- Data Science of Visiting Famous Movie Locations in San Francisco - Jul 30, 2016.
Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.
CA, Data Science, Google, IMDb, Python, San Francisco
- Theoretical Data Discovery: Using Physics to Understand Data Science - Jul 29, 2016.
Data science may be a relatively recent buzzword, but the collection of tools and techniques to which it refers come from a broad range of disciplines. Physics has a wealth of concepts to learn from, as evidenced in this piece.
Data Science, Physics, Quantum Computing
- Build vs Buy – Analytics Dashboards - Jul 29, 2016.
Read this post on choosing between available analytics dashboard options, and designing your own. Get an informed opinion.
Analytics, Dashboard
- Data Science Statistics 101 - Jul 28, 2016.
Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.
Beginners, Data Science, Statistics
- 7 Steps to Understanding NoSQL Databases - Jul 27, 2016.
Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.
7 Steps, Cassandra, Database, Graph Databases, HBase, MongoDB, Neo4j, NoSQL
- Internet of Things Key Terms, Explained - Jul 27, 2016.
This post will define 12 Key Terms for the Internet of Things, in straightforward manner.
API, Explained, Industrial Internet, Internet of Things, IoT, Key Terms
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 2 - Jul 26, 2016.
This is part 2 of a 3 part introductory series on machine learning in Python, using the Titanic dataset.
Pages: 1 2
Machine Learning, Python, Titanic
- Data Science for Beginners 1: The 5 questions data science answers - Jul 26, 2016.
A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.
Beginners, Data Science, Microsoft, Question answering
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1 - Jul 25, 2016.
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
Machine Learning, Python, scikit-learn, Titanic
- 35 Open Source tools for Internet of Things - Jul 25, 2016.
If you have heard about the Internet of Things many times by now, its time to join the conversation. Explore the many open source tools & projects related to Internet of Things.
Pages: 1 2 3
Internet of Things, IoT, Open Source, Tools
- SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? - Jul 22, 2016.
There are lots of flame wars involving different data science and analytics tools... but this isn't one of them. Check out the quantitative results and analysis of a Burtch Works survey on the subject.
Burtch Works, Python, R, SAS, Survey
- 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!
Pages: 1 2
Advice, Career, Data Science, Data Scientist, Dataquest, Machine Learning, Portfolio, Project, Python
- Multi-Task Learning in Tensorflow: Part 1 - Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
Pages: 1 2
Machine Learning, Neural Networks, TensorFlow
- In Deep Learning, Architecture Engineering is the New Feature Engineering - Jul 19, 2016.
A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.
Architecture, Deep Learning, Feature Engineering, Neural Networks
- What the Next Generation of IoT Sensors Have in Store - Jul 19, 2016.
This post is an overview of some of the next-generation IoT sensors, and what they could mean for our future.
Internet of Things, IoT, Sensors
- MNIST Generative Adversarial Model in Keras - Jul 19, 2016.
This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.
GANs, Generative Models, Keras, MNIST
- Statistical Data Analysis in Python - Jul 18, 2016.
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.
IPython, Jupyter, Pandas, Python, Statistical Analysis
- Why Big Data is in Trouble: They Forgot About Applied Statistics - Jul 18, 2016.
This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago.
Applied Statistics, Big Data, Google, Statistics
- Predictive Analytics Introductory Key Terms, Explained - Jul 18, 2016.
Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.
Book, Eric Siegel, Explained, Key Terms, Predictive Analytics
- America’s Next Topic Model - Jul 15, 2016.
Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model.
LDA, NLP, Python, Text Mining, Topic Modeling, Unsupervised Learning
- 10 Algorithm Categories for AI, Big Data, and Data Science - Jul 14, 2016.
With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.
AI, Algorithms, Big Data, Data Science
- How to Start Learning Deep Learning - Jul 14, 2016.
Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.
Andrej Karpathy, Coursera, Deep Learning, edX, Geoff Hinton, Neural Networks
- What Data Scientists Can Learn From Qualitative Research - Jul 14, 2016.
Learn what data scientists can learn from qualitative researchers when it comes to analysing text, and how this relates to writing quality code.
