Topics: 
Apr
2016 May
All (103) | Courses, Education (3) | Meetings (12) | News, Features (26) | Opinions, Interviews, Reports (34) | Software (5) | Tutorials, Overviews (17) | Webcasts & Webinars (6)
- 5 Reasons Machine Learning Applications Need a Better Lambda Architecture
- Jun 2, 2016.
The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.
- Analyzing Log Data with Spark and Looker
- May 31, 2016.
Learn how to set up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data. This on-demand webinar discusses popular collection mechanisms, does a hands-on log-parsing example in Spark, and shows how to use Looker to get insights from event data.
- Introduction to Recurrent Networks in TensorFlow
- May 31, 2016.
A straightforward, introductory overview of implementing Recurrent Neural Networks in TensorFlow.
- Intel’s Investments in Cognitive Tech: Impact and New Opportunities
- May 31, 2016.
An overview of Intel's recent investments in cognitive technology, the impact of these investments on technology and research, and the new opportunities these investments present.
- Jupyter+Spark+Mesos: An “Opinionated” Docker Image
- May 31, 2016.
Check "opinionated" Docker-based stacks for Jupyter, including one to combine Jupyter and Spark right out of the gate.
- 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.
- Top 10 Open Dataset Resources on Github
- May 31, 2016.
The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.
- Interacting with Machine Learning – Here is Why You Should Care
- May 30, 2016.
The issue of designing new interactive interfaces with machine learning systems that best serve our needs and help us build and maintain trust is a central issue in AI. Read one researcher's take on this topic.
- Top Stories, May 23-29: Machine Learning Key Terms, Explained; 10 Must Have Data Science Skills, Updated
- May 30, 2016.
Machine Learning Key Terms, Explained; 10 Must Have Data Science Skills, Updated; A Concise Overview of Standard Model-fitting Methods; Free eBook: Healthcare Social Media Analytics and Marketing; 7 Steps to Mastering Machine Learning With Python
- Hadoop Key Terms, Explained
- May 30, 2016.
An straightforward overview of 16 core Hadoop ecosystem concepts. No Big Picture discussion, just the facts.
- Predicting Popularity of Online Content
- May 30, 2016.
A look at predicting what makes online content popular, with a particular focus on images, especially selfies.
- Free eBook: Healthcare Social Media Analytics and Marketing
- May 27, 2016.
Get your free copy of a new ebook outlining social media marketing and analytics strategies (including code) for healthcare professionals.
- A Concise Overview of Standard Model-fitting Methods
- May 27, 2016.
A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.
- CRN Big Data Startups to Watch in 2016
- May 27, 2016.
The CRN editorial team has released its annual Big Data 100 report for 2016, which includes the 55 Big Data Startups to Watch in 2016. Get the info here.
- Doing Data Science: A Kaggle Walkthrough Part 2 – Understanding the Data
- May 27, 2016.
This is the second post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Read on for a great overview of practicing data science.
- Make Your Clients Look Like Superheroes With Actionable Data
- May 27, 2016.
Data scientists are in high demand to help companies gain better market information so they can make more informed decisions, and ultimately improve ROI. St. Mary's College Master of Science in Data Science can help!
- Data Scientist Salaries by City, Analyzed
- May 27, 2016.
This post will provide a quick overview the current state of Data Scientist salaries in the US, and performs some data analysis in concert with some additional data.
- Automakers Must Partner Around Big Data
- May 26, 2016.
A discussion on the need for auto manufacturers to come together and leverage Big Data.
- Trust and Analytics in the Banking Sector
- May 26, 2016.
This post explores the intricate relationship between customers, trust, and analytics in the banking sector, and offer actions that banks may need to take to assess the way they assure trust across the analytics lifecycle.
- SAP Predictive Analytics Interview with Sven Bauszus
- May 26, 2016.
I talk with Sven Bauszus, SAP Predictive Analytics global leader, about their main products in Business and Predictive Analytics and Big Data space, analytics maturity by industry, the automation of Data Science, and "citizen" Data Scientists.
- CRN Top Data Management Technologies Vendors 2016
- May 26, 2016.
The CRN editorial team has released its annual Big Data 100 report for 2016. Check out which companies made the list of Data Management Vendors.
- 5 Ways in Which Big Data Can Help Leverage Customer Data
- May 25, 2016.
Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.
- Top KDnuggets tweets, May 18-24: Google supercharges #MachineLearning, #DeepLearning tasks with TPU (Tensor Processing Unit)
- May 25, 2016.
Stanford Crowd Course Initiative: #MachineLearning with #Python course; Practical Guide to Matrix Calculus for #DeepLearning; Build your own #DeepLearning Box < $1.5K
- Be Part of Spark Summit 2016, the Premier Big Data Event Dedicated to Apache Spark
- May 25, 2016.
Whether you’re an Apache Spark newbie or a hardcore enthusiast, Spark Summit, June 6-8 in San Francisco, is the place to be to gain new insights and make valuable connections. Use promo code KDNuggets to save 15%
- Machine Learning Key Terms, Explained
- May 25, 2016.
An overview of 12 important machine learning concepts, presented in a no frills, straightforward definition style.
- CRN Top Platform and Tools Vendors 2016
- May 25, 2016.
The CRN editorial team has released its annual Big Data 100 report for 2016. Check out which companies made the list of Platform and Tools Vendors.
- Let Me Hear Your Voice and I’ll Tell You How You Feel
- May 24, 2016.
This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.
- The Good, Bad & Ugly of TensorFlow
- May 24, 2016.
A survey of six months of rapid evolution (+ tips/hacks and code to fix the ugly stuff) using TensorFlow. Get some great advice from the trenches.
- Harnessing Open Data Science for Predictive Analytics (Whitepaper)
- May 24, 2016.
Discover how Anaconda, the leading Open Data Science platform, enables powerful predictive analytic solutions for enterprises.
- CRN Top Business Analytics Vendors 2016
- May 24, 2016.
The CRN editorial team has released its annual Big Data 100 report for 2016. Check out which companies made the list of Business Analytics Vendors.
- Don’t Just Assume That Data Are Interval Scale
- May 23, 2016.
Is the interval scale assumption of your data justified? Research suggests that it may not be, and that applying scale transformations often improves performance.
- Top stories, May 16-22: Annual KDnuggets Analytics Software Poll; How to Explain Machine Learning to Software Engineers
- May 23, 2016.
Annual KDnuggets Analytics Software Poll; 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 Data Science Market: 2016 Compensation Insights
- May 23, 2016.
Burtch Works shares the annual update to their highly regarded data science salary report series. This year's report details the compensation and demographic data on 374 data scientists.
- 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.
- How to Explain Machine Learning to a Software Engineer
- May 20, 2016.
How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.
- Boosting Productivity of the Next-Generation Data Scientist: IBM June 6 event
- May 20, 2016.
On June 6, IBM will share important announcements for making R, Spark, and open data science a sustainable business reality at the Apache Spark Maker Community Event in San Francisco, Attend in person or watch live.
- What are the Challenges of the Analytics of Things?
- May 20, 2016.
without the AoT, it is difficult to realize the full potential of the IoT. We review the promise and challenges of Analytics of Things, including data, security, analytics implementation, standartization, and more.
- Doing Data Science: A Kaggle Walkthrough Part 1 – Introduction
- May 19, 2016.
This is the first post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Very thorough, and very insightful.
- Six PAW Chicago Sessions That Show Analytics’ Long Reach
- May 19, 2016.
At Chicago's Predictive Analytics World for Business conference, June 20-23, 2016, explore case studies from a range of industries and discuss best practices for infusing organizations with the power of analytics in new and innovative ways. KDnuggets subscribers enjoy $150 off!
- 5 Machine Learning Projects You Can No Longer Overlook
- May 19, 2016.
We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.
- Top KDnuggets tweets, May 11-17: Vote: What software you used for Analytics, Data Mining, Data Science projects?
- May 18, 2016.
Vote: What software you used for Analytics, Data Mining, Data Science projects? Useful #Cheatsheet: #Python, R #rstats code for #MachineLearning Algorithms; TPOT: A #Python Tool for Automating Data Science; Randomize Acceptance of Borderline Research Papers, save 25 reviewer person-years.
- 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.
- The Amazing Power of Word Vectors
- May 18, 2016.
A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.
- Spark 2.0 Preview Now on Databricks Community Edition: Easier, Faster, Smarter
- May 17, 2016.
The preview of Spark 2.0 is here, and it promises to be easier, faster, and smarter.
