![\"\"](\"http:\/\/api.ning.com:80\/files\/-q6tvS*EPsLDs8MwQuRIsxLEFTbYfjyLDIqHpbih0OnE5dry2VTTzwfUcIrZ5zvXz9NIUHqPPpih2O4T*VUe6yCnt-yd*4Gr\/Capturevcx.PNG\")
<\/a><\/p>\n1. Different Types of Data Scientists<\/strong><\/span><\/p>\n To get started and gain some historical perspective, you can read my article about 9 types of data scientists<\/a>, published in 2014, or my article \u00a0where I compare data science with 16 analytic disciplines<\/a>, also published in 2014.<\/p>\n The following articles, published during the same time period, are still useful:<\/p>\n More recently (August 2016)\u00a0\u00a0<\/b>Ajit Jaokar<\/a>\u00a0discussed Type A (Analytics) versus Type B (Builder) data scientist:<\/p>\n I also wrote about the ABCD’s of business processes optimization<\/a>\u00a0where D stands for data science, C for computer science, B for business science, and A for analytics science. Data science may or may not involve coding or mathematical practice, as you can read in my article on\u00a0low-level versus high-level data science<\/a>. In a startup, data scientists generally wear several hats, such as executive, data miner, data engineer or architect, researcher, statistician, modeler (as in predictive modeling) or developer.<\/p>\n While the data scientist is generally portrayed as a coder experienced in R, Python, SQL, Hadoop and statistics, this is just the tip of the iceberg, made popular by data camps focusing on teaching some elements of data science. But just like a lab technician can call herself a physicist, the real physicist is much more than that, and her domains of expertise are varied: astronomy, mathematical physics, nuclear physics (which is borderline chemistry), mechanics, electrical engineering, signal processing (also a sub-field of data science) and many more. The same can be said about data scientists: fields are as varied as bioinformatics, information technology, simulations and quality control, computational finance, epidemiology, industrial engineering, and even number theory<\/a>.<\/p>\n In my case, over the last 10 years, I specialized in machine-to-machine and device-to-device communications, developing systems to automatically process large data sets, to perform automated transactions: for instance, purchasing Internet traffic or automatically generating content. It implies developing algorithms that work with unstructured data, and it is at the intersection of AI (artificial intelligence,) IoT (Internet of things,) and data science. This is referred \u00a0to as deep data science<\/a>. It is relatively math-free, and it involves relatively little coding (mostly API’s), but it is quite data-intensive (including building data systems) and based on brand new statistical technology designed specifically for this context.<\/p>\n Prior to that, I worked on credit card fraud detection in real time. Earlier in my career (circa 1990) I worked on image remote sensing technology, among other things to identify patterns (or shapes or features, for instance lakes)\u00a0in satellite images and to perform image segmentation: at that time my research was labeled as computational statistics, but the people doing the exact same thing in the computer science department next door\u00a0in my home university, called their research artificial intelligence. Today, it would be called data science or artificial intelligence, the sub-domains being signal processing, computer vision or IoT.<\/p>\n Also, data scientists can be found anywhere in the lifecycle of data science projects<\/a>, at the data gathering stage, or the data exploratory stage, all the way up to statistical modeling and maintaining existing systems.<\/p>\n 2. Machine Learning versus Deep Learning<\/strong><\/span><\/p>\n Before digging deeper into the link between data science and machine learning, let’s briefly discuss machine learning and deep learning. Machine learning is a set of algorithms that train on a data set to make predictions or take actions in order to optimize some systems. For instance, supervised classification algorithms are used to classify potential clients into good or bad prospects, for loan purposes, based on historical data. The techniques involved, for a given task (e.g. supervised clustering), are varied: naive Bayes, SVM, neural nets, ensembles, association rules, decision trees, logistic regression, or a combination of many. For a detailed list of algorithms, click here<\/a>. For a list of machine learning problems, click here<\/a>.<\/p>\n All of this is a subset of data science. When these algorithms are automated, as in automated piloting or driver-less cars, it is called AI, and more specifically, deep learning. Click here<\/a>\u00a0for another article comparing machine learning with deep learning.\u00a0If the data collected comes from sensors and if it is transmitted via the Internet, then it is machine learning or data science or deep learning applied to IoT.<\/p>\n Some people have a different definition for deep learning. They consider deep learning as neural networks (a machine learning technique) with a deeper layer. The question was asked on Quora recently, and below is a more detailed explanation (source: Quora<\/a>)<\/p>\n What is the difference between machine learning and statistics?<\/strong><\/p>\n This article<\/a>\u00a0tries to answer the question. The author writes that statistics is machine learning with confidence intervals for the quantities being predicted or estimated. I tend to disagree, as I have built engineer-friendly confidence intervals<\/a>\u00a0that don’t require any mathematical or statistical knowledge.<\/p>\n 3. Data Science versus Machine Learning<\/strong><\/span><\/p>\n Machine learning and statistics are part of data science. The word learning<\/em> in machine learning means that the algorithms depend on some data, used as a training set, to fine-tune some model or algorithm parameters. This encompasses many techniques such as regression, naive Bayes or supervised clustering. But not all techniques fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-priori knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit in this category.<\/p>\n Data science is much more than machine learning though. Data, in data science, may or may not come from a machine<\/em>\u00a0or mechanical process (survey data could be manually collected, clinical trials involve a specific type of small data) \u00a0and it might have nothing to do with learning<\/em> as I have just discussed. But the main difference is the fact that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. In particular, data science also covers<\/p>\n Of course, in many organisations, data scientists focus on only one part of this process. To read about some of my original contributions to data science, click here<\/a>.<\/p>\n\n
\n
\n
\n
\n
\n
\n
\n