem domains, including sequencing DNA, estimating demand for differentiated products, measuring advertising efficacy, and forecasting for capacity planning. Ben earned an AB in Physics from Princeton University and a PhD in Economics from University College London.
Idiots guide to setting up a data science teamAshish Bansal
Some nuggets of how I started the data science practice at Gale Partners on a budget. Presented at the Toronto Hadoop Users Group (THUG) in April, 2015.
Introduction to Data Science and Large-scale Machine LearningNik Spirin
This document is a presentation about data science and artificial intelligence given by James G. Shanahan. It provides an outline that covers topics such as machine learning, data science applications, architecture, and future directions. Shanahan has over 25 years of experience in data science and currently works as an independent consultant and teaches at UC Berkeley. The presentation provides background on artificial intelligence and machine learning techniques as well as examples of their successful applications.
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: presented by Dr. Sotarat Thammaboosadee, ITM Mahidol and Datalent Team. This presentation is a part of Data Science Clinic no.9 organized by Data Science Thailand, 8 March 2017 at All Season Place, Bangkok, Thailand.
Data Science Applications | Data Science For Beginners | Data Science Trainin...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science Applications" PPT takes you through the various domains in which data science is being deployed today, along with some potential applications of this technology. The world today runs on data and this PPT shows exactly that.
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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
Ordinary people included anyone who is not a Geek like myself. This book is written for ordinary people. That includes manager, marketers, technical writers, couch potatoes and so on.
Data Science and Analytics for Ordinary People is a collection of blogs I have written on LinkedIn over the past year. As I continue to perform big data analytics, I continue to discover, not only my weaknesses in communicating the information, but new insights into using the information obtained from analytics and communicating it. These are the kinds of things I blog about and are contained herein.
Smart Data Slides: Data Science and Business Analysis - A Look at Best Practi...DATAVERSITY
Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.
To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.
In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.
This document discusses big data analytics. It provides links to resources on big data from different views, the roles in big data, and the data analytics lifecycle. It also gives tips for optimizing the use of big data, including moving big data out of IT silos, separating dirty and clean data, focusing on predictive analytics, and developing skills. Additionally, it lists 8 trends in big data analytics such as big data in the cloud, Hadoop as the new data operating system, big data lakes without prior database design, more predictive analytics, SQL on Hadoop, more and better NoSQL, deep learning, and in-memory analytics. The document concludes with an invitation for questions.
Big Data and Data Science are hot buzzwords right now. The buzzwords might go away but the ideas will not. This talk will explain the buzzwords, and it will cover some of the best resources for attaining data science skills.
Data science is the new thing! How to be a data scientist? See here.
This was originally was written by the team behind DataCamp, - the online interactive learning platform for data science!
This document provides an overview of becoming a data scientist. It defines a data scientist and lists common job titles. It discusses the functions of a data scientist like devising business strategies, descriptive/predictive analytics, and data mining. Examples are provided of customer churn analysis and market basket analysis. The skills, aptitudes, and educational paths to become a data scientist are also outlined.
This document discusses the importance of data science and building a data science team. It notes that data science provides new analytic insights and data products. Effective data science requires a team that includes data scientists, data engineers, and others. The document suggests data science can enable smart factories, supply chains, precision medicine, personalized shopping and learning. It promotes learning data science through the Data Science Thailand community.
Standardizing +113 million Merchant Names in Financial Services with Greenplu...Data Science London
The document discusses standardizing over 113 million merchant names from transaction data using regex and fuzzy matching. It involved extracting features from merchant names, cleaning names using regular expressions, fuzzy matching to group similar names, and manual rules. This allowed preliminary analysis showing 90% of transactions and spending were concentrated in 7-8% of top merchants. Customer segments were identified based on relative value added scores.
This is my talk from the PyDataLondon conference in May 2016. I outline some time management techniques and useful learning resources for those interested in transitioning into data science.
Intro to Data Science for Enterprise Big DataPaco Nathan
If you need a different format (PDF, PPT) instead of Keynote, please email me: pnathan AT concurrentinc DOT com
An overview of Data Science for Enterprise Big Data. In other words, how to combine structured and unstructured data, leveraging the tools of automation and mathematics, for highly scalable businesses. We discuss management strategy for building Data Science teams, basic requirements of the "science" in Data Science, and typical data access patterns for working with Big Data. We review some great algorithms, tools, and truisms for building a Data Science practice, and provide plus some great references to read for further study.
