Data Science | Knowledgehut Blog Category

Machine Learning Model Evaluation

By Harsha Vardhan Garlapati

If we were to list the technologies that have revolutionized and changed our lives for the better, then Machine Learning will occupy the top spot. This cutting-edge technology is used in a wide variety of applications in day-to-day life. ML has become an integral component in most of the industries like Healthcare, Software, Manufacturing, Business and aims to solve many complex problems while reducing human effort and dependency. This it does by accurately predicting solutions for problems and various applications.Generally there are two important stages in machine learning. They are Training & Evaluation of the model. Initially we take a dataset to feed to the machine learning model, and this process of feeding the data to our Designed ML model is called Training. In the training stage, the model learns the behavior of data, capable of handling different forms of data to better suit the model, draws conclusion from the data and finally predicts the end results using the model.This technique of training helps a user to know the output of the designed machine learning model for the given problem, the inputs given to the model, and the output that is obtained at the end of the model.But as machine learning model engineers, we might doubt the applicability of the model for the problem and have questions like, is the developed Machine learning model best suited for the problem, how accurate the model is, how can we say this is the best model that suits the given problem statement and what are the measures that describe model performance?In order to get clarity on the above questions, there is a technique called Model Evaluation, that describes the performance of the model and helps us understand if the designed model is suitable for the given problem statement or not.This article helps you to know, the various measures involved in calculating performance of a model for a particular problem and other key aspects involved.What is Model Evaluation?This technique of Evaluation helps us to know which algorithm best suits the given dataset for solving a particular problem. Likewise, in terms of Machine Learning it is called as “Best Fit”. It evaluates the performance of different Machine Learning models, based on the same input dataset. The method of evaluation focuses on accuracy of the model, in predicting the end outcomes.Out of all the different algorithms we use in the stage, we choose the algorithm that gives more accuracy for the input data and is considered as the best model as it better predicts the outcome. The accuracy is considered as the main factor, when we work on solving different problems using machine learning. If the accuracy is high, the model predictions on the given data are also true to the maximum possible extent.There are several stages in solving an ML problem like collection of dataset, defining the problem, brainstorming on the given data, preprocessing, transformation, training the model and evaluating. Even though there are several stages, the stage of Evaluation of a ML model is the most crucial stage, because it gives us an idea of the accuracy of model prediction. The performance and usage of the ML model is decided in terms of accuracy measures at the end.Model Evaluation TechniquesWe have known that the model evaluation is an Integral part in Machine Learning. Initially, the dataset is divided into two types, they are “Training dataset” and “Test dataset”. We build the machine learning model using the training dataset to see the functionality of the model. But we evaluate the designed Model using a test dataset, which consists of unseen or unknown samples of the data that are not used for training purposes. Evaluation of a model tells us how accurate the results were. If we use the training dataset for evaluation of the model, for any instance of the training data it will always show the correct predictions for the given problem with high accuracy measures, in that case our model is not adequately effective to use. There are two methods that are used to evaluate a model performance. They are Holdout Cross ValidationThe Holdout method is used to evaluate the model performance and uses two types of data for testing and training. The test data is used to calculate the performance of the model whereas it is trained using the training data set. This method is used to check how well the machine learning model developed using different algorithm techniques performs on unseen samples of data. This approach is simple, flexible and fast.Cross-validation is a procedure of dividing the whole dataset into data samples, and then evaluating the machine learning model using the other samples of data to know accuracy of the model. i.e., we train the model using a subset of data and we evaluate it with a complementary data subset. We can calculate cross validation based on the following 3 methods, namely Validation Leave one out cross validation (LOOCV) K-Fold Cross ValidationIn the method of validation, we split the given dataset into 50% of training and 50% for testing purpose. The main drawback in this method is that the remaining 50% of data that is subjected to testing may contain some crucial information that may be lost while training the model. So, this method doesn’t work properly due to high bias.In the method of LOOCV, we train all the datasets in our model and leave a single data point for testing purpose. This method aims at exhibiting lower bias, but there are some chances that this method might fail because, the data-point that has been left out may be an outlier in the given data; and in that case we cannot produce better results with good accuracy. K-fold cross validation is a popular method used for evaluation of a Machine Learning model. It works by splitting the data into k-parts. Each split of the data is called a fold. Here we train all the k subsets of data to the model, and then we leave out one (k-1) subset to perform evaluation on the trained model. This method results in high accuracy and produces data with less bias.Types of Predictive ModelsPredictive models are used to predict the outcomes from the given data by using a developed ML model. Before getting the actual output from the model, we can predict the outcomes with the help of given data. The prediction models are widely used in machine learning, to guess the outcomes from the data before designing a model. There are different types of predictive models: Classification model Clustering model Forecast model Outlier modelA Classification model is used in decision making problems. It separates the given data into different categories, and this model is best suited to answer “Yes” or “No” questions. It is the simplest of all the predictive models.Real Life Applications: Projects like Gender Classification, Fraud detection, Product Categorization, Malware classification, documents classification etc.Clustering models are used to group the given data based on similar attributes. This model helps us to know how many groups are present in the given dataset and we can analyze what are the groups, which we should focus on to solve the given problem statement.Real Life Applications: Projects like categorizing different people present in a classroom, types of customers in a bank, identifying fake news, spam filter, document analysis etc.A forecast model learns from the historical data in order to predict the new data based on learning. It majorly deals with metric values.Real Life Applications: Projects like weather forecast, sales forecast, stocks prices, Heart Rate Monitoring etc.Outlier model focuses on identifying irrelevant data in the given dataset. If the data consists of outliers, we cannot get good results as the outliers have irrelevant data. The outliers may have categorical or numerical type of data associated with them.Real Life Applications: Major applications are used in Retail Industries, Finance Industries, Quality Control, Fault Diagnosis, web analytics etc.Classification MetricsIn order to evaluate the performance of a Machine Learning model, there are some Metrics to know its performance and are applied for Regression and Classification algorithms. The different types of classification metrics are: Classification Accuracy Confusion Matrix Logarithmic Loss Area under Curve (AUC) F-MeasureClassification AccuracyClassification accuracy is similar to the term Accuracy. It is the ratio of the correct predictions to the total number of Predictions made by the model from the given data.We can get better accuracy if the given data samples have the same type of data related to the given problem statement. If the accuracy is high, the model is more accurate and we can use the model in the real world and for different types of applications also.If the accuracy is less, it shows that the data samples are not correctly classified to suit the given problem.Confusion MatrixIt is a NxN matrix structure used for evaluating the performance of a classification model, where N is the number of classes that are predicted. It is operated on a test dataset in which the true values are known. The matrix lets us know about the number of incorrect and correct predictions made by a classifier and is used to find correctness of the model. It consists of values like True Positive, False Positive, True Negative, and False Negative, which helps in measuring Accuracy, Precision, Recall, Specificity, Sensitivity, and AUC curve. The above measures will talk about the model performance and compare with other models to describe how good it is.There are 4 important terms in confusion matrix: True Positives (TP): The cases in which our predictions are TRUE, and the actual output was also TRUE. True Negatives (TN): The cases in which our predictions are FALSE, and the actual output was also FALSE. False Positives (FP): The cases in which our predictions are TRUE, and the actual output was FALSE. False Negative (FN): The cases in which our predictions are FALSE, and the actual output was TRUE. The accuracy can be calculated by using the mean of True Positive and True Negative values of the total sample values. It tells us about the total number of predictions made by the model that were correct. Precision is the ratio of Number of True Positives in the sample to the total Positive samples predicted by the classifier. It tells us about the positive samples that were correctly identified by the model. Recall is the ratio of Number of True Positives in the sample to the sum of True Positive and False Negative samples in the data. F1 ScoreIt is also called as F-Measure. It is a best measure of the Test accuracy of the developed model. It makes our task easy by eliminating the need to calculate Precision and Recall separately to know about the model performance. F1 Score is the Harmonic mean of Recall and Precision. Higher the F1 Score, better the performance of the model. Without calculating Precision and Recall separately, we can calculate the model performance using F1 score as it is precise and robust.Sensitivity is the ratio of Number of actual True Positive Samples to the sum of True Positive and False Positive Samples. It tells about the positive samples that are identified correctly with respect to all the positive data samples in the given data. It is also called as True Positive Rate. Specificity is also called the True Negative Rate. It is the ratio of the Number of True Negatives in the sample to the sum of True negative and the False positive samples in the given dataset. It tells about the number of actual Negative samples that are correctly identified from the given dataset. False positive rate is defined as 1-specificity. It is the ratio of number of False Positives in the sample to the sum of False positive and True Negative samples. It tells us about the Negative data samples that are classified as Positive, with respect to all Negative data samples.For each value of sensitivity, we get a different value of specificity and they are associated as follows: Area Under the ROC Curve (AUC - ROC)It is a widely used Evaluation Metric, mainly used for Binary Classification. The False positive rates and the True positive rates have the values ranging from 0 to 1. The TPR and FPR are calculated with different threshold values and a graph is drawn to better understand about the data. Thus, the Area Under Curve is the plot between false positive rate and True positive rate at different values of [0,1].Logarithmic LossIt is also called Log Loss. As we know, the AUC ROC determines the model performance using the predicted probabilities, but it does not consider model capability to predict the higher probability of samples to be more likely positive. This technique is mostly used in Multi-class Classification. It is calculated to the negative average of the log of correctly predicted probabilities for each instance. where, y_ij, indicates whether sample i belongs to class j or not p_ij, indicates the probability of sample i belonging to class j Regression MetricsIt helps to predict the state of outcome at any time with the help of independent variables that are correlated. There are mainly 3 different types of metrics used in regression. These metrics are designed in order to predict if the data is underfitted or overfitted for the better usage of the model. They are:- Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE)Mean Absolute Error is the average of the difference of the original values and the predicted values. It gives us an idea of how far the predictions are from the actual output. It doesn’t give clarity on whether the data is under fitted or over fitted. It is calculated as follows:The mean squared error is similar to the mean absolute error. It is computed by taking the average of the square of the difference between original and predicted values. With the help of squaring, large errors can be converted to small errors and large errors can be dealt with. It is computed as follows. The root mean squared error is the root of the mean of the square of difference of the predicted and actual values of the given data. It is the most popular metric evolution technique used in regression problems. It follows a normal distribution and is based on the assumption that errors are unbiased. It is computed using the below formulae.Bias vs VarianceBias is the difference between the Expected value and the Predicted value by our model. It is simply some assumptions made by the model to make the target function easier to learn. The low bias indicates fewer assumptions, whereas the high bias talks about more assumptions in the target data. It leads to underfitting of the model.Variance takes all types of data including noise into it. The model considers the variance as something to learn, and the model learns too much from the trained data, and at the end the model fails in giving out accurate results to the given problem statement. In case of high variance, the model learns too much and it can lead to overfitting of the model. ConclusionWhile building a machine learning model for a given problem statement there are two important stages, namely training and testing. In the training stage, the models learn from the data and predict the outcomes at the end. But it is crucial that predictions made by the developed model are accurate. This is why the stage of testing is the most crucial stage, because it can guarantee how accurate the results were to implement for the given problem. In this blog, we have discussed about various types of Evaluation techniques to achieve a good model that best suits a given problem statement with highly accurate results. We need to check all the above-mentioned parameters to be able to compare our model performance as compared to other models.