Programming, Qualitative Analytics, Qualitative Research, Text Analytics
- Bayesian Machine Learning, Explained - Jul 13, 2016.
Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.
Bayesian, Explained, LDA, Machine Learning
- TalkingData Data Science Competition: understand mobile users - Jul 12, 2016.
Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.
China, Competition, Kaggle, Mobile, TalkingData, Turi
- 5 Deep Learning Projects You Can No Longer Overlook - Jul 12, 2016.
There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.
C++, Deep Learning, Javascript, Machine Learning, Neural Networks, Overlook, Python
- The Hard Problems AI Can’t (Yet) Touch - Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity,
advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
AI, Machine Learning, Optimization, Reinforcement Learning, Supervised Learning
- Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey - Jul 11, 2016.
This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.
Andrew Ng, Coursera, Deep Learning, edX, Machine Learning, MOOC, Nando de Freitas, Tom Mitchell, Udacity
- New Book: Effective CRM using Predictive Analytics – get 20% discount - Jul 11, 2016.
A comprehensive step-by-step guide to designing, setting up, executing and deploying data mining techniques in marketing. Use code VBM93 for 20% discount.
Book, CRM, Predictive Analytics, Wiley
- Big Data, Bible Codes, and Bonferroni - Jul 8, 2016.
This discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data, with real world examples emphasizing the importance of statistical processes in practice.
Bible, Big Data, Bonferroni, Probability, Statistics, Terrorism
- Streamlining Analytic Deployment: Inside the FICO Decision Management Suite 2.0 - Jul 8, 2016.
This post explains what’s new in the 2.0 version of the FICO Decision Management Suite, and how it can be used by data scientists and others to create stronger customer relationships and provide strategic competitive advantage.
Decision Management, Decision Support, Deployment, FICO
- Support Vector Machines: A Simple Explanation - Jul 7, 2016.
A no-nonsense, 30,000 foot overview of Support Vector Machines, concisely explained with some great diagrams.
Aylien, Explanation, Machine Learning, Support Vector Machines
- Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists - Jul 7, 2016.
Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving.
Ajay Ohri, API, Data Science Tools, Dataiku, Florian Douetteau, Python, R
- Deep Residual Networks for Image Classification with Python + NumPy - Jul 7, 2016.
This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.
Deep Learning, Neural Networks, numpy, Python
- Storytelling: The Power to Influence in Data Science - Jul 6, 2016.
Data scientists need to share results, which is different than talking shop with other data scientists. Read about influencing people and telling stories as a data scientist.
Communication, Data Science, Storytelling
- Success Criteria for Process Mining - Jul 6, 2016.
This article provides tips about the pitfalls and advice that will help you to make your first process mining project as successful as it can be.
Process Mining, Success
- Mining Twitter Data with Python Part 7: Geolocation and Interactive Maps - Jul 6, 2016.
The final part of this 7 part series explores using geolocation and interactive maps with Twitter data.
Data Visualization, Geo-Localization, Javascript, Python, Social Media, Social Media Analytics, Text Mining, Twitter
- 3 Key Ethics Principles for Big Data and Data Science - Jul 6, 2016.
If ethics in general are important, should ethics training be a crucial element of the data science field?
Big Data, Data Science, Ethics, Hui Xiong
- Mining Twitter Data with Python Part 6: Sentiment Analysis Basics - Jul 5, 2016.
Part 6 of this series builds on the previous installments by exploring the basics of sentiment analysis on Twitter data.
Python, Sentiment Analysis, Social Media, Social Media Analytics, Text Mining, Twitter
- Data Mining History: The Invention of Support Vector Machines - Jul 4, 2016.
The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.
History, Isabelle Guyon, Support Vector Machines, SVM, Vladimir Vapnik
- What is Softmax Regression and How is it Related to Logistic Regression? - Jul 1, 2016.
An informative exploration of softmax regression and its relationship with logistic regression, and situations in which each would be applicable.
Logistic Regression, Machine Learning, Regression
- Text Mining 101: Topic Modeling - Jul 1, 2016.
We introduce the concept of topic modelling and explain two methods: Latent Dirichlet Allocation and TextRank. The techniques are ingenious in how they work – try them yourself.
LDA, Text Mining, TextRank, Topic Modeling