- Top /r/MachineLearning Posts, March: A Super Harsh Guide to Machine Learning; Is it Gaggle or Koogle?!? - Apr 4, 2017.
A Super Harsh Guide to Machine Learning; Google is acquiring data science community Kaggle; Suggestion by Salesforce chief data scientist; Andrew Ng resigning from Baidu; Distill: An Interactive, Visual Journal for Machine Learning Research
- How to Get a Data Science Job: A Ridiculously Specific Guide - Mar 7, 2017.
Job hunting is challenging and sometimes frustrating task and we all experience it in our career. Here we provide a very specific and practical guide to get your dream job in Data Science world.
- The Human Data Scientist: Safeguarding Your Career in the World of Automation - Feb 28, 2017.
"Data scientist" continues to be recognized as a top career, but does this mean unending spoils for the data scientist? With large scale mass automation on the horizon for numerous professions, what can we do to safeguard our positions?
- Reducing Science-related Stress - Feb 17, 2017.
The author presents a list of things learned through hard experience to help him with his own imposter syndrome, and help him to feel less stressed out about science.
- Career Advice for Analytics & Data Science Professionals - Feb 13, 2017.
In our experience working with many quantitative professionals over the years, the two main areas that contribute to long-term career growth are networking and continuous learning. Here is specific advice on how to do this and tips for Continuous Learning.
- Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall - Jan 5, 2017.
Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.
- How To Make Your Mark As A Woman In Big Data - Dec 3, 2016.
Despite the shift in big data technology innovation that is driving tremendous growth and opportunities, women still play a small role in this arena. Here are 5 thoughts for women considering a career in big data.
- Why We Need Data Science - Nov 26, 2016.
A gentle reminder as to why we need Data Science, reasons for which even you may have been guilty of offending at some point. A basic topic, to be sure, making it all the more important.
- Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed - Nov 25, 2016.
Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
- Top Data Scientist Daniel Tunkelang on Data Recycling - Nov 22, 2016.
Respected Data Scientist Daniel Tunkelang shares some insight into data recycling, using data from other contexts to bootstrap your initial statistical models until you can collect live data.
- Top Data Scientist Daniel Tunkelang on Data Science Project Scope… and Reducing It - Oct 19, 2016.
Respected Data Scientist Daniel Tunkelang shares some insight into problems lying at the crossroads of software engineering and data science, and prescribes one major solution: reduce scope!
- Data Preparation Tips, Tricks, and Tools: An Interview with the Insiders - Oct 14, 2016.
Data preparation and preprocessing tasks constitute a high percentage of any data-centric operation. In order to provide some insight, we have asked a pair of experts to answer a few questions on the subject.
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- Humans & Machines Ethics Framework: Assessing Machine Learning Influence - Oct 11, 2016.
Humans & Machines Ethics Canvas’ main goal is to be a guide for critical thinking throughout the ethical decision-making process. It acts as a value system and an ethics framework to assess the influence of machine learning and software development while developing a system for individuals, teams, and organisations.
- Machine Learning in a Year: From Total Noob to Effective Practitioner - Sep 19, 2016.
Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.
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- How to Become a (Type A) Data Scientist - Aug 23, 2016.
This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A.
- Approaching (Almost) Any Machine Learning Problem - Aug 18, 2016.
If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.
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- Advice for Data Science Interviews - Aug 9, 2016.
Check out an interview excerpt from Springboard’s Guide to Data Science Interviews. Determine how one can find data science interviews - and ace them!
- Building a Data Science Portfolio: Machine Learning Project Part 3 - Jul 22, 2016.
The final installment of this comprehensive overview on building an end-to-end data science portfolio project focuses on bringing it all together, and concludes the project quite nicely.
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- Building a Data Science Portfolio: Machine Learning Project Part 2 - Jul 21, 2016.
The second part of this comprehensive overview on building an end-to-end data science portfolio project concentrates on data exploration and preparation.
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- Building a Data Science Portfolio: Machine Learning Project Part 1 - Jul 20, 2016.
Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!
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- 2016’s Best Places for Data Scientist Jobs - Jul 15, 2016.
Get the info on the Best Places in the U.S. for Data Scientist Jobs with GoodCall's new data-driven report.
- Ten Simple Rules for Effective Statistical Practice: An Overview - Jun 23, 2016.