Machine Learning Model Evaluation

By Harsha Vardhan Garlapati

If we were to list the technologies that have revolutionized and changed our lives for the better, then Machine Learning will occupy the top spot. This cutting-edge technology is used in a wide variety of applications in day-to-day life. ML has become an integral component in most of the industries like Healthcare, Software, Manufacturing, Business and aims to solve many complex problems while reducing human effort and dependency. This it does by accurately predicting solutions for problems and various applications.Generally there are two important stages in machine learning. They are Training & Evaluation of the model. Initially we take a dataset to feed to the machine learning model, and this process of feeding the data to our Designed ML model is called Training. In the training stage, the model learns the behavior of data, capable of handling different forms of data to better suit the model, draws conclusion from the data and finally predicts the end results using the model.This technique of training helps a user to know the output of the designed machine learning model for the given problem, the inputs given to the model, and the output that is obtained at the end of the model.But as machine learning model engineers, we might doubt the applicability of the model for the problem and have questions like, is the developed Machine learning model best suited for the problem, how accurate the model is, how can we say this is the best model that suits the given problem statement and what are the measures that describe model performance?In order to get clarity on the above questions, there is a technique called Model Evaluation, that describes the performance of the model and helps us understand if the designed model is suitable for the given problem statement or not.This article helps you to know, the various measures involved in calculating performance of a model for a particular problem and other key aspects involved.What is Model Evaluation?This technique of Evaluation helps us to know which algorithm best suits the given dataset for solving a particular problem. Likewise, in terms of Machine Learning it is called as “Best Fit”. It evaluates the performance of different Machine Learning models, based on the same input dataset. The method of evaluation focuses on accuracy of the model, in predicting the end outcomes.Out of all the different algorithms we use in the stage, we choose the algorithm that gives more accuracy for the input data and is considered as the best model as it better predicts the outcome. The accuracy is considered as the main factor, when we work on solving different problems using machine learning. If the accuracy is high, the model predictions on the given data are also true to the maximum possible extent.There are several stages in solving an ML problem like collection of dataset, defining the problem, brainstorming on the given data, preprocessing, transformation, training the model and evaluating. Even though there are several stages, the stage of Evaluation of a ML model is the most crucial stage, because it gives us an idea of the accuracy of model prediction. The performance and usage of the ML model is decided in terms of accuracy measures at the end.Model Evaluation TechniquesWe have known that the model evaluation is an Integral part in Machine Learning. Initially, the dataset is divided into two types, they are “Training dataset” and “Test dataset”. We build the machine learning model using the training dataset to see the functionality of the model. But we evaluate the designed Model using a test dataset, which consists of unseen or unknown samples of the data that are not used for training purposes. Evaluation of a model tells us how accurate the results were. If we use the training dataset for evaluation of the model, for any instance of the training data it will always show the correct predictions for the given problem with high accuracy measures, in that case our model is not adequately effective to use. There are two methods that are used to evaluate a model performance. They are Holdout Cross ValidationThe Holdout method is used to evaluate the model performance and uses two types of data for testing and training. The test data is used to calculate the performance of the model whereas it is trained using the training data set. This method is used to check how well the machine learning model developed using different algorithm techniques performs on unseen samples of data. This approach is simple, flexible and fast.Cross-validation is a procedure of dividing the whole dataset into data samples, and then evaluating the machine learning model using the other samples of data to know accuracy of the model. i.e., we train the model using a subset of data and we evaluate it with a complementary data subset. We can calculate cross validation based on the following 3 methods, namely Validation Leave one out cross validation (LOOCV) K-Fold Cross ValidationIn the method of validation, we split the given dataset into 50% of training and 50% for testing purpose. The main drawback in this method is that the remaining 50% of data that is subjected to testing may contain some crucial information that may be lost while training the model. So, this method doesn’t work properly due to high bias.In the method of LOOCV, we train all the datasets in our model and leave a single data point for testing purpose. This method aims at exhibiting lower bias, but there are some chances that this method might fail because, the data-point that has been left out may be an outlier in the given data; and in that case we cannot produce better results with good accuracy. K-fold cross validation is a popular method used for evaluation of a Machine Learning model. It works by splitting the data into k-parts. Each split of the data is called a fold. Here we train all the k subsets of data to the model, and then we leave out one (k-1) subset to perform evaluation on the trained model. This method results in high accuracy and produces data with less bias.Types of Predictive ModelsPredictive models are used to predict the outcomes from the given data by using a developed ML model. Before getting the actual output from the model, we can predict the outcomes with the help of given data. The prediction models are widely used in machine learning, to guess the outcomes from the data before designing a model. There are different types of predictive models: Classification model Clustering model Forecast model Outlier modelA Classification model is used in decision making problems. It separates the given data into different categories, and this model is best suited to answer “Yes” or “No” questions. It is the simplest of all the predictive models.Real Life Applications: Projects like Gender Classification, Fraud detection, Product Categorization, Malware classification, documents classification etc.Clustering models are used to group the given data based on similar attributes. This model helps us to know how many groups are present in the given dataset and we can analyze what are the groups, which we should focus on to solve the given problem statement.Real Life Applications: Projects like categorizing different people present in a classroom, types of customers in a bank, identifying fake news, spam filter, document analysis etc.A forecast model learns from the historical data in order to predict the new data based on learning. It majorly deals with metric values.Real Life Applications: Projects like weather forecast, sales forecast, stocks prices, Heart Rate Monitoring etc.Outlier model focuses on identifying irrelevant data in the given dataset. If the data consists of outliers, we cannot get good results as the outliers have irrelevant data. The outliers may have categorical or numerical type of data associated with them.Real Life Applications: Major applications are used in Retail Industries, Finance Industries, Quality Control, Fault Diagnosis, web analytics etc.Classification MetricsIn order to evaluate the performance of a Machine Learning model, there are some Metrics to know its performance and are applied for Regression and Classification algorithms. The different types of classification metrics are: Classification Accuracy Confusion Matrix Logarithmic Loss Area under Curve (AUC) F-MeasureClassification AccuracyClassification accuracy is similar to the term Accuracy. It is the ratio of the correct predictions to the total number of Predictions made by the model from the given data.We can get better accuracy if the given data samples have the same type of data related to the given problem statement. If the accuracy is high, the model is more accurate and we can use the model in the real world and for different types of applications also.If the accuracy is less, it shows that the data samples are not correctly classified to suit the given problem.Confusion MatrixIt is a NxN matrix structure used for evaluating the performance of a classification model, where N is the number of classes that are predicted. It is operated on a test dataset in which the true values are known. The matrix lets us know about the number of incorrect and correct predictions made by a classifier and is used to find correctness of the model. It consists of values like True Positive, False Positive, True Negative, and False Negative, which helps in measuring Accuracy, Precision, Recall, Specificity, Sensitivity, and AUC curve. The above measures will talk about the model performance and compare with other models to describe how good it is.There are 4 important terms in confusion matrix: True Positives (TP): The cases in which our predictions are TRUE, and the actual output was also TRUE. True Negatives (TN): The cases in which our predictions are FALSE, and the actual output was also FALSE. False Positives (FP): The cases in which our predictions are TRUE, and the actual output was FALSE. False Negative (FN): The cases in which our predictions are FALSE, and the actual output was TRUE. The accuracy can be calculated by using the mean of True Positive and True Negative values of the total sample values. It tells us about the total number of predictions made by the model that were correct. Precision is the ratio of Number of True Positives in the sample to the total Positive samples predicted by the classifier. It tells us about the positive samples that were correctly identified by the model. Recall is the ratio of Number of True Positives in the sample to the sum of True Positive and False Negative samples in the data. F1 ScoreIt is also called as F-Measure. It is a best measure of the Test accuracy of the developed model. It makes our task easy by eliminating the need to calculate Precision and Recall separately to know about the model performance. F1 Score is the Harmonic mean of Recall and Precision. Higher the F1 Score, better the performance of the model. Without calculating Precision and Recall separately, we can calculate the model performance using F1 score as it is precise and robust.Sensitivity is the ratio of Number of actual True Positive Samples to the sum of True Positive and False Positive Samples. It tells about the positive samples that are identified correctly with respect to all the positive data samples in the given data. It is also called as True Positive Rate. Specificity is also called the True Negative Rate. It is the ratio of the Number of True Negatives in the sample to the sum of True negative and the False positive samples in the given dataset. It tells about the number of actual Negative samples that are correctly identified from the given dataset. False positive rate is defined as 1-specificity. It is the ratio of number of False Positives in the sample to the sum of False positive and True Negative samples. It tells us about the Negative data samples that are classified as Positive, with respect to all Negative data samples.For each value of sensitivity, we get a different value of specificity and they are associated as follows: Area Under the ROC Curve (AUC - ROC)It is a widely used Evaluation Metric, mainly used for Binary Classification. The False positive rates and the True positive rates have the values ranging from 0 to 1. The TPR and FPR are calculated with different threshold values and a graph is drawn to better understand about the data. Thus, the Area Under Curve is the plot between false positive rate and True positive rate at different values of [0,1].Logarithmic LossIt is also called Log Loss. As we know, the AUC ROC determines the model performance using the predicted probabilities, but it does not consider model capability to predict the higher probability of samples to be more likely positive. This technique is mostly used in Multi-class Classification. It is calculated to the negative average of the log of correctly predicted probabilities for each instance. where, y_ij, indicates whether sample i belongs to class j or not p_ij, indicates the probability of sample i belonging to class j Regression MetricsIt helps to predict the state of outcome at any time with the help of independent variables that are correlated. There are mainly 3 different types of metrics used in regression. These metrics are designed in order to predict if the data is underfitted or overfitted for the better usage of the model. They are:- Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE)Mean Absolute Error is the average of the difference of the original values and the predicted values. It gives us an idea of how far the predictions are from the actual output. It doesn’t give clarity on whether the data is under fitted or over fitted. It is calculated as follows:The mean squared error is similar to the mean absolute error. It is computed by taking the average of the square of the difference between original and predicted values. With the help of squaring, large errors can be converted to small errors and large errors can be dealt with. It is computed as follows. The root mean squared error is the root of the mean of the square of difference of the predicted and actual values of the given data. It is the most popular metric evolution technique used in regression problems. It follows a normal distribution and is based on the assumption that errors are unbiased. It is computed using the below formulae.Bias vs VarianceBias is the difference between the Expected value and the Predicted value by our model. It is simply some assumptions made by the model to make the target function easier to learn. The low bias indicates fewer assumptions, whereas the high bias talks about more assumptions in the target data. It leads to underfitting of the model.Variance takes all types of data including noise into it. The model considers the variance as something to learn, and the model learns too much from the trained data, and at the end the model fails in giving out accurate results to the given problem statement. In case of high variance, the model learns too much and it can lead to overfitting of the model. ConclusionWhile building a machine learning model for a given problem statement there are two important stages, namely training and testing. In the training stage, the models learn from the data and predict the outcomes at the end. But it is crucial that predictions made by the developed model are accurate. This is why the stage of testing is the most crucial stage, because it can guarantee how accurate the results were to implement for the given problem. In this blog, we have discussed about various types of Evaluation techniques to achieve a good model that best suits a given problem statement with highly accurate results. We need to check all the above-mentioned parameters to be able to compare our model performance as compared to other models.

What Is Factor Analysis in Data Science?

