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Future Proof Your Career With Data Skills

Data is everywhere, and we have all seen exponential growth in the data that is generated daily. Information must be extracted from this data to make sense of it, and we must gain insights from this information that will help us to understand repeating patterns. Analysing these patterns will help us to know more about consumers and their behaviour, hence provide services and manufacture products that will benefit both the organization as well as the consumers. This is where Data Science comes into the picture. The art of analysing the data, extracting patterns, applying algorithms, tweaking the data to suit our requirements, and more – are all parts of data science. The field has seen massive growth in the last few years and this growth may not stop for the next 5 years at least. Some people are apprehensive about the future, but the opportunities in the field of data science are improving day by day, creating new paths for people to get in and contribute their expertise/experiences. What is Data Science? As mentioned previously, data is generated in large amounts daily. This data generation happens everywhere, ranging from a small organization to multi-national companies. This kind of data is also known as 'big data', given their specific characteristics in terms of volume, type of data and speed at which the data gets generated. It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advantage (generating better profits, more customer-reach, and so on). Who is a Data Scientist? A data scientist is a person who is trained and experienced in working with data, i.e. data gathering, data cleaning, data preparation, data transformation, and data analysis. These steps will help understand the data, extract hidden patterns and put forward insights about the data. All these processes are done with the help of algorithms which are specially designed to perform a specific task. Many analyses have revealed that Data Scientist, Machine Learning Engineer, Artificial Intelligence Engineer are some of the most sought-after jobs. Not to forget the high pay that comes with it. Data science is an intricate combination of mathematics, statistics, analytics, and computer science. Mathematics & statistics are required to understand the ideas behind the algorithms and their working. On the other hand, analytics is associated with many data cleaning, transformation, preparation and analytics operations that are performed on the data with the help of computer science (programming languages). All these skills (which a data scientist possesses) will help the businesses to thrive. Data scientists are usually those who are able to find out why things work the way they do, why they don’t work as expected, what has gone wrong in the business and how it can be fixed. All these are different processes in the world of data analytics. They would also have to interact with potential stakeholders, discuss business challenges and help improve it.  What would a day in the life of a Data Scientist look like? If the general idea of stand-up meetings and sprint meetings is not taken into consideration, a day in the life of a data scientist would revolve around gathering data, understanding it, talking to relevant people about the data, asking questions about it, reiterating the requirement and the end product, and working on how it can be achieved. It looks like this: Data collection This part deals with the collection of raw data from various resources. These resources include websites, various social media platforms, people’s profiles, and so on. All this data needs to be collected and stored in a place which is easy to access while working with the data. Data cleaning This is considered as one of the most important steps in data science. This is because good data yields great results, whereas noisy, unclean, missing and redundant data yields unsatisfactory results. Once raw data has been collected, it needs to be accessed and cleaned by various methods. Redundant rows or columns have to be deleted, missing data either needs to be filled or deleted, irrelevant columns have to be eliminated, and so on. Data transformations In this step, the data (which is usually in the form of row x column) is converted into a format that is required by the algorithm to process upon. For example- a text analysis task may require data in the form of text whereas a prediction or regression problem may require data in the form of a table, i.e. rows and columns. Based on the requirement and the end product, data has to be transformed into the respective formats. Using statistics, machine learning algorithms to solve the problem and extract insights The basics of statistics are considered to be a foundation while working as a data scientist. Understanding distributions, priors probabilities, posteriors probabilities, Bayesian theorem serve as a foundation while working with the data. The data needs to be interpreted and mangled with the help of statistics. It helps understand and solve problems by helping the data scientist extract meaningful and relevant insights/patterns from data.  Up-to-date sector knowledge It is important to stay up-to-date, know the new trends, packages, frameworks, new releases and changes that occur frequently if not on a daily basis. It is important to adapt and use whatever revolutionary technology comes our way and seems to be helpful in the specific scenario. It is essential to stay on top by knowing new algorithms, techniques, data mining algorithms, and so on.  It is important to keep learning, revising your career plan and update the skills that are necessary for the current world. Updating knowledge is vital to pursue future opportunities and make sure that your career path is aligned with your personal interests.  What about the salary? Salaries for Data Scientists are on the higher end of the spectrum, with a mean salary of about £60,000. With experience and constantly upgrading the skills, the salary can go up to £100,000 too. This also depends on the organization. If it is a start-up or a new company, they might not pay as much, but as and when the company grows, the pay-out increases. On similar lines, experience increases along with skills, which would make a data scientist more valuable to the organization.  Analytics has revealed that the number of data science-related jobs will see a surge and it has also been labelled as the 'sexiest job of the 21st century’. The demand is growing steadily. Almost every organization wishes to have a machine learning wing where data scientists would be much needed.  What are the pre-requisites to becoming a Data Scientist? If it is one of the companies from FAANG – Facebook, Amazon, Apple, Netflix, Google, it is not really a requirement to have a bachelor's degree or anything. There might be certain positions that require specific qualifications, but entry-level positions don't usually have any specific requirement of a degree. These companies certainly expect the data scientists to be hands-on in one or two programming languages (object-oriented such as C++ or Java, and Python). They might also require knowing specific frameworks (TensorFlow, Keras), deep learning algorithms (Neural networks, convolutional neural networks, recurrent neural networks), NumPy, Pandas and so on.  Machine learning is a concept which data scientists will have to be familiar with. This doesn’t mean just the definitions. It involves understanding the algorithms, the mathematical working behind it, the kind of results it would provide, the kind of cases where certain algorithms can be used and how the output can be improved by tweaking certain parameters present in the algorithm.  It is also essential to understand where Machine Learning can be used, and how it plays an important role in understanding the data, and prediction as well.  It never harms to get a bachelor’s degree, a master’s degree and a PhD. All these degrees add formidable value to the knowledge already gained.  How do I start on my Data Science Journey? Any job requires a resume where the relevant skills are mentioned in the right way and format. It is important to present yourself in the right way, and also exhibit the enthusiasm of learning and being updated. An entry-level data scientist job will require the basics of object-oriented programming, Python, scientific computing packages, basics of machine learning, statistics, analytics and hands-on programming abilities. Taking up certain foundational courses, working hands-on in projects, internships and group projects also help in providing a considerable amount of experience around working with data. It is also important to have a niche for data, be able to play around with data, extract patterns, have an eye out for insights, packages that could be used, the approach and so on.  Conclusion Technology will create new jobs, but Data Science and Artificial Intelligence will be a major part of our life in the upcoming years. This means some jobs may be lost too (because many processes which seem too trivial would be automated) but think about the new jobs that would be created! Instead of worrying about the jobs that would be lost due to AI replacing their work, it is essential to foresee and adapt to it. We have seen technologies revolutionizing the current world, and all this has happened because of ‘change’, because of how people have foreseen the circumstances and have adapted to it. For example, AI will not replace a doctor. But AI can replace a doctor without AI knowledge. We need to be up-to-date with the current trends, the technologies and the new and ever-changing requirements of the real world. The focus should be on learning to work with machines, not outwork them. 