- An Introduction to Semi-supervised Reinforcement Learning
- May 17, 2016.
A great overview of semi-supervised reinforcement learning, including general discussion and implementation information.
- HR/Workforce Analytics leadership conference/London/Innovation Enterprise: Summary
- May 17, 2016.
Two intense days, buzzing with energy, knowledge exchange, panel discussions, in short London Data Festival was a great place to be if you are a data scientist. Here is summary of speakers and major attractions of the event.
- Uplift modeling, advanced techniques, analytics – Oh My!
- May 17, 2016.
Check hot topics (including uplift modeling) covered at Predictive Analytics World in Chicago, June 20-23, and register today with code KDN150 for $150 off.
- High Performance Python for Open Data Science (Whitepaper)
- May 17, 2016.
In this whitepaper, you'll learn from our seasoned experts about the approaches to scaling your data science models, review the various options, and learn how to easily accomplish them using Anaconda, the leading Open Data Science platform powered by Python.
- Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers
- May 16, 2016.
A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.
- Top stories for May 9-15: Data scientists mostly just do arithmetic, Data Scientists – future-proof yourselves
- May 16, 2016.
Data scientists mostly just do arithmetic and that’s a good thing; Data Scientists – future-proof yourselves; Are Deep Neural Networks Creative?; Implementing Neural Networks in Javascript; 7 Steps to Mastering Machine Learning With Python
- Data Mining Panama Papers & Graph Analytics – Two Upcoming Webinars
- May 16, 2016.
Ontotext offers a pair of free live webinars: Diving in Panama Papers and Open Data to Discover Emerging News, and GraphDB Fundamentals: Transforming your Graph Analytics with GraphDB. Reserve your spot today.
- 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.
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Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
- May 14, 2016.
Vote in KDnuggets 17th Annual Poll: What software you used for Analytics, Data Mining, Data Science Machine Learning projects in the past 12 months? We will clean and analyze the results and publish our analysis afterwards. - The Good, The Bad, and The Deep Algorithms… at MLconf Seattle, May 20
- May 13, 2016.
MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning.
- Practical skills that practical data scientists need
- May 13, 2016.
The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.
- Troubleshooting Neural Networks: What is Wrong When My Error Increases?
- May 13, 2016.
An overview of some of the things that could lead to an increased error rate in neural network implementations.
- TPOT: A Python Tool for Automating Data Science
- May 13, 2016.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
- How Bing Predicts is forecasting everything from sports to political outcomes
- May 13, 2016.
Bing Predicts is an innovative feature which now regularly makes headlines for its ability to analyze massive amounts of Web activity to forecast the outcomes of elections, voting-based reality TV shows, sports matchups and more.
- Are Deep Neural Networks Creative?
- May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
- Deep Learning and Neuromorphic Chips
- May 12, 2016.
The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?
- Implementing Neural Networks in Javascript
- May 12, 2016.
Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.
- Innovation in Data Analytics, help shape Singapore’s Smart Nation
- May 12, 2016.
Read a first-hand perspective on Big Data playing field in Singapore, strong support for Machine Learning and Data Science research, excellent local conditions, and how all these contribute to a bigger aspiration this city state is striving towards.
- Top KDnuggets tweets, May 4-10: Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages
- May 11, 2016.
Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages; Why AI development is going to get even faster; Why Implement #MachineLearning Algorithms From Scratch?
- Big Data: Content and Technology
- May 11, 2016.
A discussion of using Big Data to provide insight into the big economic questions, and the big expectations that come along.
- Top Talks and Tutorials From PyData London
- May 11, 2016.
Get some insight into the most recent Python data science talks and presentations with this eclectic mix of videos from PyData London 2016.
- Meet predictive analytics greatest minds (some of them) in Chicago
- May 10, 2016.
Check the keynote presentations on Weird Science, Persuasion Modeling in Presidential Campaigns, and Rethinking Analytics in Service-oriented world. Register now with code KDN150 to get double discount.
- Data Scientists – future-proof yourselves
- May 10, 2016.
Here are 7 suggestions for Data Scientist to make themselves future-proof and get skills for a successful Data Science career in the future.
- How Can Marketing Analytics & Data Science Increase ROI?
- May 10, 2016.