By Dipayan Ghatak

Factor analysis is a part of the general linear model (GLM). It is a method in which large amounts of data are collected and reduced in size to a smaller dataset. This reduction in the size of the dataset ensures that the data is manageable and easily understood by people. In addition to manageability and interpretability, it helps extract patterns in data as well as show the characteristics that are commonly seen in the different patterns (that are extracted). It helps create a variable set for data points in the datasets that are similar. This similar set of data is also known as dimensions. AssumptionAn assumption while dealing with factor analysis is that, in a collection of the variables observed, there is a set of underlying variables, which is known as ‘factor’. This factor helps explain the inter-relationship between these variables. There should be a linear relationship between the variables in the data. There should be no multicollinearity between variables in the data. There should be true correlation between the variables and factors in the data. There are multiple methods to extract factors from data, but principal component analysis is one of the most frequently used methods. In Principal component analysis (PCA), maximum variance is extracted and placed in the first factor. Once this is done, the variance explained by the first set of factors is eliminated and then maximum variance is again extracted for the second factor. This goes on until the last factor in the variable set. Types of factor analysisThe word ‘factor’ in factor analysis refers to the variable set which has similar patterns. They are sometimes associated with a hidden variable, which is also known as confounding variable. This hidden variable is not measured directly. The ‘factors’ talk about the variation in data which can be explained. There are two types of factors: Exploratory;Confirmatory Exploratory factor analysisThis deals with data that is unstructured or when the person/s dealing with the data are clueless about the structure of the data and the dimensions of the variable associated with the data. Exploratory factor analysis gives information about the optimum number of factors which may be required to represent the data. If a researcher wishes to explore patterns, it is suggested to use exploratory factor analysis.Confirmatory factor analysisThis kind of analysis is used to verify the structure of the data, given the condition that the people dealing with the data are aware of its structure and dimensions of the variable associated with the data. This kind of analysis helps specify the number of factors required to perform the analysis.Factor analysis is a multivariate method- this means it deals with multiple variables associated with data. This is a data reduction technique wherein the basic idea is to use a smaller set of variables, which is known as ‘factors’, that is a representation of a bigger set of variables.It helps the researcher in understanding whether the relationship between the observed variables (aka manifest variables) and their underlying construct exists or not.If a researcher wishes to perform hypothesis testing, it is suggested to use exploratory factor analysis.What are factors?Factors can be understood as a construct which can’t be measured with the help of a single variable. Factor analysis is generally used with interval data, but it can be used for ordinal data as well. What is ordinal data?Ordinal data is statistical data in which variables exist in naturally occurring categories that are in a particular order. The distance between categories in ordinal data can’t be found using ordinal data itself.For a dataset to be ordinal data, it needs to fulfil a few conditions. Multiple terms in the dataset are in an ordered fashion. The difference between variables in the dataset is not homogeneous/uniform. A group of ordinal numbers indicates ordinal data, and a group of ordinal data can be represented using an ordinal scale.Likert Scale is one type of ordinal data. Let us understand Likert scale with the help of an example:Suppose we have a question that says “Please indicate how satisfied you are with this product purchase”. A Likert scale may have numbers between 0/1 to 5 or 0/1 to 10. On this scale, 0/1 indicates a lesser value and 5 or 10 indicates a higher value.Let us understand ordinal data with the help of another example. If we have variables stored in a specific order, say “low, medium, high” or “not happy, slightly happy, happy, very happy, extremely happy”, it is considered as ordinal data.Conditions for variables in factor analysisThese variables (in factor analysis) need to be linearly associated with each other. Linear relationship or association describes a relationship that forms a straight line when two variables are plotted on a graph. It can also be represented as a mathematical equation in the form ‘y = mx + b’.This linear associativity can be checked by plotting scatterplots of the pairs of variables. This indicates that the variables need to be moderately correlated to each other.If the variables are not correlated, the number of factors will be the same as the number of original variables. This means that performing factor analysis on this kind of variables would be useless.How can factor analysis be performed?Factor analysis is a complex mathematical procedure. It can be performed with the help of software applications. Before performing the analysis, it is essential to check if the data is relevant. This can be done with the help of Kaiser-Meyer-Olkin test.Kaiser-Meyer-Olkin testThis is also known as the KMO test, which is used to see how well the data is suited to perform factor analysis. It measures the sampling adequacy for every variable in the model.This statistic measures the proportion of variance among all the variables in the data. The lower the proportion, more suited the data is to perform factor analysis.KMO returns values between 0 and 1.If KMO value lies between 0.8 and 1, it means that the sampling is adequate.If KMO value is less than 0.6 or lies between 0.5 and 0.6, it means that the sampling is not adequate. This means proper actions need to be taken.If KMO value is closer to 0, this indicates that the data contains large number of partial correlations in comparison to the sum of correlations. This is not suited for factor analysis. Values between 0 and 0.49 are considered unacceptable. Values between 0.50 and 0.59 are considered not good. Values between 0.60 and 0.69 are considered mediocre. Values between 0.70 and 0.79 are considered to be good. Values between 0.80 and 0.89 are considered to be great. Values between 0.90 and 1.00 are considered to be absolutely fantastic. The formula to perform KMO test is:Here, R = which is the correlation matrix; and U = which is the partial covariance matrix.Once the relevant data has been collected, factor analysis can be performed in a variety of ways.Using StataIt can be performed in Stata with the help of postestimation command- ‘estat kmo’.Using RIt can be performed in R using the command ‘KMO(r)’ where ‘r’ refers to the correlation matrix that needs to be analysed.Using SPSSSPSS is a statistical platform that can be used to run factor analysis. First go to Analyze -> Dimension Reduction -> Factor, and check the “KMO and Bartlett’s test of sphericity” box.If the measure of sampling adequacy (MSA) for single variable is needed, the ‘”anti-image” box needs to be checked. An ‘anti-image’ box shows the MSAs listed in diagonals of matrix.The test can also be executed by specifying KMO in the Factor Analysis command. The KMO statistic is found in the “KMO and Bartlett’s Test” table in the Factor output.ConclusionIn short, Factor Analysis brings in simplicity after reducing variables. Factor Analysis, including Principal Component Analysis, is also often used along with segmentation studies. In this post, we understood about the factor analysis method, and the assumptions made before working on the method. We also saw different kinds of factor analysis, and how they can be performed on different platforms.