An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.
- How Do You Identify the Right Data Scientist for Your Team? - Jun 8, 2016.
Have you been trying to answer the question of what type of a data scientist would be the best fit for your team? Is there a single all-encompassing answer or does it vary based on the client objectives? Read on for some insight.
- KDnuggets™ News 16:n20, Jun 8: R, Python Duel for 1st Place; “Regular” Machine Learning vs Deep Learning; Numpy Intro - Jun 8, 2016.
R, Python Duel As Top Analytics, Data Science software; What is the Difference Between Deep Learning and "Regular" Machine Learning; An Introduction to Scientific Python; How to Build Your Own Deep Learning Box
- Beyond Big Data Skills: Creating the Right DNA for the Managers of the Data-driven Business World - May 31, 2016.
Advice to schools and universities to help them prepare the future managers of the data-driven business world. One key step is to get managers acquainted with data by touching data, manipulating it, and 'playing' with it.
- KDnuggets™ News 16:n19, May 25: Explain Machine Learning to Software Engineer; 5 Can’t Miss Machine Learning Projects - May 25, 2016.
How to Explain Machine Learning to a Software Engineer; 5 Machine Learning Projects You Can No Longer Overlook; Doing Data Science: A Kaggle Walkthrough Part 1 - Introduction; The Amazing Power of Word Vectors
- 10 Must Have Data Science Skills, Updated - May 23, 2016.
An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.
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- Tips for Data Scientists: Think Like a Business Executive - May 18, 2016.
Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.
- KDnuggets™ News 16:n18, May 18: Annual Software Poll; Practical Data Science Skills; Creative Deep Learning - May 18, 2016.
Poll: What software you used for Analytics, Data Mining, Data Science; Practical skills that practical data scientists need; Are Deep Neural Networks Creative?; TPOT : A Python Tool for Automating Data Science
- Resume Tips for Early Career Analytics Professionals - May 16, 2016.
Trying to put together your first resume or two after graduation can be tricky. Without a lot of relevant work experience to highlight, sometimes none at all, graduates often wonder how they can adequately impress hiring managers with their analytics capabilities.
- Free Advice For Building Your Data Science Career - May 4, 2016.
Got hired as data scientist, where to go now from here? Understand how you can make the most of your career by following the different paths like managerial, consulting, or as a domain expert.
- Three Pitfalls to Avoid When Building Data Science Into Your Business - Apr 27, 2016.
An overview of pitfalls to avoid when building data science into your business, how to avoid them, and what to do instead.
- When Does Deep Learning Work Better Than SVMs or Random Forests? - Apr 22, 2016.
Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.
- Best Data Science, Machine Learning Blogs from Companies and Startups - Apr 21, 2016.
A collection of company data science blogs to follow and read. Top blogs have links to, and excerpts from, recent quality posts of particular interest.
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- Does Your Company Need a Data Scientist? - Apr 19, 2016.
Your company needs a data scientist... doesn't it? It very well may not, but you need to know either way. Read on to determine whether or not your company could benefit from the skills of an on-board data scientist.
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- What Developers Actually Need to Know About Machine Learning - Apr 14, 2016.
Some guidance on what, exactly, it is that developers need to know to get up to speed with machine learning.
- From Science to Data Science, a Comprehensive Guide for Transition - Apr 12, 2016.
An in-depth, multifaceted, and all-around very helpful roadmap for making the switch from 'science' to 'data science,' yet generally useful for data science beginners or anyone looking to get into data science.
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- A Pocket Guide to Data Science - Apr 11, 2016.
A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.
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- 3 Ways to Build an Analytics Dream Team - Apr 4, 2016.
So your March Madness bracket is busted. Maybe that new algorithm can through the first round next year. It's never too early to start building your analytics Dream Team.
- Don’t be afraid to Fail – Start Now with Data Science - Mar 30, 2016.
An argument for why aspiring data scientists should stop waiting for permission and start doing data science.
- How To Become A Machine Learning Expert In One Simple Step - Mar 29, 2016.
This post looks at perhaps the most important, and often overlooked, step in learning machine learning, an aspect which can make the biggest difference in one's skill set.
- Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department - Mar 28, 2016.
An exploration of data science team building, with insight into why engineers should not write ETL, and other not-so-subtle pieces of advice.
