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Your time would probably be better spent on Kaggle than in a PhD program. When you read the stories by winners ([Kaggle blog][1]) you'll see that it takes a large amount of practice and the winners are not just experts of one single method. On the other hand, being active and having a plan in a PhD program can get y...
Perhaps a good way to paraphrase the question is, what are the advantages compared to alternative formats? The main alternatives are, I think: a database, text files, or another packed/binary format. The database options to consider are probably a columnar store or NoSQL, or for small self-contained datasets SQ...
I am glad you also found Yann LeCun's AMA page, it's very useful. Here are my opinions Q: Should I take some intro software engineering courses at my local University to make myself a stronger candidate? A: No, you need to take more math courses. It's not the applied stuff that's hard, it's the theory stuff. ...
**MapReduce**: A fault tolerant distributed computational framework. MapReduce allows you to operate over huge amounts of data- with a lot of work put in to prevent failure due to hardware. MapReduce is a poor choice for computing results on the fly because it is slow. (A typical MapReduce job takes on the order of min...
I've seen a few similar systems over the years. I remember a company called ClickTrax which if I'm not mistaken got bought by Google and some of their features are now part of Google Analytics. Their purpose was marketing, but the same concept can be applied to user experience analytics. The beauty of their system...
There is plenty of hype surrounding Hadoop and it's ego-system. However, in practice, where many data sets are in the tera-byte range, is it not more reasonable to use Amazon RedShift for querying large data sets, rather than spending time and effort building a Hadoop cluster? Also, how does Amazon Redshift compare wit...
Does Amazon RedShift replace Hadoop for ~1XTB data?
Personally, I don't think it's all that difficult to set up a hadoop cluster, but I know that it is sometimes painful when you are getting started. HDFS size limitations well exceed a TB (or did you mean exabyte?). If I'm not mistaken it scales to yottabytes or some other measurement that I don't even know the word...
News outlets tend to use "Big Data" pretty loosely. Vendors usually provide case studies surrounding their specific products. There aren't a lot out there for open source implementations, but they do get mentioned. For instance, Apache isn't going to spend a lot of time building a case study on hadoop, but vendors l...
News outlets tend to use "Big Data" pretty loosely. Vendors usually provide case studies surrounding their specific products. There aren't a lot out there for open source implementations, but they do get mentioned. For instance, Apache isn't going to spend a lot of time building a case study on hadoop, but vendors l...
I do not know a standard answer to this, but I thought about it some times ago and I have some ideas to share. When you have one confusion matrix, you have more or less a picture of how you classification model confuse (mis-classify) classes. When you repeat classification tests you will end up having multiple conf...
I'm working on improving an existing supervised classifier, for classifying {protein} sequences as belonging to a specific class (Neuropeptide hormone precursors), or not. There are about 1,150 known "positives", against a background of about 13 million protein sequences ("Unknown/poorly annotated background"), or a...
One-Class discriminatory classification with imbalanced, heterogenous Negative background?
What are the good books about the science and mathematics behind data science? It feels like so many "data science" books are programming tutorials and don't touch things like data generating processes and statistical inference. I can already code, what I am weak on is the math/stats/theory behind what I am doing. I...
Books about the "Science" in Data Science?
What are the good books about the science and mathematics behind data science? It feels like so many "data science" books are programming tutorials and don't touch things like data generating processes and statistical inference. I can already code, what I am weak on is the math/stats/theory behind what I am doing. I...
If I could only recomend one to you, it would be: [The Elements of Statistical Learning and Prediction](http://www.amazon.com/The-Elements-Statistical-Learning-Prediction/dp/0387848576) by Hastie, Tibshirani and Friedman. It provides the math/statistics behind a lot of commonly used techniques in data science. For ...
What are the books about the science and mathematics behind data science? It feels like so many "data science" books are programming tutorials and don't touch things like data generating processes and statistical inference. I can already code, what I am weak on is the math/stats/theory behind what I am doing. If I a...