Combining Models – Python Machine Learning

By Harsha Vardhan Garlapati

Machine Learning is emerging as the latest technology these days, and is solving many problems that are impossible for humans. This technology has extended its wings into diverse industries like Automobile, Manufacturing, IT services, Healthcare, Robotics and so on. The main reason behind using this technology is that it provides more accurate solutions for problems, simplifies tasks and eases work processes. It automates the world with its applications that are helpful for many organizations and for the well-being of people. This technology uses the input data to develop a model, and further predicts the outcomes to know the performance of the model.Generally, we develop machine learning models to solve a problem by using the given input data. When we work on a single algorithm, we are unable to distinguish the performance of the model for that particular statement, as there is nothing to compare it against. So, we feed the input data to other machine learning algorithms and then compare them with each other to know which is the best algorithm that suits the given problem. Every algorithm has its own mathematical computation and significance to deal with a specific problem to bring out the best results at the end.Why do we combine models?While dealing with a specific problem with a machine learning algorithm we sometimes fail, because of the poor performance of the model. The algorithm that we have used may be well suited to the model, but we still fail in getting better outcomes at the end. In this situation, we might have many questions in our mind. How can we bring out better results from the model? What are the steps to be taken further in the model development? What are the hidden techniques that can help to develop an efficient model?To overcome this situation there is a procedure called “Combining Models”, where we mix one or two weaker machine learning models to solve a problem and get better outcomes. In machine learning, the combining of models is done by using two approaches namely “Ensemble Models” & “Hybrid Models”.Ensemble Models use multiple machine learning algorithms to bring out better predictive results, as compared to using a single algorithm. There are different approaches in Ensemble models to perform a particular task. There is another model called Hybrid model that is flexible and helps to create a more innovative model than an Ensemble model. While combining models we need to check how strong or weak a particular machine learning model is, to deal with a particular problem.What are Ensemble Methods?An Ensemble is made up of things that are grouped together, that take up a particular task. This method combines several algorithms together to bring out better predictive results, as compared to using a single algorithm. The objective behind the usage of an Ensemble method is that it decreases variance, bias and improves predictions in a developed model. Technically speaking, it helps in avoiding overfitting.The models that contribute to an Ensemble are referred to as the Ensemble Members, which may be of the same type or different types, and may or may not be trained on the same training data.In the late 2000s, adoption of ensembles picked up due in part to their huge success in machine learning competitions, such as the Netflix Prize and other competitions on Kaggle.These ensemble methods greatly increase the computational cost and complexity of the model. This increase comes from the expertise and time required to train and maintain multiple models rather than a single model.Ensemble models are preferred because of two main reasons; namely Performance & Robustness. The ensemble methods majorly focus on improving the accuracy of the model by reducing variance component of the prediction error and by adding bias to the model.Performance helps a Machine Learning model to make better predictions. Robustness reduces the spread or dispersion of the prediction and model performance.The goal of a supervised machine learning algorithm is to have “low bias and low variance”.The Bias and the Variance are inversely proportional to each other i.e., if the bias is low then the variance is high, else the bias is high then the variance is low.We explicitly use ensemble methods to seek better predictive performance, such as lower error on regression or higher accuracy for classification. They are also further used in Computer vision and are given utmost importance in academic competitions also.Decision TreesThis type of algorithm is commonly used in decision analysis and operation Research, and it is one of the mostly used algorithms in the context of Machine Learning.The decision tree algorithm aims to produce better results for small and large amounts of data, which are taken as input data and fed to the model. These algorithms are majorly used in decision making problem statements.The decision tree algorithm is a tree like structure consisting of nodes at each stage. The top of the tree is the Root Node which describes the main problem that we deal with, and there are Sub Nodes which act as classes or labels for the data given in the dataset. The Leaf Node is the last layer of the decision tree, representing the outcomes or values of the problem.The tree structure is extended with a number of nodes till a perfect prediction is made from the given data using the model. Decision tree algorithms are used in classification as well as regression problems. This algorithm is widely used in machine learning to solve problems, and the main advantage of this model is that we can have 2 or more outputs, from which we can select the most suitable one for the given problem.These can operate on both small and large amounts of data. Decisions taken using this algorithm are often fast and accurate. In machine learning the different types of Decision Tree algorithms includeClassification and Regression Tree (CART)Decision stumpChi-squared automatic interaction detection (CHAID)Types of Ensemble MethodsEnsemble methods are used to improve the accuracy of the model by reducing the bias and variance. These methods are widely used in dealing with Classification and Regression Problems. In ensemble method, several models combine together to form one reliable model that results in improving accuracy at the end.Ensemble methods are widely classified into the following types to exhibit better performance of the model. They are:BaggingBoostingStackingThese ensemble methods are broadly classified into four categories, namely “Sequential methods”, “Parallel methods”, “Homogeneous Ensemble” and “Heterogeneous Ensemble”. They help us to differentiate the performance and accuracy of models for a problem.Sequential methods generate sequential base learners who are data dependent. Here the new data we take as input to the model is dependent on the previous data, and the data which is mislabeled previously by the model is tuned with weights to get better accuracies at the end. This technique is possible in “BOOSTING”, for example in Adaptive Boosting (AdaBoost).Parallel methods generate parallel order base learners in which the data is independent. This independence of the base learners on the data significantly reduces the error with the application of averages. This technique is possible in “STACKING”, for example in Random Forest.A Homogenous ensemble is a combination of the same type of classifiers. Even though the dataset consists of different classifiers, this ensemble technique makes a model that best suits a given problem. This type of technique is computationally expensive and is suitable for solving large datasets. “BAGGING” & “BOOSTING” are the popular methods that exhibit homogeneous ensemble.Heterogeneous ensemble is a combination of different types of classifiers, in which each classifier is based on the same data. This method works on small datasets. “STACKING” comes in this category.BaggingBagging is a short form of Bootstrap Aggregating, used to improve the accuracy of the model. It is used when dealing with problems related to Classification and Regression. This technique improves the accuracy of the model by reducing variance, and helps to prevent the overfitting of the model. Bagging can be applied with any type of method in machine learning, but generally it is implemented using Decision Trees.Bagging is an ensemble technique, in which several models are grouped together to make one single reliable model to improve the accuracy. In the technique of bagging, we fit several independent models together and average their predictions to get a model that results in low variance and high accuracy to the model.Bootstrapping is a sampling technique, where we obtain the data in the form of samples. The samples are derived from the whole population with the help of replacement procedure. The sampling technique with the help of replacement method helps the learners to make the selection procedure randomized. Now the base learning algorithm is run across the samples to complete the procedure for better results.Aggregation is a technique in bagging that helps to incorporate all the possible outcomes of the predictions and randomizes the