Presented initially at the Enterprise Big Data meetup at Tata Consultancy Services, Santa Clara, 2012-08-20 http://www.meetup.com/Enterprise-Big-Data/events/77635202/
This document discusses 10 R packages that are useful for winning Kaggle competitions by helping to capture complexity in data and make code more efficient. The packages covered are gbm and randomForest for gradient boosting and random forests, e1071 for support vector machines, glmnet for regularization, tau for text mining, Matrix and SOAR for efficient coding, and forEach, doMC, and data.table for parallel processing. The document provides tips for using each package and emphasizes letting machine learning algorithms find complexity while also using intuition to help guide the models.
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.
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Artificial intelligence is the study and design of intelligent agents, with no single goal. It aims to put the human mind into computers by developing machines that can achieve goals through computation. The origins of AI began in the 1940s with the development of electronic computers. Significant early developments included the first stored program computer in the 1950s, the Dartmouth Conference which coined the term "artificial intelligence" in the 1950s, and the development of the LISP programming language. In the following decades, AI research expanded and led to applications in fields like expert systems, games, and military systems. While progress has been made, the full extent of intelligence and the future of AI remains unknown.
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
Tutorial on Deep learning and ApplicationsNhatHai Phan
In this presentation, I would like to review basis techniques, models, and applications in deep learning. Hope you find the slides are interesting. Further information about my research can be found at "https://sites.google.com/site/ihaiphan/."
NhatHai Phan
CIS Department,
University of Oregon, Eugene, OR
This document summarizes a presentation on machine learning and Hadoop. It discusses the current state and future directions of machine learning on Hadoop platforms. In industrial machine learning, well-defined objectives are rare, predictive accuracy has limits, and systems must precede algorithms. Currently, Hadoop is used for data preparation, feature engineering, and some model fitting. Tools include Pig, Hive, Mahout, and new interfaces like Spark. The future includes YARN for running diverse jobs and improved machine learning libraries. The document calls for academic work on feature engineering languages and broader model selection ontologies.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
Data By The People, For The People
Daniel Tunkelang
Director, Data Science at LinkedIn
Invited Talk at the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012)
LinkedIn has a unique data collection: the 175M+ members who use LinkedIn are also the content those same members access using our information retrieval products. LinkedIn members performed over 4 billion professionally-oriented searches in 2011, most of those to find and discover other people. Every LinkedIn search and recommendation is deeply personalized, reflecting the user's current employment, career history, and professional network. In this talk, I will describe some of the challenges and opportunities that arise from working with this unique corpus. I will discuss work we are doing in the areas of relevance, recommendation, and reputation, as well as the ecosystem we have developed to incent people to provide the high-quality semi-structured profiles that make LinkedIn so useful.
Bio:
Daniel Tunkelang leads the data science team at LinkedIn, which analyzes terabytes of data to produce products and insights that serve LinkedIn's members. Prior to LinkedIn, Daniel led a local search quality team at Google. Daniel was a founding employee of faceted search pioneer Endeca (recently acquired by Oracle), where he spent ten years as Chief Scientist. He has authored fourteen patents, written a textbook on faceted search, created the annual workshop on human-computer interaction and information retrieval (HCIR), and participated in the premier research conferences on information retrieval, knowledge management, databases, and data mining (SIGIR, CIKM, SIGMOD, SIAM Data Mining). Daniel holds a PhD in Computer Science from CMU, as well as BS and MS degrees from MIT.
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.
How To Interview a Data Scientist
Daniel Tunkelang
Presented at the O'Reilly Strata 2013 Conference
Video: https://www.youtube.com/watch?v=gUTuESHKbXI
Interviewing data scientists is hard. The tech press sporadically publishes “best” interview questions that are cringe-worthy.
At LinkedIn, we put a heavy emphasis on the ability to think through the problems we work on. For example, if someone claims expertise in machine learning, we ask them to apply it to one of our recommendation problems. And, when we test coding and algorithmic problem solving, we do it with real problems that we’ve faced in the course of our day jobs. In general, we try as hard as possible to make the interview process representative of actual work.