There is plenty of hype surrounding Hadoop and its eco-system. However, in practice, where many data sets are in the terabyte range, is it not more reasonable to use [Amazon RedShift][1] for querying large data sets, rather than spending time and effort building a Hadoop cluster? Also, how does Amazon Redshift com...
I've built an artificial neural network in python using the scipy.optimize.minimize (Conjugate gradient) optimization function. I've implemented gradient checking, double checked everything etc and I'm pretty certain it's working correctly. I've run it a few times and it reaches 'Optimization terminated successfu...
I've built an artificial neural network in python using the scipy.optimize.minimize (Conjugate gradient) optimization function. I've implemented gradient checking, double checked everything etc and I'm pretty certain it's working correctly. I've run it a few times and it reaches 'Optimization terminated successfu...
The setup is simple: binary classification using a simple decision tree, each node of the tree has a single threshold applied on a single feature. In general, building a ROC curve requires moving a decision threshold over different values and computing the effect of that change on the true positive rate and the false p...
How can we calculate AUC for a simple decision tree?
How can I extract news about a particular company from various websites using RODBC package in R? And perform sentiment analysis on the data?
I maybe asking a very basic Q. I am, just starting to develop ML application for academic purposes, but i am also interested in knowing what people are using both academia and Industries. A lot of places i saw people using python. what do you recommand? i am currently using R and train myself in it. Thanks , Ido
In order to build the ROC curve and AUC (Area under curve) you have to have a binary classifier which provides you at classification time, the distribution (or at least a score), not the classification label. To give you an example, suppose you have a binary classification model, with classes c1 and c2. For a given ins...
I've now seen two data science certification programs - the [John Hopkins one available at Coursera][1] and the [Cloudera one][2]. I'm sure there are others out there. The John Hopkins set of classes is focused on R as a toolset, but covers a range of topics: * R Programming * cleaning and obtaining data * D...
What do you think of Data Science certifications?
Increasing the number of hidden layers for a standard neural network actually won't improve results in a majority of cases. Changing the _size_ of the hidden layer will. This fact has actually was noted historically and is the motivation behind the field of deep learning. Deep learning is effectively clever ways of ...
Data visualization is an important sub-field in data science and python programmers would need to have available toolkits for them. **Is there a Python API to Tableau?** **Are there any Python-based data visualization toolkits?**
Increasing the number of hidden layers for a standard neural network actually won't improve results in a majority of cases. Changing the _size_ of the hidden layer will. This fact (that the number of hidden layers does very little) has actually was noted historically and is the motivation behind the field of deep le...
I maybe asking a very basic question. I'm just starting to develop a ML application for academic purposes, but I'm also interested in knowing what people are using both in academia and industry. A lot of places I saw people using Python. What do you recommend? I'm currently using R and training myself in it.
Could you give some examples of typical tasks that a data scientist does in his daily job and the must know minimum for each of the levels (like junior, senior, etc. if there are any)? (if possible something like the following Programmer competency matrix http://www.starling-software.com/employment/programmer-compete...
Could you give some examples of typical tasks that a data scientist does in his daily job, and the must-know minimum for each of the levels (like junior, senior, etc. if there are any)? If possible, something like a [Programmer competency matrix](http://www.starling-software.com/employment/programmer-competency-matrix....
In some cases, [it may be impossible][1] to draw Euler diagrams with overlapping circles to represent all the overlapping subsets in the correct proportions. This type of data then requires using polygons or other figures to represent each set. When dealing with data that describes overlapping subsets, how can I figure...
How do I figure out if subsets can be plotted in a normal Euler diagram?
as I am very interested in programming and statistics, Data Science seems like a great career path to me - I like both fields and would like to combine them. Unfortunately, I have studied political science with a non-statistical sounding Master. I focused on statistics in this Master, visiting optional courses and writ...
Data Science as a Social Scientist?