outcomes at the end. Without the usage of aggregation, the predictions will not be that accurate, because all the outcomes that are obtained at the end of the model are not taken into consideration. Thus, the aggregation is used based on the probability bootstrapping procedures or on the basis of all outcomes of the predictive models.Bagging is an advantageous procedure in Machine Learning, as it combines all the weak base learners that come together to form a single strong learner which is more stable. This technique reduces variance, thereby increasing the accuracy to the model. It prevents overfitting of the model. The limitation for bagging is that it is computationally expensive. When the proper procedure for bagging is established, we should not ignore bias as it fails in obtaining better results at the end.Random Forest ModelsIt is a supervised machine learning algorithm, which is flexible and widely used because of its simplicity and diversity. It produces great results without hyper-parameter tuning.In the term “Random Forest”, the “Forest” refers to a group of decision trees or an ensemble of decision trees, usually trained with the method of “Bagging”. We know that the method of bagging is the combination of learning models that increases the overall result.Random forest is used for classification and regression problems. It builds many decision trees and combines them together to get a more accurate and stable prediction at the end of the model.Random forest adds additional randomness to the model, while growing the trees. Instead of finding the most important feature at the time of splitting a node, the random forest model searches for the best feature among a random subset of features. Thus in random forest, only a random subset of features is taken into consideration by the algorithm for node splitting.Random forest has the quality of measuring the relative importance of each feature on the prediction. In order to use the random forest algorithm, we import a tool “Sklearn”, which measures features importance by looking at the amount of tree nodes used to reduce the impurity across all the trees in the forest.The benefits of using random forest include the following:The training time is less compared to other algorithms.Runs efficiently on a large dataset, and predicts output with high accuracy.When a large proportion of data is missing it also maintains accuracy.It is flexible to apply and outcomes are obtained easily.BoostingBoosting is an ensemble technique, which converts the weak machine learning models into strong models. The main goal of this technique is to reduce bias and variance of a model to improve accuracy. This technique learns from the previous predictor mistakes of data to make better predictions in future by improving the performance of the model.It is a stack like structure in which the weak learners are placed at the bottom and the strong learners are placed at the top. In the stack, the learners at the upper layers initially learn from the weak learners by applying some sort of modifications to the previous techniques.It exists in many forms, that includes XGBoost (Extreme Gradient Boosting), Gradient Boosting, Adaptive Boosting (AdaBoost).AdaBoost makes use of weak learners that are in the form of decision trees, which includes one split normally known as decision stumps. The main decision stumps of Adaboost comprises of observations carrying similar weights.Gradient Boosting follows the sequential addition of predictors to an ensemble, each correcting the previous one. Without changing the weights of incorrect classified observations like Adaboost, this Gradient boosting technique places a new predictor based on the residual errors made by the previous predictors in the generated model.XGBoost is called as Extreme Gradient Boosting. It is designed in order to show better speed and performance of the machine learning model, that we developed. XGBoost technique is an implementation of Gradient Boosted Decision Trees. Generally, normal boosting techniques are very slow as they are in sequential form of training, so XGBoost technique is widely used to have good computational speed and to show better model performance.Simple Averaging / Weighted MethodIt is a technique to improve the accuracy of the model, mainly used for regression problems. It is based on the weights of the model multiplied with the actual instance values in the given problem. This method produces some consistent results that are reliable and help to get a better understanding about the outcomes of the given problem.In the case of a simple averaging method, average predictions are calculated for every instance of the test dataset. It can reduce the overfitting of the model, and is mainly suitable for regression problems as it consists of numerical data. It creates a smoother regression model at the end by reducing the effect of overfitting. The technique of simple averaging is like calculating the mean of the given values.The weighted averaging method is a slight modification to the simple averaging method, in which the prediction values are multiplied with the weight factor and sum up all the multiplied values for every instance. We then calculate the average. We assume that the predicted values are in the range of 0 to 1.StackingThis method is a combination of multiple regression or classifier techniques with a meta-regressor or meta-classifier. Stacking is different from bagging and boosting. Bagging and boosting models work mainly on homogeneous weak learners and don’t consider heterogeneous learners, whereas stacking works mainly on heterogeneous weak learners, and consists of different algorithms altogether.The bagging and boosting techniques combine weak learners with the help of deterministic algorithms, whereas the stacking method combines the weak base learners with the help of a meta-model. As we defined earlier, when using stacking, we learn from several weak base learners and combine them together by training with a meta-model to predict the results that are predicted by the weak learners used in the model.Stacking results in a pile-like structure, in which the lower-level output is used as the input to the next layer. In the same way the stack increases from maximum error rate at the bottom to the minimum error rate area at the top. The top layer in the stack has good prediction accuracy compared to the lower levels. The aim of stacking is to produce a low bias model for accurate results for a given problem.BlendingIt is a technique similar to the stacking approach, but uses only the validation set from the training set of the model to make predictions. The validation set is also called a holdout set.The blending technique uses a holdout set to make predictions for the given problem. With the help of holdout set and the predictions, a model is built which will run across the test set. The process of blending is explained below:Train dataset is divided into training and validation setsThe model is fitted on to the training setPredictions are made on the validation set and the test setNow the validation set and the predictions are used as features to build a new modelThis developed model is used to make final predictions on the test set and on the meta-features.The stacking and blending techniques are useful to improve the performance of the machine learning models. They are used to minimize the errors to get good accuracy for the given problem.Voting Voting is the easiest ensemble method in machine learning. It is mainly used for classification purposes. In this technique, the first step is to create multiple classification models using a training dataset. When the voting is applied to regression problems, the prediction is made with the average of multiple other regression models.In the case of classification there are two types of voting,Hard Voting Soft VotingThe Hard Voting ensemble involves summing up the votes for crisp class labels from other models and predicting the class with the most votes. Soft Voting ensemble involves summing up the predicted probabilities for class labels and predicting the class label with the largest sum probability.In short, for the Regression voting ensemble the predictions are the averages of contributing models, whereas for Classification voting ensemble, the predictions are the majority vote of contributing models.There are other forms of voting like “Majority Voting” and “Weighted Voting”. In the case of Majority Voting, the final output predictions are based on the number of votes it gets. If the count of votes is high, that model is taken into consideration. In some of the articles this method is also called as “Plurality Voting”.Unlike the technique of Majority voting, the weighted voting works based on the weights to increase the importance of one or more models. In the case of weighted voting, we count the prediction of the better models multiple times.ConclusionIn order to improve the performance of weak machine learning models, there is a technique called Ensembling to improve or boost the accuracy of the model. It is comprised of different techniques, helpful for solving different types of regression and classification problems.

