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HomeBlogData ScienceDefinitive Guide on Data Science Modeling
In recent years, the field of data science has received increased attention and has employed great research efforts in developing advanced analytics, improving data science models and cultivating new algorithms. Data Centric enthusiasm is growing strong across a variety of domains. The data science research community is growing day by day and is always nourished by the neighboring fields of mathematics, statistics and computer science.
Data doesn’t exist in a vacuum. Key to understanding the value of the data is having the understanding about the relational nature of the data. For example, without being able to conjure data that connects price points to certain products, how will a marketing team conduct pricing analysis?
The process of assigning relational rules to data, such as those mentioned above, is called data modeling. For instance, a data model may specify that the data element representing a car be composed of several other elements which, in turn, represent the color and size of the car and define its owner. Sharpen your data science skills with these Data Science courses - data science courses in India and learn to tackle complex Data Science problems.
Data modeling comes with a goal to produce greater quality, structured and consistent data for running business applications, and achieving consistent outcomes. Data modeling in data science can be termed as a mechanism designed for defining and ordering data for the use and analysis by certain business processes. One of the objectives of modeling in data science is to create the most efficient method of storing information while still providing for complete access and reporting.
The modeling in data science can include symbols, text, or diagrams to represent the data and the way that it interrelates. The process of data modeling subsequently increases consistency in naming, semantics, rules, and security, while also improving data analytics, mainly because of the structure that data modeling enforces upon data.
The ability to think clearly and systematically about the key data points to be stored and retrieved, and how they should be grouped and related, is what the data modeling component of data science is all about.
A data model helps organizations capture all the points of information necessary to perform operations and enact policy based on the data they collect. This can be explained with an example of a sales transaction which is broken down into related groups of data points, describing the customer, the seller, the item sold, and the payment mechanism. For instance, if the sales transactions were recorded without the date on which they occurred, it would be impossible to enforce certain return policies. Data modeling in data science is also performed to help organizations ensure that they are collecting all the necessary items of information in the first place. To learn more about modeling in data science, attend this training - Complete Data Science Bootcamp.
Organizing the data elements and standardizing how they relate to one another is the main objective of the data model. The data model represents reality since data elements tend to document real-life things, places, and people and also the events between them. It can include all types of data but is not limited to logical data, conceptual data, and physical data.nKnow more about how to become a dependable data scientist.
There are three stages or types of data model:
This is performed as the first step in the data modeling process, and it enforces a theoretical order on data as per its existence in relation to the entities being described, often real-world concepts or artefacts. These data models are meant to cater to business professionals, especially key business stakeholders can make the most out of this.
A logical data model is often the next step after conceptual data modeling. Inherently the logical modeling process attempts to enforce order by establishing, key values, discrete entities, and relationships in a logical structure, by taking the semantic structure built at the conceptual stage.
This is the data modelling step that breaks the data into the actual tables, indexes, and clusters required for the storage of the data. This step involves dwelling into more detail with the primary keys, foreign keys, column keys and restraints. This model exercises inclusion of exact types and attributes in the column. A physical model represents the internal schema very well.
As you fall into the hype vortex of Machine Learning and Artificial Intelligence, it seems that only advanced techniques will solve all your problems when you want to build a predictive model. But, as you get your hands dirty in the code, you find out that the truth is very, very different. A lot of the problems you will face as a data scientist are solved with a combination of several models and most of them have been around for ages. To learn these techniques through projects, coding and to gain practical insights, attend this training - KnowledgeHut complete data science Bootcamp.
There are various data science modeling techniques and methods that one can employ to perform the analysis.
The primary question asked by data scientists in classification problems is, "What category does this data belong to?". There can be many reasons for classifying data into categories. Perhaps the data is a scanned image of text document and you want to know what set of letters or numbers the image represents. Or perhaps the data represents cancer detection scheme and you want to know if it should be in the "positive" or "negative" category. Other classifications could be focused on determining health of the crop or whether a tweet is a factual or a rumor.
The algorithms and methods that one should use to filter the data into categories are as follows:
The first non-linear algorithm to study should be the Decision Tree. A fairly simple and explainable algorithm based on if-else rules. Decision Trees are the building blocks of all tree-based models.
Other algorithms based on Decision Trees that bring them stability are XGBoost or LightGBM. These models are boosting algorithms, they work on errors made by previous weak learners to find patterns that are more robust and generalize better.
Advantages include being simple to understand and visualize, require little data preparation, and can handle both numerical and categorical data. Drawbacks include as they can create complex trees that do not generalize well, and they can be unstable because small variations in the data might result in a completely different tree being generated.
Supported vector machines find a hyperplane in an N-dimensional space that classifies data points. SVMs aim to draw a line or plane with a wide margin to separate data into different categories.
Benefits of using SVMs include being effective in high dimensional spaces and using a subset of training points in the decision function so they are also memory efficient. Downside is that the algorithm does not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.
Naive Bayes classifiers are simple probabilistic classifiers based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".
Advantages of using these classifiers are algorithms requiring a small amount of training data to estimate the necessary parameters and are extremely fast compared to more sophisticated methods. Major drawback is that they are known to be bad estimators.