Future Proof Your Career With Data Skills

11K
Future Proof Your Career With Data Skills

Data is everywhere, and we have all seen exponential growth in the data that is generated daily. Information must be extracted from this data to make sense of it, and we must gain insights from this information that will help us to understand repeating patterns. Analysing these patterns will help us to know more about consumers and their behaviour, hence provide services and manufacture products that will benefit both the organization as well as the consumers. 

This is where Data Science comes into the picture. The art of analysing the data, extracting patterns, applying algorithms, tweaking the data to suit our requirements, and more – are all parts of data science. The field has seen massive growth in the last few years and this growth may not stop for the next 5 years at least. 

Some people are apprehensive about the future, but the opportunities in the field of data science are improving day by day, creating new paths for people to get in and contribute their expertise/experiences. 

What is DatScience? 

As mentioned previously, data is generated in large amounts daily. This data generation happens everywhere, ranging from a small organization to multi-national companies. This kind of data is also known as 'big data', given their specific characteristics in terms of volume, type of data and speed at which the data gets generated. 

It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advantage (generating better profits, more customer-reach, and so on). 

Who is a Data Scientist? 

A data scientist is a person who is trained and experienced in working with data, i.e. data gathering, data cleaning, data preparation, data transformation, and data analysis. These steps will help understand the data, extract hidden patterns and put forward insights about the data. All these processes are done with the help of algorithms which are specially designed to perform a specific task. 

Many analyses have revealed that Data Scientist, Machine Learning Engineer, Artificial Intelligence Engineer are some of the most sought-after jobs. Not to forget the high pay that comes with it. 

Data science is an intricate combination of mathematics, statistics, analytics, and computer science. Mathematics & statistics are required to understand the ideas behind the algorithms and their working. 

On the other hand, analytics is associated with many data cleaning, transformation, preparation and analytics operations that are performed on the data with the help of computer science (programming languages). 

All these skills (which a data scientist possesses) will help the businesses to thrive. 

Data scientists are usually those who are able to find out why things work the way they do, why they don’t work as expected, what has gone wrong in the business and how it can be fixed. All these are different processes in the world of data analytics. They would also have to interact with potential stakeholders, discuss business challenges and help improve it.  

What would a day in the life of a Data Scientist look like? 