How To Become A Data Aanalyst In 2021?

By Dipayan Ghatak

In 2020, Data Analysis has become one of the core functions in any organization. This is a highly sought-after role that has evolved immensely in the past few years. But what is Data Analysis? What do Data Analysts do? How to become a Data Analyst in 2020? What are the skills one needs to have to be a Data Analyst? There are many such questions which strike our mind when we talk about this profession.Let's walk through the answers to all the questions to ensure we have a clear picture in our mind.What is Data analytics?Information collected from different sources is used to make informed decisions for the organization, and is analyzed for some specific goals through Data Analysis. Data Analysis is not only used for research and analysis; but it helps organizations learn more about their customers, develop marketing strategies and optimize product development, just to name a few areas where it makes an impact.To be precise, there are four types of Data Analytics:Descriptive Analytics: - In this type of analytics, analysts examine the past data like monthly sales, monthly revenue, website traffic and more to find out the trend. They then draft a description or a summary of the performance of the firm or website. This type of analytics uses arithmetic operations, mean, median, max, percentage and other statistical summaries.Diagnostic Analytics:- As the name suggests, here we diagnose the data and find out the reasons behind any particular trend, issue or scenario. If a company is faced with any negative data then this type of analysis will help them to find out the main reasons/causes for the decline in the performance, against which decisions and actions can be taken.Predictive Analytics:- This type of analytics helps in predicting the future outcome by analyzing the past data and trends. It will help companies to take proactive actions for the better outcomes. Not just this, but predictive analysis also helps us forecast the sales, demand, fraud, failures and set our budgets and other resources accordingly. Prescriptive Analytics:- This type of analytics helps in determining what action the company should take next in response to the situation, to keep the business going and growing.Why do we need Data Analysts?Organizations across different fields or sectors rely on data analysis to take important decisions for the development of a new product, to forecast sales for the near future, or find out about entry into new markets or new customers to target. Data analysis is also used to analyze the business performance based on the present data and find out various inefficiencies in the organizations. Not only industries or businesses use data analysis, but it is also used by different political parties and other groups to find out about opportunities as well as challenges.What does a data analyst do?There are several functions which an analyst performs, but some of the functions may also depends on the type of business and organization. Generally, a data analyst performs the following responsibilities:Data collection from various sources like primary sources and secondary sources and arranging the data in a proper sequence.Cleaning and processing the data as per requirement. A data analyst may be required to treat missing values, clean invalid or wrong data and remove unwanted information.Using different kind of statistical tools like R, Python, SPSS or SAS, to interpret the data collected.Adjusting the data according to the upcoming trends or changes like seasonal trends and then making interpretations.Preparing a data analysis report.Identifying opportunities and threats from the analyzed data and apprising the same to the organization.Now that you know what areas a Data Analyst works on, let us move to the skills and knowledge you would require to get started in this field.What are the skills necessary to be a Data Analyst?Broadly, a data analyst needs to have two type of skills at a broader level:Technical skills - Knowledge of different technical languages and tools like R, SQL, Microsoft Excel, Tableau, Mathematical skills, statistical skills and data visualization skills. These technical skills would help an analyst actually use the data and visualize the final outcome in a form that could be beneficial for the firm. This may include tables, graphs, charts, and more. Decision making – This is extremely necessary to present the outcome and take the executives through the various changes, trends, demand, and downfall. Deep analysis is required to be able to take logical, factual and beneficial decisions for the firm. Data analysts must have the ability to think strategically and get a 360 degree view of the situation, before suggesting the way forward.After acquiring the above mentioned skills, it is very much required to keep yourself updated with the latest trends in the industry, so a mindset of continuous learning is a must.How to become a data analyst in 2021?The year 2020 changed all the definitions of a business and its processes. COVID-19 put companies across the world in a tailspin, forcing them to rethink their business strategies in order to cope with the evolving challenges thrown up by the pandemic. Some companies that were market leaders in their domain were unable to cope, and many had to even close down. The question therefore arises, in such an uncertain scenario, with challenges around every corner, is it even prudent to consider stepping into the role of a Data Analyst at this juncture? The answer to this is, “YES”. This is the best time to be a data analyst because organizations everywhere are looking for expert Analysts who can guide them in making the right decisions, helping the organization to survive through the pandemic and beyond. Data analysts can perform detailed sales forecasting, or carry out a complete market analysis to make the right predictions for future growth. Companies need to prepare smart strategies for sales and marketing, to survive and thrive in the long run.If you want to shape your career in data analytics then You must have a degree in Mathematics, Economics, Engineering, Statistics or in a field which emphasizes on statistical and analytical skills. You must know some data analytics tools or skills which are mentioned above like R, SQL, Tableau, Data Warehousing, Data visualization, Data mining and advanced Microsoft Excel. You must consider some good certifications in the above-mentioned skills. You may also consider a master’s degree in the field of data analytics.Let us now take you through the scope of Data Analysis in 2021.What is the scope of data analytics in 2021?The world is witnessing a surge in demand for data analytics services. According to a report, it is expected that there will be 250,000 new openings in the Data Analytics field in 2021, which is almost 60% higher than the demand in 2019-20. To stay ahead of the competition, organizations are employing Data Analysts and the demand for experts in the field is only set to rise. According to another report published in 2019, there were 150000 jobs which were vacant in the Data Analytics sector because of lack of available talent. This is a lucrative field, and those professionals who have expertise and experience can easily climb to the top in a short time. A report by IBM predicts that by 2021, Data Science and Analytics jobs would grow to nearly 350,000.What are the sectors in which Data Science jobs are expected to grow in India in 2021?Though the need for data analytics is growing across every sector, there are a few sectors that are more in-demand than others. These include:Aviation sector: uses data analysis for pricing and route optimization. Agriculture sector: analyses data to forecast the output and pricing. Cyber security: Global companies are adopting data engineering and data analysis for anomaly and intrusion detection. Genomics: Data analytics is used to study the sequence of genomes. It is heavily used to diagnose abnormalities and identify diseases.Conclusion If you would like to enter the field of Data Analytics, there’s no time like now! Data is useless without the right professional to analyze it. Leading companies leverage the power of analytics to improve their decision making and fuel business growth, and are always looking to employ bright and talented professionals with the capabilities they need. Opportunities are plentiful and the rewards are immense, so take the first step and start honing all the skills that can make you fulfil your dream!