Although named regression, Logistic Regression is the best model to start your mastery on Classification Problems. Logistic regression is a popular supervised learning algorithm used to assess the probability of a variable having a binary label based on some predictive features.
Benefits are that unlike discriminant function analysis, it does not require predictor variables to be normally distributed, linearly related or to have equal variance. But downside is that it assumes data is free of missing values, it assumes all predictors are independent of each other, it mostly works when the predicted variable is binary.
This is one of the simplest and most effective classical machine learning algorithms. It classifies an unknown test state by finding the k-nearest neighbors from a set of M train states.
Advantages of this algorithm are easy implantation, robustness to presence of the noise in the training data, and it is mostly effective when training data is large. But it comes with a very high computation cost as one needs to compute the distance of each instance to all the training samples.
Random forests are among the one of the most widely used ML classifiers. They are ensemble learning method for classification task. For classification tasks, the output of the random forest is the class selected by most trees. The concept of Random Forest is really simple, if Decision trees are a dictatorship, Random Forests are a democracy. They help to diversify across different decision trees and this helps to bring robustness to your algorithm just like decision trees, you can configure a ton of hyper-parameters to enhance the performance of this Bagging model.
One of the main advantages is that random forest classifiers are more accurate than decision trees in most cases and also they offer excellent performance with nearly zero parameter tuning. But disadvantages include slow real time prediction, difficult to implement, and they are complex algorithms.
ANNs are currently one of the best models to find non-linear patterns in data and to build really complex relationships between independent and dependent variables. By learning them you will be exposed to the concepts of activation function, back-propagation and neural network layers.
Advantages with these is that they have shown profound capabilities for classification with extremely large sets of training data. Downside is that interpretability and explainability of the neural network is a daunting task and is still a completely unsolved problem and is an active research area.
Let us say instead of trying to find out which category the data belongs to, one would like to know the relationship between different data points? The main objective of regression is to answer the question, "What is the predicted value for the given data?" This is a simple concept that arises from the statistical idea of "regression to the mean," it can either be a straightforward regression between one independent and other dependent variable or a multidimensional one that tries to find the relationship between multiple variables.
Some classification techniques that are already discussed above, such as SVMs, ANNs and decision trees, can also be used to perform regression operation. In addition, the regression techniques that are available to data scientists are as follows:
Linear Regression is a machine learning algorithm based on supervised learning, and is used for predictive analysis. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. The simplest form of the regression equation with one dependent and one independent variable can be represented by the formula y = c + b*x, where y is an estimated dependent variable score, c is a constant, b is the regression coefficient, and x is the score on the independent variable.
Lasso regression is like linear regression, but it employs a technique called “shrinkage", where the coefficients of determination shrunk towards zero. As we know linear regression gives us regression coefficients as observed in the dataset, where-in the lasso regression allows us to shrink or regularize these coefficients to avoid the overfitting and make them work better on different datasets.
This is quite similar to the simple linear regression we have discussed above, but with multiple independent variables contributing to the dependent variable and hence multiple coefficients to determine and complex computation due to the added variables.
Key steps in building data science models are as follows:
To start with, you need to have an idea about the problem at hand. This may be the most important and uncertain step. What are the goals of the model? What’s in the scope and outside the scope of the model? Asking the right question will determine what data to collect later. This also determines if the cost to collect the data can be justified by the impact of the model. Also, what are the risk factors known at the beginning of the process?
Not any data, but the collected chunks of unstructured data should be relevant to the business problem you are about to solve. You would be surprised to know how the World Wide Web proves to be a boon for data discovery. Note that not all data is relevant and updated. To make sense out of the gathered data sets, use web scraping. It is a simplified and automated process for extracting relevant data from the websites.
You are required to clean the data while you are collecting it. The sooner you get rid of the redundancies, the better! Some of the common sources of the data errors include duplicated entries gathered from across many databases and missing values in variables across databases etc. Techniques to eliminate these errors include filtering out the duplicates by referring to the common IDs and filling in the missing data entries with the mean value etc.
Data collection is time-consuming, often iterative, and quite often under-estimated. Data can be messy, and need to be curated in order to start the data exploratory analysis (EDA). Learning the data is a critical part of the research. If you observe missing values, you will research what the right values should be to fill in the missing values.
One can build an interactive dashboard and see how your data becomes a mirror to important insights. The picture would be clear and now you would know what is driving the variable features of your business. For example, if it is the pricing attribute, you would know when the price fluctuates and why.
When seeking to get hold of key patterns in business, feature engineering can be deployed. This step can’t be ignored as it forms the prerequisite for finalizing a suitable machine learning algorithm. In short, if the features are strong, the machine learning algorithm would produce awesome results.
This makes for one of the most important steps as the machine learning algorithm helps build a workable data model. There are many algorithms to choose from. In the words of data scientists, machine learning is the process of deploying machines for understanding a system or an underlying process and making changes for its improvement.
Here are the three types of machine learning methods you need to know about:
Once you are done with picking the right machine learning algorithm, next comes its evaluation. The stability of a model means it can continue to perform over time. The assessment will focus on evaluating (a) the overall fit of the model, (b) the significance of each predictor, and (c) the relationship between the target variable and each predictor. We also want to compare the lift of a newly constructed model over the existing model.