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- Don’t Buy Machine Learning - Mar 28, 2016.
In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a future phase after building the application first.
- Career Advice to Data Scientists – Go Make More Money - Mar 16, 2016.
Data Scientist should offer the enterprise more than the ability (and cost) of doing analysis, but behave as an executive with expertise in analysis and help lead the enterprise on decisions, investments, and operations.
- Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement - Mar 2, 2016.
In February on /r/MachineLearning, we get a run-down of the AlphaGo matches, Distributed TensorFlow is released, convolutional neural nets are cleaning Star Wars images, vintage science is on parade, military machine learning is criticized, and the overwhelmed researcher is given advice.
- Details on First Data Science Job Salary - Jan 29, 2016.
A person new to the Data Science field details their salary and the negotiation process.
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- 5 Ways Data Scientists Keep Learning After College - Dec 17, 2015.
Taken from the answers experts gave, here is a compiled list of 5 essential actions and attitudes that keep data scientists learning after their degrees.
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- Interview: Stefan Groschupf, Datameer on Why Domain Expertise is More Important than Algorithms - Aug 6, 2015.
We discuss large-scale data architectures in 2020, career path, open source involvement, advice, and more.
- Interview: Ali Vanderveld, Groupon on How Data Science is Changing the Global E-commerce Marketplace - Jul 17, 2015.
We discuss the tools used for data science, competitive landscape, journey from astrophysics to data science, advice, skills sought in data scientists, and more.
- Interview: Joseph Babcock, Netflix on Curiosity and Courage – Key for Success in Data Science - Jun 17, 2015.
We discuss discovery vs. personalization, advice, trends, desired skills in data scientists, and more.
- Is Analytics Career Right for You? - Jun 12, 2015.
An analytical way to decide whether you should pursue a career in analytics. We shared some economic ways to getting started and mind-set required for entering into this exciting field.
- Interview: Ranjan Sinha, eBay on Winner Insights from International Sorting Competitions - Jun 10, 2015.
We discuss advancements in the field of Personalization, lessons from winning sorting competitions, Data Science trends, career advice, and more.
- Interview: Sheridan Hitchens, Auction.com on Data Science Evolution from a Nerdy Hobby to a Strategic Priority - Jun 4, 2015.
We discuss the evolution of Data Science expectations, Data Science as a career, advice, and more.
- Interview: Antonio Magnaghi, TicketMaster on Why Honesty is Key for Analytics Success - May 19, 2015.
We discuss lessons from implementing lambda architecture, impact of Big Data on recommender systems, trends, advice, and more.
- Interview: Hobson Lane, SHARP Labs on How Analytics can Show You “All the Light You Cannot See” - May 14, 2015.
We discuss the impact of rapid growth in magnitude of data, programming skills for data science, major trends, advice, data science skills, and more.
- Interview: Alison Burnham, Scorebig on Optimal, Real-time Pricing through Analytics - May 8, 2015.
We discuss Analytics at ScoreBig, company’s business model, unexpected insights, challenges in customer value management, advice, and more.
- The Inconvenient Truth About Data Science - May 5, 2015.
Data is never clean, you will spend most of your time cleaning and preparing data, 95% of tasks do not require deep learning, and more inconvenient wisdom.
- Interview: Haile Owusu, Mashable on Surviving Imprecision in Digital Media Analytics - May 1, 2015.
We discuss the challenges in tracking social media sharing, advice, important trends, and more.
- Interview: Emmanuel Letouzé, Data-Pop Alliance on Big Data for Development and Future Prospects - Apr 25, 2015.
We discuss the field of Big Data for Development, current projects and future plans for Data-Pop Alliance, public participation opportunities, advice, and more.
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- Interview: Michael Li, Data Incubator on Bridging the Data Science Skills Gap between Academia and Industry - Apr 21, 2015.
We discuss the response from hiring companies, recommendations for aspirants, retaining data science talent, advice, and more.
- Interview: Ksenija Draskovic, Verizon on Conquering Fear and Cherishing Creativity for Success in Data Science - Apr 17, 2015.
We discuss career advice, motivation, key qualities sought in Data Science practitioners, and more.
- Interview: Xia Wang, AstraZeneca on Big Data and the Promise of Effective Healthcare - Apr 10, 2015.