How To Switch To Data Science From Your Current Career Path?

By Dipayan Ghatak

WHAT DO DATA SCIENTISTS DO?A data scientist needs to be well-versed with all aspects of a project and needs to have an in-depth knowledge of what’s happening. A data scientist’s job needs loads of exploratory data research and analysis on a daily basis with the help of various tools like Python, SQL, R, and Matlab. The life of a data scientist involves getting neck-deep into huge datasets, analysing them, processing them, learning new aspects and making novel discoveries from a business perspective.This role is an amalgamation of art and science that requires a good amount of prototyping, programming and mocking up of data to obtain novel outcomes. Once they get desired outcomes, data scientists move forward for production deployment where the customers can actually experience them. Every day, a data scientist is required to come up with new ideas, iterate them on already built products and develop something better.WHY SHOULD YOU GET INTO DATA SCIENCE?One of the most in-demand industries of the modern world is Data Science. Year on year, the increase in the total data generated by customers is huge, and has now almost touched 2.5 quintillion bytes per day. You can imagine how large that is! For any organization, customer data is of the utmost priority as with its help, they can sell their customer the products they want, by creating the advertisements they would be attracted to, providing the offers they won't reject, and in short delighting their customers every step of the way.The money factor has already been mentioned by me earlier. A Data Scientist earns about 25% more than a computer programmer. A person with a die-hard passion to work on large datasets and to draw meaningful insights can definitely begin their journey in becoming a great data scientist. WHAT ALL DO YOU NEED TO KNOW AND UNDERSTAND TO BECOME A DATA SCIENTIST?Data science skill sets are in a continuous state of fluctuation. Many people are confused with the thought that if they can gain expertise in 2 - 3 software technologies, they are well equipped to begin a career in data science and some also think that if they just learn machine learning, they can become a good data scientist. It is an undeniable fact that all these things together can make you a good data scientist but having only these skills will definitely not make you one. A good data scientist is a big data wrangler, who has the ability to apply quantitative analysis, statistics, programming and business acumen to help an enterprise grow. Solving just a data analysis problem or creating a machine learning algorithm will not make you a great enterprise data scientist. An expert in programming and machine learning who is not able to glean valuable insights to help the growth of an organization cannot be called a real Data Scientist. Data scientists work very closely with different business stakeholders to analyse where and what kind of data can actually add value to the real-world business applications. Data scientists should be able to discern the impacts of solving a data analysis problem such as what is the criticality of the problem, identifying the logical flaws in the analysis outcomes and must always ponder on the question- Does the outcome of the analysis make any sense to the business?Now the next question that arises is HOW TO GET INTO DATA SCIENCE FROM YOUR CURRENT CAREER PATH? The first and the foremost step is to understand the urgent need to change your path to Data Science, because if you have doubts in your mind then it would be hard to succeed. This does not mean that you need to quit your job, sit at home and wait for some company to hire you as a data scientist. It means that you need to understand your priority and have to work and develop the required skills to hone your knowledge in that field, so as to excel in the career path you tend to follow next.A data scientist must be able to navigate through multifaceted data issues and various statistical models, keeping the business perspective in mind. Translation of the business requirements into datasets and machine learning algorithms to obtain value from the data, are the core responsibilities of a Data Scientist. Moreover, communication plays a pivotal role in data science as well because through the entire data science process, a data scientist must be able to closely communicate with the business partners. Data scientists should work in collaboration with top level executives in the organization like marketing managers, product development managers, etc. to figure out how to support each of the departments in the company to grow with their respective data driven analysis. Data Science requires three main skills :-Statistics: To enter the field of data science, a solid foundation in statistics is a must. Professionals must be well-equipped with statistical techniques, and should know when and how to apply them to a data-driven decision-making problem. Data Visualisation: Data visualization is the heart of the data science ecosystem as it assists to present the solution and outcome to a data driven decision making problem in a better format to the clients who do not belong to data analytics background. Data visualization in data science is challenging as it requires finding answers to complex questions. Before stepping into this field, a lot of preparation in visualization needs to be done. Programming: People often ask themselves “Do I need to be a BIG time coder or an expert programmer to pursue a lucrative career in Data Science?” The answer to this is probably no. Expertise in programming skills can be an added advantage in Data Science, but it is not compulsory. Programming skills are not needed in big data applications but are rather needed to solve a data equation that is time consuming when solved manually. If a data scientist can figure out what needs to be done with the dataset, that would be enough.WHAT IS DATA IN DATA SCIENCE?Data is the essence of Data Science. Data Science revolves around big datasets but many a times, data is not of the quality that is required to take decisions. Before being ready for processing, data goes through pre-processing which is a necessary group of operations that translate raw data into a more understandable format and thus, useful for further processing. Common processes are:Collect raw data and store it on a server. This is untouched data that scientists cannot analyze straight away. This data may come from surveys, or through popular automatic data collection methods, like using cookies on a website.Class-label the observationsThis consists of arranging the data by categorizing or labelling data points to the appropriate data type such as numerical, or categorical data.Data cleansing / Data scrubbingDealing with incongruous data, like misspelled categories or missing values.Data balancingIf the data is unbalanced, for instance if the categories contain unequal numbers of observations and are not representative, applying certain data balancing methods, like extracting equal numbers of observations for the individual categories, and then processing it, fixes the issue.Data shufflingRe-arranging the data points to remove the unwanted patterns and improve predictive performance is the major task here. An example would be, if the first 1000 observations in the dataset are from the first 1000 people who have used a website; the data is not randomized due to different sampling methods used.The gist of the requirements for a Data Scientist are:Hands on with SQL is a must. It is a big challenge to understand the dicing and slicing of data without expert knowledge of various SQL concepts.Revisit Algebra and MatricesDevelop expertise in statistical learning and implement them in R or Python based on the kind of dataset.Ability to understand and implement Big Data, as the better the data, the more is the accuracy of a machine learning algorithm.Data visualization is the key to master data science as it provides the summary of the solution.WHERE SHOULD YOU LEARN DATA SCIENCE FROM?There are many institutions which offer in-depth courses on data science. You can also undertake various online courses to equip yourself with Data Science skills. As the Data Science market is growing exponentially, more professionals are leaning toward a career in this rewarding space. To explore some course options in data science, you can visit.