You need to validate the algorithm to check whether it produces the desired results for your business. Techniques such as cross-validation or even ROC (Receiver operating characteristic) curve, work well for generalizing the model output for new data. If the model appears to be producing satisfying results, you are all good to go!
Deploying machine learning models into production can be done in a wide variety of ways. The simplest form is the batch prediction. You take a dataset, run your model, and output a forecast on a daily or weekly basis.
The most common type of prediction is a simple web service. The raw data is transferred via the so-called REST API in real time. This data can be sent as arbitrary JSON which allows complete freedom to provide whatever data is available.
Over time a model will lose its predictability due to many reasons: the business environment may change, the procedure may change, more variables may become available or some variables become obsolete. You will monitor the predictability over time and decide to re-build the model.
To get the best out of the data science models, some of the methods to optimize data science modeling are:
Training a good model is a balancing act between generalization and specialization. A model is unlikely to ever get every prediction right because data is noisy, complex, and ambiguous.
A model must generalize to handle the variety within data, especially data that it hasn't been trained on. If a model generalizes too much, though, it might underfit the data. The model needs to specialize to learn the complexity of the data.
Alternatively, if the model specializes too much, it might overfit the data. Overfitted models learn the intricate local details on the data that they are trained on. When they're presented with new data or out-of-sample data, these local intricacies might not be valid. The ideal is for the model to be a good representation of the data on the whole, and to accept that some data points are outliers that the model will never get right.
Objective is to improve efficiency by making changes to the current state of the data model. Essentially, data model performs better after the data model goes through optimization. You might find that your report runs well in test and development environments, but when deployed to production for broader consumption, performance issues arise. From a report user's perspective, poor performance is characterized by report pages that take longer to load and visuals taking more time to update. This poor performance results in a negative user experience.
Poor performance is a direct result of a bad data model, bad Data Analysis Expressions (DAX), or the mix of the two. The process of designing a data model for performance can be tedious, and it is often underestimated. However, if you address performance issues during development, with the help of right visualization tools you will get better reporting performance and a more positive user experience.
Wherever you can, limit the data pulled to the only columns and rows you really need for reporting and ETL (Extract, Transform and Load) purposes. There is no need to overload your account with unused data, as it will slow down data processing and all dependent calculations.
The main way of tuning data science models is to adjust the model hyperparameters. Hyperparameters are input parameters that are configured before the model starts the learning process. They're called hyperparameters because models also use parameters. However, those parameters are internal to the model and are adjusted by the model during the training process.
Many data science libraries use default values for hyperparameters as a best guess. These values might create a reasonable model, but the optimal configuration depends on the data that is being modeled. The only way to work out the optimal configuration is through trial and error.
One needs to apply the above discussed methods and techniques in the data science toolkit appropriately to the specific analytics problems and evaluate the data that's available to address them. Good data scientists must always be able to understand the nature of the problem at hand and analyze, and see whether is it a clustering task, classification or regression one? and come up with the best algorithmic approach that can yield the expected answers given the characteristics and nature of the data.
Data science has already proven to solve some of the complex problems across the wide array of industries like education, healthcare , automobile, e-commerce, agriculture etc. and yet yield improved productivity, smart solutions, improved security and care, business intelligence:
The most common text classification is done in sentiment analysis, where texts are classified as positive or negative. Sometimes the problem at hand can be slightly harder, classifying whether a tweet is about an actual disaster happening or not. Not all tweets that contain words associated with disasters are actually about disasters. A tweet such as, "California forests on fire near San Francisco" is a tweet that should be taken into consideration, whereas "California this weekend was on fire, good times in San Francisco" can safely be ignored.
Data Science is an art as well as much as it is a science. By understanding the various techniques, methods, tools and analytical approaches, data scientists can help the organizations that employ them achieve the strategic and competitive benefits that many business rivals are already enjoying. In this post we have learnt what is data modeling in data science in detail. With the help of meaningful examples we discussed different types of data modeling.
Data Science models come with different flavors and techniques -- luckily, most advanced models are based on a couple of fundamentals. In this article we have discussed key data science modeling techniques in detail. As we have seen, building a data science model is a beautiful journey of collecting varied data sets and putting meaning to it. We have discussed various steps involved in data science modeling and some of the important strategies we should be aware of to further optimize data science modeling.
Considering the armory it’s equipped with, data science has varied applications across different sectors and business verticals. Data Science, like most of the technologies out there, is value neutral, and it’s only how they’re implemented and by whom that makes them either good or bad. With any new technology there’s a danger it could fall into the wrong hands. It’s up to all of us to ensure that it is developed responsibly for social good.
A data science model organizes data elements and standardizes how the data elements relate to one another and to the properties of real-world entities.
Different models in data analytics include linear regression, logistic regression, SVMs (Support Vector Machines), Random Forest, Naïve Bayes Classifiers and Decision Trees etc.
Each machine learning algorithm can be categorized into one of the three models: Supervised Learning. Unsupervised Learning. Reinforcement Learning.
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