I attack this problem frequently with inefficiency because it's always pretty low on the priority list and my clients are resistant to change until things break. I would like some input on how to speed things up. I have multiple datasets of information in a SQL database. The database is vendor-designed, so I hav...
How to best accomplish high speed comparison of like data?
I've now seen two data science certification programs - the [John Hopkins one available at Coursera][1] and the [Cloudera one][2]. I'm sure there are others out there. The John Hopkins set of classes is focused on R as a toolset, but covers a range of topics: * R Programming * cleaning and obtaining data * D...
Sounds interesting. Could the solution be to dump the data out, build a fast custom processing thingie to run it through and then import it back to the database? I've seen some blazing fast Java-based text processing tools for topic modeling that handle millions of lines of text per second. If it's an option then yo...
Logic often states that by overfitting a model, it's capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the complexity of data. How do you know when your model has struck the right balance and is not underfitting the data it seeks to ...
When is a Model Underfitted?
Logic often states that by underfitting a model, it's capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the complexity of data. How do you know when your model has struck the right balance and is not underfitting the data it seeks to...
Logic often states that by overfitting a model, it's capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does overfitting cause models to become worse regardless of the complexity of data, and if so, why? ---------- **Related:*...
Can't you create a hash for each classes, and then merge rows by rows, field by field only the classes where the hash changed ? It should be faster if most of the classes don't change.. Or a hash of each rows or maybe columns.. depending on how the data normally change..
@OP: Choosing answers by votes is the WORST idea. Your question becomes a popularity contest. You should seek the right answer, I doubt you know what you are asking, know what you are looking for. To the your question: Q: how seriously DS certifications are viewed at this point by the community. A: ...
To answer your question it is important to understand the frame of reference you are looking for, if you are looking for what philosophically you are trying to achieve in model fitting, check out Rubens answer he does a good job of explaining that context. However, in practice your question is almost entirely define...
@OP: Choosing answers by votes is the WORST idea. Your question becomes a popularity contest. You should seek the right answer, I doubt you know what you are asking, know what you are looking for. To answer your question: Q: how seriously DS certifications are viewed at this point by the community. ...
What kind of error measures do each give and how do I know when to use one over the other? If you could give an example of when to use each, that would be great as well! Thanks so much!
Difference between using RMSE and nDCG to evaluate Recommender Systems?
I'd like to use my MSc thesis as an opportunity to explore 'data science'. Frankly the term seems a little vague to me (or at least, I've heard so many people apply it to so many situations that its become diluted), but I expect it requires a) machine learning (rather than traditional statistics) b) a large enough data...
I'm curious about natural language querying. Stanford has what looks to be a strong set of [software for processing natural language][1]. I've also seen the [Apache OpenNLP library][2], and the [General Architecture for Text Engineering][3]. There are an incredible amount of uses for natural language processing an...
How to process natural language queries?
I'm curious about natural language querying. Stanford has what looks to be a strong set of [software for processing natural language][1]. I've also seen the [Apache OpenNLP library][2], and the [General Architecture for Text Engineering][3]. There are an incredible amount of uses for natural language processing an...
Natural language querying poses very many intricacies which can be very difficult to generalize. From a high level, I would start with trying to think of things in terms of nouns and verbs. So for the sentence: How many books were sold last month? You would start by breaking the sentence down with a parser which...
The majority of people use S3. However, Google Drive seems a promising alternative solution for storing large amounts of data. Are there specific reasons why one is better than the other?
I'm developing a distributed algorithm, and to improve efficiency, it relies both on the number of disks (one per machine), and on an efficient load balance strategy. With more disks, we're able to reduce the time spent with I/O; and with an efficient load balance policy, we can distribute tasks without much data repli...
I'm developing a distributed algorithm, and to improve efficiency, it relies both on the number of disks (one per machine), and on an efficient load balance strategy. With more disks, we're able to reduce the time spent with I/O; and with an efficient load balance policy, we can distribute tasks without much data repli...