In this session, I’ll offer general principles and concrete examples of how to interview data scientists. I’ll also touch on the challenges of sourcing and closing top candidates.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Data Scientist: the Sexiest Job of the 21st CenturyLyn Fenex
The document discusses the growing field of data science. It begins by defining data science and explaining how the rise of big data and the internet of things has led to an increasing demand for data scientists. It then discusses the skills and qualifications needed for different types of data science roles, including data analysts, engineers, and research scientists. Finally, it provides resources for continuing to learn about data science.
The document discusses six emerging trends in business analytics:
1. Humans and machines will increasingly work together in complementary roles, with machines handling tasks like data processing and humans focusing on creativity, empathy, and oversight of machine performance.
2. Analytics capabilities are expanding across entire organizations, moving from isolated initiatives to enterprise-wide strategies aimed at creating "insight-driven organizations."
3. Cybersecurity is becoming more important and proactive, utilizing predictive analytics to anticipate threats rather than just reacting to attacks.
4. The Internet of Things is expanding to include people and generating new business models by aggregating and analyzing behavioral data.
5. Companies are getting creative in addressing talent shortages, collaborating more closely
Analytics trends 2016 the next evolutionYann Lecourt
The document discusses six emerging trends in business analytics:
1. Humans and machines will increasingly work together in complementary roles, with machines handling tasks like data processing and humans focusing on creativity, empathy, and oversight of machine performance.
2. Analytics capabilities are expanding across entire organizations to create "insight-driven organizations" and scale initiatives from targeted areas to the enterprise level.
3. Cybersecurity is becoming more important as threats evolve, requiring proactive approaches like predictive modeling rather than just reactive defenses.
4. The Internet of Things is expanding to include people and generating new business models by aggregating and analyzing behavioral data.
5. Companies are addressing talent shortages by cultivating external talent providers and collaboration with
Evolution of Data Analytics: the past, the present and the futureVarun Nemmani
This paper delves into the topic of advanced analytics, the current industry demands to utilize and analyze huge/diverse amounts of data, how big data analytics is becoming a part of the decision making process and to anticipate trends. This paper takes the reader from Analytics era 1.0 to the current Analytics era 3.0; shows the future projections of big data analytics and also the current leaders of the Big Data Analytics market.
As 2017 begins, we are seeing big data and data science communities engage with new tools that specifically cater to data scientists and data engineers who aren’t necessarily experts in these techniques. Given rapid technological advances, the question for companies now is how to integrate new data science capabilities into their operations and strategies—and position themselves in a world where analytics can upend entire industries. Leading companies are using their data science capabilities not only to improve their core operations but also to launch entirely new business models.
Tracxn Big Data Analytics Landscape Report, June 2016Tracxn
New Enterprise Associates, Andreessen Horowitz, Accel Partners, Intel Capital and Khosla Ventures are the top 5 investors in big data analytics, with over 10 investments each.
What does a data scientist actually do? Here at Good Rebels we wanted to outline a profile of this new profession, with the help of various industry leaders from academia, business and institutions. In short, we concluded that the main tasks of a data scientist are to identify data, transform it when incomplete, categorize it, prepare it for analysis, perform the analysis, visualize the results and communicate them.
This document provides an introduction to data science and analytics. It discusses why data science jobs are in high demand, what skills are needed for these roles, and common types of analytics including descriptive, predictive, and prescriptive. It also covers topics like machine learning, big data, structured vs unstructured data, and examples of companies that utilize data and analytics like Amazon and Facebook. The document is intended to explain key concepts in data science and why attending a talk on this topic would be beneficial.
The document is an analytics salary study report presented by Analytics India Magazine and AnalytixLabs that examines salary trends in analytics roles in India. It provides an overview of emerging analytics job roles such as data scientist, data engineer, big data analyst, and data visualization analyst. These roles require skills in areas like machine learning, statistical analysis, data mining, programming, and working with big data platforms and tools. The report also studies salary trends across different cities, experience levels, skills, tools, and company types to help analytics professionals make informed career decisions.
This document discusses data science innovations and systems of insight. It provides examples of new data sources like social media language and drone/mobile sensor data that can generate novel insights. Systems of insight use machine learning and natural language generation to automatically analyze data, detect patterns, and present findings and narratives to users without extensive data preparation. This approach reduces the time spent on data wrangling and moves organizations from crisis-level talent shortages to faster decision making. The document advocates starting to use innovative data sources and systems of insight to generate customer insights, optimize processes, and gain a competitive advantage.