We discuss challenges in analyzing text data, Big Data impact on translational bioinformatics, advice, desired skills in data scientists, and more.
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- Interview: Ravi Iyer, Ranker on Dealing with Inherent Bias in Crowdsourcing Data - Apr 8, 2015.
We discuss the challenges of analyzing crowdsourcing data, tools and technologies, competitive landscape, advice, trends, and more.
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- Interview: Beth Diaz, Washington Post on How Dark Social is Shadowing Modern Analytics - Apr 6, 2015.
We discuss recent events at Washington Post, growth initiatives, the growing pain of Dark Social, how to deal with it, audience analytics, advice and more.
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- Interview: Alessandro Gagliardi, Glassdoor on the Fun and Boring Part of Data Scientist Job - Apr 3, 2015.
We discuss interesting trends, motivation, different aspects of data scientist job, advice, and more.
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- A Data Scientist Advice to Business Schools - Apr 1, 2015.
To remain relevant business school graduates must learn to speak to Data Scientists, whose domain expertise is playing a vital role in an organization's ability to compete in today's market.
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- Interview: Bill Moreau, USOC on the Pursuit of a Career in Sports Analytics - Mar 28, 2015.
We discuss challenges in applying Data Analytics to sports, advice to beginners in the field of Sports Analytics, and more.
- Interview: Brad Klingenberg, StitchFix on Decoding Fashion through Analytics and ML - Mar 21, 2015.
We discuss the challenges in making personal styling recommendations, unexpected insights, interesting trends, motivation, advice, desired qualities in data scientists and more.
- Interview: Vince Darley, King.com on What do you need to become Top Grossing Game - Mar 19, 2015.
We discuss common characteristics of games that achieved top ranking, career advice, trends, desired qualities in data scientists and more.
- Interview: Kenneth Viciana, Equifax on Data Governance – Red Tape or Catalyst? - Mar 14, 2015.
We discuss recommendations for Data Governance policies, advice, Big Data trends, qualities sought in Data Scientists, and more.
- Interview: Josh Hemann, Activision on Why the Tolerance for Ambiguity is Vital - Mar 12, 2015.
We discuss handling bias in data, other data quality concerns, advice, desired qualities, and more.
- Interview: Slava Akmaev, Berg on Challenges in Transitioning Analytics to Clinical Utility - Mar 10, 2015.
We discuss Analytics use cases, challenges in relating molecular/clinical data to real-life outcomes, Healthcare Analytics trends and more.
- Interview: Kaiser Fung, NYU on Why Statistical Reasoning is more important than Number Crunching - Mar 5, 2015.
We discuss why every individual should care about statistics, inspiration behind the book Numbersense, teaching statistics as liberal arts, Junk Charts blog, advice and more.
- Interview: Ted Dunning, MapR on Apache Mahout & Technology Landscape in ML - Mar 3, 2015.
We discuss Apache Mahout, its comparison with Spark and H2O, trends, advice, desired qualities in data scientists and more.
- Interview: Rachel Hawley, SAS on Why Data Science Needs Communication Skills - Feb 4, 2015.
We discuss SAS Analytics Center of Excellence, trends, advice, desired skills in data science and more.
- Interview: Eli Collins, Cloudera on Evolution and Future of Big Data Ecosystem - Feb 2, 2015.
We discuss the change in Big Data priorities, risks, Big Data ecosystem, rise of data culture in organizations, challenges, advice and more.
- Interview: Nandu Jayakumar, Yahoo on What Does One Need for Big Data Success - Jan 27, 2015.
We discuss Yahoo’s contributions to Big Data ecosystem, recommendation to Big Data vendors, predictions for Big Data, advice, and more.
- Interview: Miriah Meyer, Univ. of Utah on the Art and Science of Visualization - Jan 12, 2015.
We discuss insights from the best paper at ACM AVI 2014, increasing interest in visualization, infographics, trends, challenges, advice and more.
- Interview: Sharmila Mulligan, ClearStory Data on Variety & Velocity to be Big Data Priorities - Jan 10, 2015.
We discuss the ClearStory Data’s competitive differentiation, client use case, Big Data trends, advice, desired soft skills in data scientists and more.
- Interview: Ben Werther, CEO, Platfora on Insightful Analytics for Big Data - Dec 30, 2014.