In our Marketing Analytics & Data Science interview series, we are catching up with thought leaders across industries to hear their take on trends and challenges in Big Data ahead of MARKETING ANALYTICS & DATA SCIENCE CONFERENCE, June 8-10, San Francisco. Save 20% with Code MADS16KDN
- Data scientists mostly just do arithmetic and that’s a good thing
- May 10, 2016.
Are you also wondering how you can get started as data scientist, and become a valuable team player. Understand what really matters as data scientist, and things to focus in the initial stages.
- Data Science and Cognitive Computing with HPE Haven OnDemand: The Simple Path to Reason and Insight
- May 10, 2016.
HPE Haven OnDemand is a diverse collection of APIs for interacting with data designed with flexibility in mind, allowing developers to quickly perform data tasks in the cloud. See why it is a simple path to reason and insight for data science and cognitive computing.
- Chief Data & Analytics Officer Forum, Melbourne, Australia, 5-7 September
- May 10, 2016.
The CDAO Forum in Melbourne, 5-7 Sep 2016 will bring over 150 leading data and analytics executives from the region to share their insights on how to develop the infrastructure, ecosystem, buy-in, and culture to use data to drive business advantage. Get KDnuggets discount with code CDAOMKDN.
- Artificial Intelligence ‘Chatbots’ – When or if?
- May 9, 2016.
Chatbots can have extensive applications, now that Facebook is considering to implement AI in their Messenger and WhatsApp platforms. We examine 3 main factors that will determine the success of chatbots.
- Big Data Innovation, Data Visualization, IoT Summits, Boston, Sep 8-9
- May 9, 2016.
Save the date for 3 Boston Summit, Sep 8-9: Big Data Innovation, Data Visualization, and Internet of Things. Supersaver rates end June 10.
- UC Analytics Summit, May 20
- May 9, 2016.
UC Analytics Summit is a day-long immersion into the practice of applying analytic methods to solve real-world problems. Hear seasoned practitioners and thought leaders from 17 of the top companies and organizations.
- There is more to a successful data scientist than mere knowledge
- May 9, 2016.
Look at Data scientist "definitions" with a wry smile: the "essential" skills very much reflect those that a short time ago were quite novel, and are being used in applications to problems that have recently become solvable.
- Top stories for May 1-7: Why Implement Machine Learning Algorithms From Scratch? 7 Steps to Mastering Machine Learning With Python
- May 8, 2016.
How to Use Cohort Analysis to Improve Customer Retention; Why Implement Machine Learning Algorithms From Scratch?; 7 Steps to Mastering Machine Learning With Python; R vs Python for Data Science: The Winner is ...
- Want to become a data scientist?
- May 7, 2016.
Logit has reduced the tuition for its first immersive Data Science program in Southern California by over 66%, thanks to the grant it received. Learn more and apply for June cohort at www.logitdatascience.com.
- Meet the 11 Big Data & Data Science Leaders on LinkedIn
- May 6, 2016.
In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!
- Why Implement Machine Learning Algorithms From Scratch?
- May 6, 2016.
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
- Ten Signs of Data Science Maturity – free O’Reilly ebook
- May 6, 2016.
Two leading data scientists at the consulting firm Booz Allen Hamilton describe ten characteristics of a mature data science capability.
- Data-Driven Product, Data Security Innovation Summits, Austin June 29-30
- May 6, 2016.
Learn how to transform your products at Data-Driven Product Innovation Summit, and about Big Data risks and solutions at Data security summit. Use code KD10 to get an extra 10% off.
- Will Predictive Hiring Algorithms Replace or Augment your HR Decisions?
- May 6, 2016.
Corporate recruiters spend an average of 6 seconds on every resume. Predictive screening algorithms can help identify good candidates, and help recruiters to do a better job.
- Stanford Webinar: The Secret to a Perfect Search, May 17
- May 5, 2016.
What makes “googling” the best way to search for something on the Internet? Join Rajan Patel, Stanford Instructor and Google Engineer, as he shares insights into the manipulation of large complex data sets and how you can turn them into valuable, actionable information for your company.
- Improve your processes – with statistical models
- May 5, 2016.
Learn how to solve more scientific, engineering and business problems correctly and faster by extracting powerful insights from existing data using proven, simple statistical modeling methods. Watch the webcast.