JU Analytics Day Presentation by Naveen Agarwal, Creative Analytics Solutions...Naveen Agarwal
This document discusses opportunities and challenges in big data analytics for professionals. It begins with an introduction by Naveen Agarwal about himself and his work at Johnson & Johnson Vision Care analyzing big data. The document then covers topics like what constitutes big data, why big data potential has been difficult to realize, assessing an organization's maturity with big data, and case studies of analytics projects at J&J Vision Care addressing questions in areas like product quality, sales forecasting, and cannibalization. It also discusses roles for data professionals like business analysts, data scientists, software engineers and the skills required for these roles.
Data lineage is a regulatory and internal requirement with potential to deliver significant operational and business benefits, but financial institutions can find it difficult to implement and complex to maintain as systems and regulatory requirements themselves, change quickly. The importance of understanding where the true source of the data is coming from, where the data flows to and what has changed cannot be overstated. The webinar defines data lineage and discuss implementation through the eyes of those that have implemented and sustained successful lineage solutions with significant benefits.
Listen to the webinar to find out about:
- Data management for data lineage
- Winning buy-in for projects
- Best practice implementation
- Operational and business benefits
- Expert practitioner advice
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
Why is big data all the rage? What is this "data science" that people are talking about? Why do I care — as a customer, and as someone who works at a company generating data? In this talk, I present the case for models, and how we can use data science to create and use models of our customers and the society around us.
This document summarizes a report on big data analytics and the use of analytical platforms. It describes how companies have been dealing with large volumes of data for decades but that data volumes are growing exponentially due to new types of structured, semi-structured, and unstructured data from sources like the web, social media, sensors and machine data. New analytical platforms and technologies are needed to efficiently store, manage and analyze this diverse new "big data". The report is based on a survey of 302 BI professionals and interviews with industry experts regarding their use of analytical platforms for big data analytics.
CPA ONE 2016 - Big data: big decisions or big fallacyLaurie Desautels
Laurie Desautels presented on "Big data: big decisions or big fallacy" at CPA Canada's national conference in September 2016. The presentation discussed what big data is, the language of analytics, lessons learned, and implications for accountants. Big data refers to large volumes of structured, unstructured and semi-structured data that is growing exponentially. Analytics can extract insights from data to help organizations make more informed decisions. Finance functions are spending more time on data analysis and generating business insights. Both human judgment and machine learning algorithms will play important roles in decision-making. Organizations must apply the right analytics approaches to different types of decisions.
Jill Pizzola's Tenure as Senior Talent Acquisition Partner at THOMSON REUTERS...dsnow9802
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Leadership Ambassador club Adventist modulekakomaeric00
Aims to equip people who aspire to become leaders with good qualities,and with Christian values and morals as per Biblical teachings.The you who aspire to be leaders should first read and understand what the ambassador module for leadership says about leadership and marry that to what the bible says.Christians sh
Joyce M Sullivan, Founder & CEO of SocMediaFin, Inc. shares her "Five Questions - The Story of You", "Reflections - What Matters to You?" and "The Three Circle Exercise" to guide those evaluating what their next move may be in their careers.
How to Prepare for Fortinet FCP_FAC_AD-6.5 Certification?NWEXAM
Begin Your Preparation Here: https://bit.ly/3VfYStG — Access comprehensive details on the FCP_FAC_AD-6.5 exam guide and excel in the Fortinet Certified Professional - Network Security certification. Gather all essential information including tutorials, practice tests, books, study materials, exam questions, and the syllabus. Solidify your knowledge of Fortinet FCP_FAC_AD-6.5 certification. Discover everything about the FCP_FAC_AD-6.5 exam, including the number of questions, passing percentage, and the time allotted to complete the test.
Job Finding Apps Everything You Need to Know in 2024SnapJob
SnapJob is revolutionizing the way people connect with work opportunities and find talented professionals for their projects. Find your dream job with ease using the best job finding apps. Discover top-rated apps that connect you with employers, provide personalized job recommendations, and streamline the application process. Explore features, ratings, and reviews to find the app that suits your needs and helps you land your next opportunity.
Learnings from Successful Jobs SearchersBruce Bennett
Are you interested to know what actions help in a job search? This webinar is the summary of several individuals who discussed their job search journey for others to follow. You will learn there are common actions that helped them succeed in their quest for gainful employment.