We discuss the challenges in implementing end-to-end solutions for Big Data, Platfora use cases, Big Data trends, advice and more.
- Interview: Mac Devine, CTO, IBM Cloud on the Conflux of Cloud, IoT & Big Data - Dec 26, 2014.
We discuss the implications of Cloud Speed of technological advancement, significant trends in Internet of Things (IoT), future of cloud computing and more.
- Interview: Igor Elbert, Gilt on Boosting Sales through Analytics-curated Shopping - Dec 4, 2014.
We discuss Analytics at Gilt, unique Analytics challenges of a flash sales portal, consumer behavior across channels, interesting insights, advice and more.
- Interview: Philip Maymin, NYU on Why Sports should Embrace Analytics? - Nov 22, 2014.
We discuss how the increasing use of Analytics will change the game of basketball, the concern of Analytics ruining the game, significant trends, advice and more.
- Interview: Toni Jones, U-Haul on Deriving Business Insights from Social Media - Oct 5, 2014.
We discuss social media strategy at U-Haul, the key drivers of a social media campaign, identifying what data to focus on, important metrics, career advice and more.
- Exclusive Interview: Ajay Bhargava, TCS on the Ideal Analytics Curriculum at Graduate-level - Sep 5, 2014.
We discuss the differences between analytics and Big Data, the evolution of expectations from data science, important qualities desired in data scientists, ideal curriculum for Analytics focused programs, advice and more.
- Interview: Debora Donato, StumbleUpon on the Secret Sauce of Impressive Content Curation - Aug 28, 2014.
We discuss the role of data science at StumbleUpon, the shift from search to discovery, metrics for user engagement, the art of collaborative filtering, how native ads improve user experience, major trends, advice and more.
- Interview: Arpit Gupta, CEO, Actionable Analytics on Enterprise Challenges in Big Data and Cloud - Aug 24, 2014.
We discuss Actionable Analytics start-up, enterprise challenges in Big Data, relationship with cloud computing, metrics vs. insights, Big Data expectations and more.
- Interview: Pedro Domingos: the Master Algorithm, new type of Deep Learning, great advice for young researchers - Aug 19, 2014.
Top researcher Pedro Domingos on useful maxims for Data Mining, Machine Learning as the Master Algorithm, new type of Deep Learning called sum-product networks, Big Data and startups, and great advice to young researchers.
- Interview: John Funge, CTO, Knack on Why Gaming is the Next Big Thing for Hiring - Aug 18, 2014.
We discuss the gamification of hiring, founding story of Knack, applications of Predictive Human Analytics, challenges, Big Data tools and technology used, key qualities sought in data scientists, career advice and more.
- Interview: Pallas Horwitz, Blue Shell Games on Why Gaming Analytics is Not a Piece of Cake - Aug 15, 2014.
We discuss the challenges of gaming analytics, most desired missing data, current trends, career advice, important soft skills in data science and more.
- Interview: Sujee Maniyam, Elephant Scale on Why Open Source is So Important for Big Data - Aug 8, 2014.
We discuss the importance of contributing to Open Source, Big Data skills for business managers, Big Data predictions, key qualities sought in data engineers, career advice and more.
- Interview: Thomas Levi, PlentyOfFish on What does Big Data tell us about Romance - Jul 30, 2014.
We discuss interesting research on the state of romance in US, how PlentyOfFish is managing competition, personal journey from String Theory to Data Science, career advice and more.
- Interview: Amy Gaskins, AVP, MetLife on New Era Hiring at MetLife through Synapse - Jul 23, 2014.
We discuss the motivation behind launch of Synapse, updating the recruitment process to meet today's needs, data science trends, career advice, important soft skills and more.
- Interview: Cliff Lyon, Stubhub on Mastering Recommendation & Personalization Analytics Part 2 - Jul 19, 2014.
We discuss current trends, future vision, interesting correlations, privacy concerns, and advice for Data Science practitioners.
- Interview: Piero Ferrante, BCBS on Why Healthcare is Rich in Data but Poor in Information - Jul 17, 2014.
We discuss role of analytics in healthcare payer firms, major challenges in leveraging healthcare data, shift to value-based payments, personal motivation towards analytics, career advice and more.
- Interview: Marc Smith, Chief Social Scientist, Connected Action, on Why We Need Open Tools for Social Networks - Jul 14, 2014.
