AI News, Machine Learning FAQ
Machine Learning FAQ
Anyway, I think the field of Data Science is highly interdisciplinary and influenced by many many other fields … Okay, before I start, I’d say data science is mainly about extracting knowledge from data (and I think that the terms “data mining” or “Knowledge Discovery in Databases” are highly related).
Turing award winner Jim Gray imagined data science as a 'fourth paradigm' of science (empirical, theoretical, computational and now data-driven) and asserted that 'everything about science is changing because of the impact of information technology' and the data deluge. When Harvard Business Review called it 'The Sexiest Job of the 21st Century', the term 'data science' became a buzzword, and is now often applied to business analytics, business intelligence, predictive modeling, or any arbitrary use of data, or used as a glamorized term for statistics. In many cases, earlier approaches and solutions are now simply rebranded as 'data science' to be more attractive, which can cause the term to become 'dilute[d] beyond usefulness.' While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents. To its discredit, however, many data science and big data projects fail to deliver useful results, often as a result of poor management and utilization of resources. The term 'data science' has appeared in various contexts over the past thirty years but did not become an established term until recently.
In 2005, The National Science Board published 'Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century' defining data scientists as 'the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection' whose primary activity is to 'conduct creative inquiry and analysis.' Around 2007, Turing award winner Jim Gray envisioned 'data-driven science' as a 'fourth paradigm' of science that uses the computational analysis of large data as primary scientific method and 'to have a world in which all of the science literature is online, all of the science data is online, and they interoperate with each other.' In the 2012 Harvard Business Review article 'Data Scientist: The Sexiest Job of the 21st Century', DJ Patil claims to have coined this term in 2008 with Jeff Hammerbacher to define their jobs at LinkedIn and Facebook, respectively.
Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models. In an effort similar to Dhar's, Stanford professor David Donoho, in September 2015, takes the proposition further by rejecting three simplistic and misleading definitions of data science in lieu of criticisms. First, for Donoho, data science does not equate big data, in that the size of the data set is not a criterion to distinguish data science and statistics. Second, data science is not defined by the computing skills of sorting big data sets, in that these skills are already generally used for analyses across all disciplines. Third, data science is a heavily applied field where academic programs right now do not sufficiently prepare data scientists for the jobs, in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. As a statistician, Donoho, following many in his field, champions the broadening of learning scope in the form of data science, like John Chambers who urges statisticians to adopt an inclusive concept of learning from data, or like William Cleveland who urges to prioritize extracting from data applicable predictive tools over explanatory theories. Together, these statisticians envision an increasingly inclusive applied field that grows out of traditional statistics and beyond.
Data scientists vs data analysts: Why the distinction matters (updated 2018)
As a relatively new – but already highly sought after – position, it can be hard to know where Data Analytics ends and Data Science begins.
In this quick 10 minute presentation – given at last year’s Extract San Francisco – the CEO and Co-founder of Framed Data clearly outlines what makes a true Data Scientist and discusses how they differ from a traditional Analyst.
Data analysts are generally well versed in Sequel, they know some Regular Expressions, they can slice and dice data, they can use analytics or BI packages – like Tableau or Pentaho or an in house analytics solution – and they can tell a story from the data.
Data Scientist should have a wide breadth of abilities: academic curiosity, storytelling, product sense, engineering experience, business sense and just a catch-all I call cleverness.
Much like how scientists in the research lab will have a very amorphous charter of improving science, data scientists in a business, will have an amorphous charter of improving their company’s product somehow.
A data analyst may be able to interpret that data and explain it to those already in the data science field, but often it takes a data scientist to turn the numbers into a worthwhile storytelling opportunity.
This ability to distill a quantitative result from a machine learning model into something (be it words, pictures, charts, etc) that everyone can understand immediately is actually a very important skill for data scientists.
Product sense is the ability to use the story to create a new business product or change an existing product in a way that improves company goals and metrics.
In this way, data scientists can demonstrate just how valuable they are to the business side things, especially if some within the organization wonder, what does a data scientist do exactly?
For example, the “customers who bought this item also bought” section is an 800 by 20 pixel box which outlines the result of this machine learning model in a way that is visually appealing to customers.
Even if you’re not the product manager – or the engineer that creates these products – as a Data Scientist, whatever you create, in code or in algorithms, will need to translate into one of these products.
Statistical and machine learning knowledge is the domain expertise required to acquire data from different sources, create a model, optimize its accuracy, validate its purpose and confirm its significance.
Consequently, R is a great language for scaffolding models and visualization, but it’s not so great for writing production ready code – it breaks whenever you throw anything more than 10 megabytes in front of it.
But, it’s a great language to set up a proof of concept, and the ability to create something out of nothing and to prove that it works, is a skill that I think most data scientists ought to have.
So the ability to take on deadlines, constrained resources – even your company’s political climate – and push a product out in a reasonable amount of time is a really important skill.
The best big data scientists will internalize this important trait and always be ready to meet and overcome these important business demands and obstacles.
Big Data Analytics
Data mining technology helps you examine large amounts of data to discover patterns in the data – and this information can be used for further analysis to help answer complex business questions.
With data mining software, you can sift through all the chaotic and repetitive noise in data, pinpoint what's relevant, use that information to assess likely outcomes, and then accelerate the pace of making informed decisions.
It has become a key technology to doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast.
Text mining uses machine learning or natural language processing technology to comb through documents – emails, blogs, Twitter feeds, surveys, competitive intelligence and more – to help you analyze large amounts of information and discover new topics and term relationships.
The 10 Statistical Techniques Data Scientists Need to Master
Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it.
With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers — and the companies that hire them — Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.
As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them.
Now being exposed to the content twice, I want to share the 10 statistical techniques from the book that I believe any data scientists should learn to be more effective in handling big datasets.
I wrote one of the most popular Medium posts on machine learning before, so I am confident I have the expertise to justify these differences: In statistics, linear regression is a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable.
Now I need to answer the following questions: Classification is a data mining technique that assigns categories to a collection of data in order to aid in more accurate predictions and analysis.
Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.
Types of questions that a logistic regression can examine: In Discriminant Analysis, 2 or more groups or clusters or populations are known a priori and 1 or more new observations are classified into 1 of the known populations based on the measured characteristics.
Discriminant analysis models the distribution of the predictors X separately in each of the response classes, and then uses Bayes’ theorem to flip these around into estimates for the probability of the response category given the value of X.
In other words, the method of resampling does not involve the utilization of the generic distribution tables in order to compute approximate p probability values.
In order to understand the concept of resampling, you should understand the terms Bootstrapping and Cross-Validation: Usually for linear models, ordinary least squares is the major criteria to be considered to fit them into the data.
This approach fits a model involving all p predictors, however, the estimated coefficients are shrunken towards zero relative to the least squares estimates.
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.
So far, we only have discussed supervised learning techniques, in which the groups are known and the experience provided to the algorithm is the relationship between actual entities and the group they belong to.
Below is the list of most widely used unsupervised learning algorithms: This was a basic run-down of some basic statistical techniques that can help a data science program manager and or executive have a better understanding of what is running underneath the hood of their data science teams.
- On Friday, January 18, 2019
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