- From Insight-as-a-Service to Insightful Applications
- May 5, 2016.
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
- Top April stories: 10 Essential Books for Data Enthusiast; When Deep Learning is better than SVMs or Random Forests?
- May 5, 2016.
Top 10 Essential Books for the Data Enthusiast; 10 Signs Of A Bad Data Scientist; When Does Deep Learning Work Better Than SVMs or Random Forests?; Comprehensive Guide to Learning Python for Data Science and more.
- Spark with Tungsten Burns Brighter
- May 4, 2016.
Apache Spark is one of “the hottest technology” for data science and analytics. A project called Tungsten represents a huge leap forward for Spark, particularly in the area of performance. Understand how it works, and why it improves Spark performance so much.
- 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.
- How to Quantize Neural Networks with TensorFlow
- May 4, 2016.
The simplest motivation for quantization is to shrink neural network representation by storing the min and max for each layer. Learn more how to perform quantization for deep neural networks.
- How Much do Analytics Salaries Increase when Changing Jobs?
- May 4, 2016.
A data-informed analysis of analytics career salaries and their increase when changing jobs.
- A Data Science Approach to Writing a Good GitHub README
- May 4, 2016.
Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.
- Top KDnuggets tweets, Apr 27 – May 3: Trifecta: Python, Machine Learning, and Dueling Languages; Fun game 4 #MachineLearning newbies
- May 4, 2016.
Trifecta: #Python, #MachineLearning, + Dueling Languages; Cartoon: When #Automation Goes Too Far; #AI Speed: 2-year old #xkcd cartoon: cannot check if a photo has a bird; Removing Duplicates in #BigData.
- Webinar: Predictive Analytics: Failure to Launch [May 10]
- May 3, 2016.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinar is May 10.
- 90+ upcoming May – December Meetings in Analytics, Big Data, Data Mining, Data Science
- May 3, 2016.
Coming soon: TDWI Chicago, Apache Big Data Vancouver, Deep Learning Summit Boston, Data by the Bay Oakland, Biz Analytics Summit Chicago, KxCon Montauk, Open Data Science Boston, In-Memory Computing San Francisco, and many more.
- Learn to apply data and predictive analytics to meet business objectives
- May 3, 2016.
The Penn State World Campus online MS in Data Analytics/Business Analytics focuses on exploring and analyzing large data sets to support data-driven business decisions. Complete and submit your application by July 1 to start taking classes in August.
- Chief Data & Analytics Officer Forum, Singapore, 27-28 July, 2016
- May 3, 2016.
Join 100+ CAOs, CDOs, and other leading data and analytics professionals who share their insights on developing the infrastructure, ecosystem, culture, and strategies to turn data into a strategic asset.
- Datasets Over Algorithms
- May 3, 2016.
The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
- How to Network and Build a Personal Brand in Data Science
- May 2, 2016.
SpringBoard shares some ideas on how to network and build a data career, as taken from a new guide they have put together on the topic.
- Attend In-Memory Computing Summit, May 23-24, San Francisco
- May 2, 2016.
This conference is the only industry-wide event of its kind, tailored to in-memory computing related technologies and solutions. The IMC Summit brings together in-memory computing visionaries, decision makers, experts and developers for the purpose of education, discussion and networking. Get 20% off with code KDN20.
- Top /r/MachineLearning Posts, April: New Google Machine Learning Videos, Deep Learning Book, TensorFlow Playground
- May 2, 2016.
Check out the most popular topics on Reddit's Machine Learning subreddit from April, including TensorFlow, deep learning, tutorials, self-reflection, and free books.
- How to Use Cohort Analysis to Improve Customer Retention
- May 2, 2016.
Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in April 2016
- May 2, 2016.
Academic/Research positions Analytics and Data Science in Zurich, Hatfield-UK, Paris, Ningbo-China, Tianjin-China, Melbourne, Buffalo-NY, Birmingham-UK, and Tampere-Finland.
- Top stories, Apr 24-30: How to Remove Duplicates in Large Datasets; The “Thinking” Part of “Thinking Like A Data Scientist”
- May 1, 2016.
7 Steps to Mastering Machine Learning With Python; When Does Deep Learning Work Better Than SVMs or Random Forests; How to Remove Duplicates in Large Datasets; The "Thinking" Part of "Thinking Like A Data Scientist".