IT Career Hacks Navigate the Tech Jungle with a RoadmapBase Camp
Feeling overwhelmed by IT options? This presentation unlocks your personalized roadmap! Learn key skills, explore career paths & build your IT dream job strategy. Visit now & navigate the tech world with confidence! Visit https://www.basecamp.com.sg for more details.
Resumes, Cover Letters, and Applying OnlineBruce Bennett
This webinar showcases resume styles and the elements that go into building your resume. Every job application requires unique skills, and this session will show you how to improve your resume to match the jobs to which you are applying. Additionally, we will discuss cover letters and learn about ideas to include. Every job application requires unique skills so learn ways to give you the best chance of success when applying for a new position. Learn how to take advantage of all the features when uploading a job application to a company’s applicant tracking system.
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Our Time Today
– What is Data Science?
– Who needs a Data Scientist?
– What makes a Data Scientist?
– What kind of Data Scientist are
you?
Specialized
Talent and
Solutions
New
Work
Models
Shifts in
Business
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Hal Varian, the chief economist at Google, is known to have
said, “The sexy job in the next 10 years will be statisticians.
People think I’m joking, but who would’ve guessed that
computer engineers would’ve been the sexy job of the 1990s?”
The Hot Job of the Decade
Source: HBR
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The internet population now has over 2.1 billion people, and with every
website browsed, status shared, and photo uploaded, we leave a digital trail
that continually grows the hulking mass of big data.
Every minute, on average,
• YouTube users upload 48 hours of video
• Facebook users share 684,478 pieces of content,
• Instagram users share 3,600 new photos, and
• Tumblr sees 27,778 new posts published.
A perspective on how Big is Data
Source: That Conference, 2015
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“By 2015, 4.4 million IT jobs globally will be created to support big
data, generating 1.9 million IT jobs in the United States,” said Peter
Sondergaard, senior vice president at Gartner and global head of
Research. “In addition, every big data-related role in the U.S. will
create employment for three people outside of IT, so over the next
four years a total of 6 million jobs in the U.S. will be generated by
the information economy.“
Source:
Gartner Symposium/ITxpo 2012
The Information Economy
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A big data scientist understands how to integrate multiple systems and
data sets.
They need to be able to link and mash up distinctive data sets to
discover new insights.
This often requires connecting different types of data sets in different
forms as well as being able to work with potentially incomplete data
sources and cleaning data sets to be able to use them.
Sound familiar?
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8 Data Skills to Get You Hired
• Basic Tools
• Basic Statistics
• Machine Learning
• Multivariable Calculus and Linear Algebra
• Data Munging
• Data Visualization & Communication
• Software Engineering
Source: Udacity
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Most in-demand data skills
Source: WANTED Analytics, 2014
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Source: Udacity
4 Types of Data
Science Roles
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All joking aside, there are in fact some companies where being a data
scientist is synonymous with being a data analyst.
Your job might consist of tasks like pulling data out of MySQL
databases, becoming a master at Excel pivot tables, and producing
basic data visualizations (e.g., line and bar charts).
You may on occasion analyze the results of an A/B test or take the lead
on your company’s Google Analytics account.
.
A Data Scientist is a Data Analyst Who Lives in San Francisco:
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You’ll see job postings listed under both “Data Scientist” and “Data
Engineer” for this type of position.
Since you’d be (one of) the first data hires, there are likely many low-
hanging fruit, making it less important that you’re a statistics or machine
learning expert.
A data scientist with a software engineering background might excel at
a company like this, where it’s more important that a data scientist make
meaningful data-like contributions to the production code and provide
basic insights and analyses.
Please Wrangle Our Data!
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There are a number of companies for whom their data (or
their data analysis platform) is their product. In this case, the
data analysis or machine learning going on can be pretty
intense.
This is probably the ideal situation for someone who has a
formal mathematics, statistics, or physics background and is
hoping to continue down a more academic path.
Data Scientists in this setting likely focus more on producing
great data-driven products than they do answering operational
questions for the company.
We Are Data. Data Is Us:
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A lot of companies fall into this bucket. In this type of role, you’re joining
an established team of other data scientists.
The company you’re interviewing for cares about data but probably isn’t a
data company. It’s equally important that you can perform analysis, touch
production code, visualize data, etc.
Generally, these companies are either looking for generalists or they’re
looking to fill a specific niche where they feel their team is lacking, such
as data visualization or machine learning.
