ronaldweinland.info Manuals A SIMPLE INTRODUCTION TO DATA SCIENCE PDF

A SIMPLE INTRODUCTION TO DATA SCIENCE PDF

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The demand for skilled data science practitioners in industry, combining a simple, elegant writing and publishing workflow with a store. Introduction to Data Science, by Jeffrey Stanton, provides non-technical readers with a gentle introduction to essential concepts and activities of data science. Real-Time Big Data Analytics: Emerging Architecture .. Hadoop Tutorial as a PDF This is a simple introduction to time series analysis using the R statistics.


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Introduction to. Data Science and Machine Learning. Tias Guns. VUB Brussels, Belgium. CP/SAT/ICLP Page 2. Credit. Data Science in Practice. Multidisciplinary study of data collections for analysis, prediction, learning and prevention. ○ Utilized in As soon as the data scientist identified the problem she is trying to solve, she must assess: ○ . Machine learning - basics. Machine learning Ref: ronaldweinland.info Introduction to Data Science was originally developed by Prof. Tim Kraska. “ The intuition behind this ought to be very simple: Mr. .. ronaldweinland.info].

There are a lot of similarities but there are a lot of difference as well. So the above table gives you a high-level understanding of what are the major difference between a data scientist and a data analyst. One more key difference between the two domains is that data analysis is a necessary skill for data science. Thus data science can be thought of a big set while data analysis can be thought of as a subset of it. In this data science tutorial you will learn top tools, technologies, skills needed to be a successful data scientist. So this is your preliminary step to learn data science and become an accomplished data scientist.

It is a Bash built in. It is one of the primary concept in, or building blocks of, computer science: the basis of the design of elegant and efficient code, data processing and preparation, and software engineering. There are many ways to get a dataset like configuring an API, internet, database, etc.

To convert binary data into a useful data, we need to perform certain tasks which includes-Decompress files, Querying relational database, etc.

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It is very much important to track the origin of database and check whether that data is up to date Read More Scrubbing Data Techniques for Scrubbing or Cleaning Data in Data Science As we know the obtained data has inconsistencies, errors, weird characters, missing values or different problems. In this situation, you have to scrub, or clean the data before to use this data. So for scrubbing the data some techniques are used which are as follows:- Filter lines Extract certain columns or Read More Data visualization Data Visualization in R programming Here we will be using R programming language to visualize data.

It is very important to visualize the result in a graphical format, to analyze the obtained output. Apart from that, we will be deriving statistics to get all the unique values, identifiers, factors, and continuous variable. We can check the overall result through summary Read More Modeling the data Data Modelling Concepts in Data Science To predict something useful from the datasets, we need to implement machine learning algorithms.

You can also apply more complicated statistical approaches. Data normalization can help you avoid getting stuck in a local optima during the training process in the context of neural networks. Another useful technique in data preparation is the conversion of categorical data into numerical values.

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As a string, this isn't useful as an input to a neural network, but you can transform it by using a one-of-K scheme also known as one-hot encoding. In this scheme illustrated in Figure 3 , you identify the number of symbols for the feature — in this case, six — and then create six features to represent the original field. For each symbol, you set just one feature, which allows a proper representation of the distinct elements of the symbol.

You pay the price in increased dimensionality, but in doing so, you provide a feature vector that works better for machine learning algorithms.

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Figure 3. Transforming a string into a one-hot vector An alternative is integer encoding where T0 could be value 0, T1 value 1, and so on , but this approach can introduce problems in representation. For example, in a real-valued output, what does 0. Machine learning In this phase, you create and validate a machine learning model. Sometimes, the machine learning model is the product, which is deployed in the context of an application to provide some capability such as classification or prediction.

In other cases, the machine learning algorithm is just a means to an end. In these cases, the product isn't the trained machine learning algorithm but rather the data that it produces. This section discusses the construction and validation of a machine learning model.

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You can learn more about machine learning from data in Gaining invaluable insight from clean data sets. Model learning The meat of the data science pipeline is the data processing step.

In one model, the algorithm can process the data, with a new data product as the result. But, in a production sense, the machine learning model is the product itself, deployed to provide insight or add value such as the deployment of a neural network to provide prediction capabilities for an insurance market.

Machine learning approaches are vast and varied, as shown in Figure 4. This small list of machine learning algorithms segregated by learning model illustrates the richness of the capabilities that are provided through machine learning. Figure 4.

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Machine learning approaches View image at full size Supervised learning, as the name suggests, is driven by a critic that provides the means to alter the model based on its result. Given a data set with a class that is, a dependent variable , the algorithm is trained to produce the correct class and alter the model when it fails to do so. The model is trained until it reaches some level of accuracy, at which point you could deploy it to provide prediction for unseen data. In contrast, unsupervised learning has no class; instead, it inspects the data and groups it based on some structure that is hidden within the data.

You could apply these types of algorithms in recommendation systems by grouping customers based on the viewing or downloading history. Finally, reinforcement learning is a semi-supervised learning algorithm that provides a reward after the model makes some number of decisions that lead to a satisfactory result.

Model validation After a model is trained, how will it behave in production? One way to understand its behavior is through model validation. A common approach to model validation is to reserve a small amount of the available training data to be tested against the final model called test data.

You use the training data to train the machine learning model, and the test data is used when the model is complete to validate how well it generalizes to unseen data see Figure 5.

Figure 5. Training versus test data for model validation View image at full size The construction of a test data set from a training data set can be complicated. A random sampling can work, but it can also be problematic.

For example, did the random sample over-sample for a given class, or does it provide good coverage over all potential classes of the data or its features?

Random sampling with a distribution over the data classes can be helpful for avoiding overfitting that is, training too closely to the training data or underfitting that is, doesn't model the training data and lacks the ability to generalize. Operations Operations refers to the end goal of the data science pipeline.

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This goal can be as simple as creating a visualization for your data product to tell a story to some audience or answer some question created before the data set was used to train a model. Or, it could be as complex as deploying the machine learning model in a production environment to operate on unseen data to provide prediction or classification.

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This section explores both scenarios. Model deployment When the product of the machine learning phase is a model that you'll use against future data, you're deploying the model into some production environment to apply to new data. This model could be a prediction system that takes as input historical financial data such as monthly sales and revenue and provides a classification of whether a company is a reasonable acquisition target.

In scenarios like these, the deployed model is typically no longer learning and simply applied with data to make a prediction. There are good reasons to avoid learning in production.

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In the context of deep learning neural networks with deep layers , adversarial attacks have been identified that can alter the results of a network. In an image processing deep learning network, for example, applying an image with a perturbation can alter prediction capabilities of the image such that instead of "seeing" a tank, the deep learning network sees a car.

Adversarial attacks have grown with the application of deep learning, and new vectors of attack are part of active research. Model visualization In smaller-scale data science, the product sought is data and not necessarily the model produced in the machine learning phase. This scenario is the most common form of operations in the data science pipeline, where the model provides the means to produce a data product that answers some question about the original data set.