We discuss NodeXL impact stories, upcoming NodeXL features, importance of an open environment, future of social media analytics, advice for novice researchers and more.
- Interview: Amy Gershkoff, Director of Customer Analytics & Insights, eBay on How to Design Custom In-House BI Tools - Jul 11, 2014.
We discuss key principles for designing business intelligence tools, exploring causation based on correlation insights, attributes of future Analytics leaders, interesting Big Data trends, important qualities in data scientists and more.
- Interview: Samaneh Moghaddam, Applied Researcher, eBay on Opinion Mining – Typical Projects and Major Challenges - Jun 27, 2014.
We discuss typical sentiment analysis problems at eBay, underrated challenges, career motivation, important soft skills and more.
- Interview: Lloyd Tabb, Chairman & CTO, Looker on Front-line Analytics and Data Democratization - Jun 9, 2014.
We discuss the capabilities of Looker, data democratization across organization, change in the tools being used by analytics-savvy business managers, front-line analytics, competitive landscape and more.
- Interview: Tom Kern, Risk Modeling Manager, Paychex on Risk Analytics and Sales Anticipation Model - Jun 2, 2014.
We discuss the role of Risk Analytics at Paychex, strategic importance of Sales Anticipation Model, optimizing business processes by leveraging Big Data, and advice for companies thinking about Big Data as well as aspiring students.
- Interview: Kirk Borne, Data Scientist, GMU on Decision Science as a Service and Data Science curriculum - May 31, 2014.
We discuss Kirk's role at Syntasa, the concept of "Decision Science as a Service", key components of a well-designed Data Science education curriculum, advice for young aspirants and more.
- Interview: Richard Wendell, VP, Data Science, TE Connectivity on the Role of Analytics in Organizations - May 24, 2014.
We discuss organizational structure of data science team, making Analytics an integral component of all projects, future of Big Data and crucial soft-skills for aspiring practitioners.
- Interview: Dale Russell, CTO, Talksum on Building Talksum Router and Real-time Anaytics - May 21, 2014.
We discuss challenges in building Talksum data stream solution, current trends in real-time analytics, advice for Data Science aspirants and more.
- Interview: Gary Shorter, Quintiles on Future of Heathcare and Big Data Skills - May 16, 2014.
We discuss how Big Data is shaping the future of Healthcare industry and advice for career in Analytics.
- Interview: Vasanth Kumar, Principal Data Scientist, Live Nation - May 2, 2014.
We discuss challenges in analyzing bursty data, real-time classification, relevance of statistics and advice for newcomers to Data Science.
- Top stories in March: Machine Learning in 7 Pictures; How Many Data Scientists? - Apr 2, 2014.
Also - The Dos and Donts of Data Mining; Is Data Scientist the right career path for you - Candid advice.
- Top KDnuggets tweets, Mar 28-30: SAS vs R vs Python, ecosystem comparison; Practical Data Science with R - Mar 31, 2014.
SAS vs. R vs. Python - Which should you learn?; New Book: Practical Data Science with R ; Is Data Scientist the right career path for you? Candid advice; Must read books for people interested in Analytics.
- Is Data Scientist the right career path for you? Candid advice - Mar 28, 2014.
Candid advice from an industry veteran reveals the true picture behind the much-talked-about Data Scientist "glamour" and helps people have the right expectations for a Data Science career.
- Fractal Analytics Interview Highlights - Mar 27, 2014.
Fractal Analytics CEO on starting the company, competing with the best, managing attrition, attributes he looks for when hiring, 4 different analytics career tracks, strategic bets, and advice for starting data scientists.
- KDnuggets Exclusive: Interview with Anjul Bhambhri, VP of Big Data Products at IBM - Mar 25, 2014.
KDnuggets talks with Anjul Bhambhri, IBM’s Vice President of Big Data Products about Big Data Trends, developing the Big Data capabilities in-house vs. outsourcing, five crucial steps to adopting a success big data strategy and advice for beginners.
- KDnuggets Exclusive: Part 2 of the interview with Quentin Clark, CVP, Microsoft Data Platform Group - Mar 6, 2014.
We discuss Microsoft decision to embrace Hadoop as the standard, collaboration with Hortonworks, and advice to newbies in Data Science.