Financial Services is a big user of Big Data, and innovator too. One example is mortgage bond trading. To answer your questions for it: > What kinda data these companies used. What was the size of the data? - Long histories of each mortgage issued for the past many years, and payments by month against them. (B...
CAPM (Capital Asset Pricing Model) in Finance is a classic example of an underfit model. It was built on the beautiful theory that "Investors only pay for risk they can't diversify away" so expected excess returns are equal to correlation to market returns. As a formula [0] Ra = Rf + B (Rm - Rf) where Ra is the ex...
I've came across the following problem, that I recon is rather typical. I have some large data, say, a few million rows. I run some non-trivial analysis on it, e.g. an SQL query consisting of several sub-queries. I get some result, stating, for example, that property X is increasing over time. Now, there are two ...
How to debug data analysis?
I have a binary classification problem: - Approximately 1000 samples - 10 attributes, including binary, numeric and categorical Which algorithm is the best choice for this type of problem? By default I'm going to start with SVM, as it is considered the best for relatively clean and not noisy data.
I have a binary classification problem: - Approximately 1000 samples in training set - 10 attributes, including binary, numeric and categorical Which algorithm is the best choice for this type of problem? By default I'm going to start with SVM, as it is considered the best for relatively clean and not nois...
I once heard that filtering spam by using blacklists is not a good approach, since some user searching for entries in your dataset may be looking for particular information from the sources blocked. Also it'd become a burden to continuously validate the *current state* of each spammer blocked, checking if the site/doma...
I once heard that filtering spam by using blacklists is not a good approach, since some user searching for entries in your dataset may be looking for particular information from the sources blocked. Also it'd become a burden to continuously validate the *current state* of each spammer blocked, checking if the site/doma...
I have a binary classification problem: - Approximately 1000 samples in training set - 10 attributes, including binary, numeric and categorical Which algorithm is the best choice for this type of problem? By default I'm going to start with SVM (preliminary having nominal attributes values converted to binar...
@OP: Choosing answers by votes is the WORST idea. Your question becomes a popularity contest. You should seek the right answer, I doubt you know what you are asking, know what you are looking for. To answer your question: Q: how seriously DS certifications are viewed at this point by the community. ...
I'm currently in the very early stages of preparing a new research-project (still at the funding-application stage), and expect that data-analysis and especially visualisation tools will play a role in this project. In view of this I face the following dilemma: Should I learn Python to be able to use its extensive s...
According to me, all the factors, you have mentioned are superficial in nature. You have not considered the core of tool selection. In this case, there are 2 aspects, you mentioned: 1. Data analysis - What kind of analysis are you working on? There might be some analysis which are easier in some languages and more d...
From my experience, the points to keep in mind when considering a data analysis platform are: 1. Can it handle the size of the data that I need? If your data sets fit in memory, there's usually no big trouble, although AFAIK Python is somewhat more memory-efficient than R. If you need to handle larger-than-memory ...
If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic? I have looked into machine learning algorithms, but I'm not sure which one to use. In my test data, a year over year trend is more accurate compared to ot...
How can I predict traffic based on previous time series data?
I think Christopher's answers above are entirely sensible. As an alternate approach (or perhaps just in addition to the advise he's given), I might start by just visualizing the data a bit to try get a rough sense of what's going on. If you haven't already done this, you might try adding a date's month and day of w...
I'm currently working on implementing sgd for neural nets using backpropagation, and while I understand its purpose I have some questions about how to choose values for the learning rate. Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent? If so, how do you use this info...
I'm currently working on implementing Stochastic Gradient Descent (SGD) for neural nets using backpropagation, and while I understand its purpose I have some questions about how to choose values for the learning rate. - Is the learning rate related to the shape of the error gradient, as it dictates the rate of desce...
It seems as though most languages have some number of scientific computing libraries available. - Python has `Scipy` - `Rust` has `SciRust` - `C++` has several including `ViennaCL` and `Armadillo` - `Java` has `Java Numerics` and `Colt` as well as several other Not to mention languages like `R` and `Julia...