Data Science Foundations & Learning Path

By Dipayan Ghatak

In the age of big data processing, how to store these terabytes of data surfed over the internet was the key concern of companies until 2010. Now that the issue of storage of big data has been solved successfully by Hadoop and various other frameworks, the concern has shifted to processing these data. From website visits to online shopping, transitions from cell phones to browsing computers, every little thing we search online forms an enormous source of business industry data.The pandemic has led to an increase in data science demand as the world has shifted in pursuit of the "new normal" from offline to online. But what is Data Science? What are its salient characteristics? Where are we going to learn more about this? Let's take a look at all the fuss about data science, its courses, and the path to the future.What is Data Science?In order to discover insights and then analyse multiple structured and unstructured data, Data Science requires the use of different instruments, algorithms and principles. This is achieved using different methods and languages that we will eventually address in the alternative portion.Predictive causal analytics, prescriptive analytics and machine learning are some tools used to make decisions and predictions in data science.Predictive causal analytics: When lending your friends money, do you ever wonder if they're going to give it back to you or not? Or are you making predictions that are the same? If so, then this is exactly what casual predictive analysis does. In the future, it estimates the possibilities of a real occurrence that may or may not happen. This tool helps businesses measure the probability of such events, such as whether or not purchases made by a customer will be on time.Prescriptive analytics: Back in the 2000s, people admired flying vehicles. Today, when self-driven vehicles are already on the market, we have entered a point where we do not even need to drive a vehicle. How was this possible? If you want a model that has the intelligence to make its own choices and the ability to change it with dynamic parameters, what is needed is prescriptive analytics. This helps to make a decision based on the predictions of a computer programme. The best thing is that, the best course of action to take is advised for a certain situation.Machine learning for making predictions: Machine Learning (ML) is a computer programme framework that allows algorithms and is capable without human intervention of taking decisions and generating outputs. Known to be one of the most powerful and important technological advances in recent times, machine learning has already enabled us to conduct real-world calculations and analytics, something that would have taken years to solve through traditional computing. For example, it is possible to plan and train a fraud detection model, using the past records of fraudulent transactions. Machine learning for discovery of pattern: If you don't have the parameters you can forecast, you need to figure out the secret trends in the dataset in order to be able to make any predictions that are meaningful. Clustering, a technique in which data points are grouped together according to the similarity of their characteristics and patterns, is the most used algorithm for pattern discovery.Suppose you work in a telephone company, for instance, and you are expected to set up a network by building towers in an area. In this case, to locate the tower positions, you can use the clustering technique to ensure that all users obtain the maximum signal power.The Base For Data Science Though data scientists come from different backgrounds, have different skills and work experience, most of them should either be strong in it or have a good grip on the four main areas: Business and ManagementStatistics and Probability.B.Tech(Computer Science) Or Data Architecture. Verbal and Written Communications. Based on these foundations, we can conclude that a data scientist is a person who has the expertise to extract some useful knowledge and actionable insights from data,by managing complicated data sources and the above areas. The knowledge we receive can be used to make strategic business decisions and to make improvements necessary to achieve business objectives. This is done by the use of experience in the business domain, efficient communication and analysis of findings and the use of some or all of the related statistical techniques and methods, databases, programming languages, software packages, data infrastructure, etc.Data Science Goals and DeliverablesLet's look at the paradigms that data science has proven to succeed in. There are different fields in which data science has been extremely beneficial. Data scientists set certain targets and results to be accomplished by the data science process. Let's discuss them in brief:Prediction Classification Recommendations Pattern detection and classification Anomaly detection Recognition Actionable insights Automated processes and decision-making Scoring and ranking SegmentationOptimization Forecast SalesAll of these are intended to address specific problems and solve it.Many managers are highly intelligent people, but they may still not be well versed in all the instruments or techniques and algorithms available (e.g., statistical analysis, machine learning, artificial intelligence, etc.). Therefore, they might not be able to tell a data scientist what they want as a final deliverable, or recommend the sources, features and the right direction to get there from the data sources.Therefore an ideal data scientist must have a reasonably detailed understanding of how organisations function in general and how data from an organisation can be used to achieve top-level business objectives. With exceptional experience in the business domain, a data scientist should be able to constantly discover and recommend new data projects to help the organisation accomplish its objectives and optimise its KPIs.Data Scientists vs. Data Analysts vs. Data EngineersLike several other related positions, the role of data scientist is most frequently misunderstood. Data Analysts and Data Engineers are the two key ones, both very distinct from each other as well as from Data Science.Let us look into how they are different from one another so that we may have a clear understanding of all these different job roles and profiles.Data AnalystData analysts have many skills and responsibilities similar to a data scientist, and sometimes even have a similar educational background as well. Some of these similar skills include the ability to:Access and query (e.g., SQL) different data sources Process and clean data Summarize data Understand and use statistics and mathematical techniques Prepare data visualizations and reports Some of the distinctions, however are that computer programmers are not data analysts, nor are they accountable for mathematical modelling or machine learning, and several other measures explained above in the data science process.The various instruments used are often typically different. Data analysts typically use analytical and business intelligence software such as MS Excel, Tableau, PowerBI, QlikView, SAS, and may also use a few SAP modules. Analysts also do data mining and modelling tasks occasionally, but typically prefer to use visual tools for data science activities, such as IBM SPSS Modeler, Rapid Miner, SAS, and KNIME.Data scientists, on the other hand, usually perform the same tasks with software such as R or Python, together with some relevant libraries for the language used. Data scientists are also more accountable for teaching linear, non-linear algorithms in mathematical models.Data EngineerData scientists also use data from different sources, which are then collected, transformed, combined, and ultimately processed in a manner that is optimised for analytics, business intelligence, and modelling. On the other hand, computer engineers are responsible for the design of data and the setting up of the necessary infrastructure. They need to be competent programmers with some skills that are very similar to those necessary in a DevOps job, and with good and powerful writing skills for data query. Another main aspect of this position is database design (RDBMS, NoSQL, and NewSQL), data warehousing, and setting up a data lake. This means they need to be very familiar with many database technology and management systems available, including those associated with big data (For example, Hadoop, Redshift, Snowflake and Cassandra).The Data Scientist’s ToolboxData scientists should be proficient with such programming languages such as Python, R, SQL, Java, Julia, Apache Spark and Scala, as computer programming is a huge part. Usually, in all of these, it's not important to be an expert programmer, but Python or R, and SQL are certainly the main languages they should be familiar with.Some useful and famous data science courses which you can definitely avail to strengthen your knowledge and concepts are as follows :Data Science Specialization from JHU (Coursera) Introduction to Data Science from Metis Applied Data Science along with Python Specialization from University of Michigan (Coursera) Dataquest Statistics and Data Science Micro Masters from MIT (edX) CS109 Data Science from Harvard Python for Data Science and Machine Learning Bootcamp from UdemySome of the many courses available online related to Data Science are the courses listed above. At the end of the course, all the courses provide you with a certificate of completion. These courses will, above all other advantages, help you develop a database on data science and eventually move you to a level where you will be fully prepared to deal with some real data!ConclusionData science has become an important part of today's generation. Even the tiniest move we take on the internet leaves our digital footprint and extracts information from it. Having expertise in the processing of data science can help you go a long way. Perhaps it's not unfair to suggest that Data Science would control a large portion of our future. Data scientists have a huge positive effect and impact on the performance of a company, but sometimes they may also cause financial losses, which is one of the many reasons why it is important to employ a top-notch data scientist. However it can bring prosperity, effectiveness and sustainability to any organisation if implemented in a perfect manner.

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