Reasonably Sized Non-Data Companies Who Are Data-Driven:
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Business Brain
• Concentrate on developing a “business brain” in addition to those
hard data skills.
• Data insights are useless without the foundational knowledge of the
business to which the data belongs.
• Keep your eyes and ears open to absorb as much understanding of
how a business and strategy works.
• Develop presentations to advise senior management in clear language
about the implications of their work for the organization.
• Develop ability to create examples, prototypes, demonstrations to help
management better understand the work.
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The Changing Role of Analysts
• Statistics done by non-Statisticians,
• The growth of Statistics into new areas such as healthcare and financial
applications,
• Greater expectations by management for statisticians to “be responsive
and vital to today's business needs and to be able to prove their
contributions quantitatively,”
• The requirements of analyses to be timely as well as appropriate,
• The need to work with immense databases, and
• Adapting to new forms of communication. (Hahn & Hoerl, 1998)
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Resources for ongoing learning
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As Peter Sondergaard, global head of research at Gartner, said in a
2012 statement,
The most valued data analysts of tomorrow will be able not only to
derive insights from existing data sets, but also to tell the quantitative
future:
“Dark data is the data being collected, but going unused despite its
value. Leading organizations of the future will be distinguished by the
quality of their predictive algorithms. This is the CIO challenge, and
opportunity.”
In conclusion
The investment made in data collection demands putting the data to good use to drive business results forward.
Big data and analytics are all around these days. IBM projects that every day we generate 2.5 quintillion bytes of data. This means that 90% of the data in the world has been created in the last two years.
In 2013, Gartner projected that by 2015, 85% of Fortune 500 organizations would be unable to exploit big data for competitive advantage and that 6M jobs would be created as a result of the data explosion.
Couple of key points:
Using data for predictive analysis
30+% believe that their demand will exceed the supply of available talent
Also- traditional BI professionals may not have the profile required to impact these new requirements. Like SAS says- looking through the windshield instead of the rear view mirror.
According to this graph from Forbes, the greatest preponderance of Data Scientists are working in the services information industry-
Search engines (Google, Microsoft), social networks (Twitter, Facebook, LinkedIn), financial institutions, Amazon, Apple, eBay, the health care industry, engineering companies (Boeing, Intel, Oil industry), retail analytics, mobile analytics, marketing agencies, data science vendors (for instance, Pivotal, Teradata, Tableau, SAS, Alpine Labs), environment, utilities government and defense routinely hire data scientists, though the job title is sometimes different.
Traditional companies (manufacturing) tend to call them operations research analysts.
This article cited that there is very little difference in result between 2013 and 2015, except that there appears for be more growth of hiring in smaller companies.
But these companies corroborate the previous graph; platform and software developers, as well as service/consulting companies are the largest employers of this skills set.
In this graphic, It is this outer ring of skills that are fundamental in becoming a data scientist.
The skills in the inner part of the diagram are skills that most people will have some experience in one or more of them.
The other skills can be developed and learned over time, all depending on the type of person you are.
When it comes to being a data scientist it might be fair to say you are a ‘A jack of all trades and a master of some’.
This graph outlines some of the characteristics that EMC thinks makes a Data Scientist- notice that none of these are technical skills
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
The big data scientist needs to be able to program, preferably in different programming languages such as Python, R, Java, Ruby, Clojure, Matlab, Pig or SQL.
You need to have an understanding of Hadoop, Hive and/or MapReduce.
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
Hopefully this gives you a sense of just how broad the title ‘data scientist’ is. Each of the four company ‘personalities’ above are seeking different skillsets, expertise, and experience levels. Despite that, all of these job postings would likely say “Data Scientist,” so look closely at the job description for a sense of what kind of team you’ll join and what skills to develop.
A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset
It seems like a number of companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they’re looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
There will be less guidance and you may face a greater risk of flopping or stagnating.
Companies like CoreLogic, Lexus Nexus, RAND Corporation make their business out of identifying trends
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
Some of the more important skills when interviewing at these firms are familiarity with tools designed for ‘big data’ (e.g., Hive or Pig) and experience with messy, ‘real-life’ datasets.
Being able to advice senior management in clear language about the implications of their work for the organization;
Having an, at least basic, understanding of how a business and strategy works;
Being able to create examples, prototypes, demonstrations to help management better understand the work;
[Use this slide to summarize what you know about the client’s current business challenges]