It seems as though most languages have some number of scientific computing libraries available. - Python has `Scipy` - `Rust` has `SciRust` - `C++` has several including `ViennaCL` and `Armadillo` - `Java` has `Java Numerics` and `Colt` as well as several other Not to mention languages like `R` and `Julia...
What kind of error measures do RMSE and nDCG give while evaluating a recommender system, and how do I know when to use one over the other? If you could give an example of when to use each, that would be great as well!
I work with datasets that contain personally identifiable information (PII) and sometimes need to share part of a dataset with third parties, in a way that doesn't expose PII and subject my employer to liability. For example, one task involves identifying individuals by their names, in user-submitted data, while tak...
How can I transform names in a confidential data set to make it anonymous, but preserve some of the characteristics of the names?
- **Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent?** - In plain SGD, no. A global learning rate is used which is indifferent to the error gradient. However the intuition you are getting at has inspired various modifications of the SGD update rule. - **If...
I work with datasets that contain personally identifiable information (PII) and sometimes need to share part of a dataset with third parties, in a way that doesn't expose PII and subject my employer to liability. For example, one task involves identifying individuals by their names, in user-submitted data, while tak...
- **Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent?** - In plain SGD, no. A global learning rate is used which is indifferent to the error gradient. However the intuition you are getting at has inspired various modifications of the SGD update rule. - **If...
- **Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent?** - In plain SGD, no. A global learning rate is used which is indifferent to the error gradient. However the intuition you are getting at has inspired various modifications of the SGD update rule. - **If...
- **Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent?** - In plain SGD, no. A global learning rate is used which is indifferent to the error gradient. However the intuition you are getting at has inspired various modifications of the SGD update rule. - **If...
Does anyone know some good tutorials on online machine learning technics? I.e. how it can be used in real-time environments, what are key differences compared to normal machine learning methods etc.
I'm planning to run some experiments with very large datasets, and I'd like to distribute the computation. In fact, the main result of this experiment is to evaluate efficiency gains in comparison to previous proposals. I have about ten machines available, each with 200GB of free space on hard disk. However, I would...
I have recently seen this really cool feature (that was once) available in google sheets: you start by writing a few related keywords in consecutive cells, say: "blue", "green", "yellow", and it automatically generates the next keywords on the same theme (in this case, other colors). See more examples in this video:...
How it works: growing a list of related objects based on initial keywords?
Are anyone using `Julia` ([http://julialang.org/][1]) for professional jobs? Or use it instead of R , Matlab or Mathematica? Is it a good language? If you have to predict next 5-10 years: Do you think it grow up enough to became such a standard in datascience like R or similar? [1]: http://julialang.or...
does anyone use Julia programming language?
How to grow a list of related words based on initial keywords?
I'm trying to understand how all the "big data" components play together in a real world use case, e.g. hadoop, monogodb/nosql, storm, kafka, ... I know that this is quite a wide range of tools used for different types, but I'd like to get to know more about their interaction in applications, e.g. thinking machine lear...
I'm trying to understand how all the "big data" components play together in a real world use case, e.g. hadoop, monogodb/nosql, storm, kafka, ... I know that this is quite a wide range of tools used for different types, but I'd like to get to know more about their interaction in applications, e.g. thinking machine lear...
I have an huge dataset from a relational database which I need to create a classification model for. Normally for this situation I would use ILP but due to special circumstances I can't do that. The other way to tackle this would be just to try to aggregate the values when I have a foreign relations however I have t...
I have a huge dataset from a relational database which I need to create a classification model for. Normally for this situation I would use [Inductive Logic Programming](http://en.wikipedia.org/wiki/Inductive_logic_programming) (ILP), but due to special circumstances I can't do that. The other way to tackle this wou...
If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic? I have looked into machine learning algorithms, but I'm not sure which one to use. In my test data, a year over year trend is more accurate compared to oth...