If the general idea of stand-up meetings and sprint meetings is not taken into consideration, a day in the life of a data scientist would revolve around gathering data, understanding it, talking to relevant people about the data, asking questions about it, reiterating the requirement and the end product, and working on how it can be achieved. It looks like this: 

Data collection 

This part deals with the collection of raw data from various resources. These resources include websites, various social media platforms, people’s profiles, and so on. All this data needs to be collected and stored in a place which is easy to access while working with the data. 

Data cleaning 

This is considered as one of the most important steps in data science. This is because good data yields great results, whereas noisy, unclean, missing and redundant data yields unsatisfactory results. Once raw data has been collected, it needs to be accessed and cleaned by various methods. Redundant rows or columns have to be deleted, missing data either needs to be filled or deleted, irrelevant columns have to be eliminated, and so on. 

Data transformations 

In this step, the data (which is usually in the form of row x column) is converted into a format that is required by the algorithm to process upon. For example- a text analysis task may require data in the form of text whereas a prediction or regression problem may require data in the form of a table, i.e. rows and columns. Based on the requirement and the end product, data has to be transformed into the respective formats. 

Using statistics, machine learning algorithms to solve the problem and extract insights 

The basics of statistics are considered to be a foundation while working as a data scientist. Understanding distributions, priors probabilities, posteriors probabilities, Bayesian theorem serve as a foundation while working with the data. The data needs to be interpreted and mangled with the help of statistics. It helps understand and solve problems by helping the data scientist extract meaningful and relevant insights/patterns from data.  

Up-to-date sector knowledge 

It is important to stay up-to-date, know the new trends, packages, frameworks, new releases and changes that occur frequently if not on a daily basis. It is important to adapt and use whatever revolutionary technology comes our way and seems to be helpful in the specific scenario. It is essential to stay on top by knowing new algorithms, techniques, data mining algorithms, and so on.  

It is important to keep learning, revising your career plan and update the skills that are necessary for the current world. Updating knowledge is vital to pursue future opportunities and make sure that your career path is aligned with your personal interests.  

What about the salary? 

Salaries for Data Scientists are on the higher end of the spectrum, with a mean salary of about £60,000With experience and constantly upgrading the skills, the salary can go up to £100,000 too. This also depends on the organization. If it is a start-up or a new company, they might not pay as much, but as and when the company grows, the pay-out increases. On similar lines, experience increases along with skills, which would make a data scientist more valuable to the organization.  

Analytics has revealed that the number of data science-related jobs will see a surge and it has also been labelled as the 'sexiest job of the 21st century’. The demand is growing steadily. Almost every organization wishes to have a machine learning wing where data scientists would be much needed.  

What are the pre-requisites to becoming a Data Scientist? 

If it is one of the companies from FAANG – Facebook, Amazon, Apple, Netflix, Google, it is not really a requirement to have a bachelor's degree or anything. There might be certain positions that require specific qualifications, but entry-level positions don't usually have any specific requirement of a degree. These companies certainly expect the data scientists to be hands-on in one or two programming languages (object-oriented such as C++ or Java, and Python). They might also require knowing specific frameworks (TensorFlowKeras), deep learning algorithms (Neural networks, convolutional neural networks, recurrent neural networks)NumPy, Pandas and so on.  

Machine learning is a concept which data scientists will have to be familiar with. This doesn’t mean just the definitions. It involves understanding the algorithms, the mathematical working behind it, the kind of results it would provide, the kind of cases where certain algorithms can be used and how the output can be improved by tweaking certain parameters present in the algorithm.  

It is also essential to understand where Machine Learning can be used, and how it plays an important role in understanding the data, and prediction as well.  

It never harms to get a bachelor’s degree, a master’s degree and a PhD. All these degrees add formidable value to the knowledge already gained 

How do I start on my Data Science Journey? 

Any job requires a resume where the relevant skills are mentioned in the right way and format. It is important to present yourself in the right way, and also exhibit the enthusiasm of learning and being updated. An entry-level data scientist job will require the basics of object-oriented programming, Python, scientific computing packages, basics of machine learning, statistics, analytics and hands-on programming abilities. Taking up certain foundational courses, working hands-on in projects, internships and group projects also help in providing a considerable amount of experience around working with data. 

It is also important to have a niche for data, be able to play around with data, extract patterns, have an eye out for insights, packages that could be used, the approach and so on.  

Conclusion 

Technology will create new jobs, but Data Science and Artificial Intelligence will be a major part of our life in the upcoming years. This means some jobs may be lost too (because many processes which seem too trivial would be automated) but think about the new jobs that would be created! 

Instead of worrying about the jobs that would be lost due to AI replacing their work, it is essential to foresee and adapt to it. We have seen technologies revolutionizing the current world, and all this has happened because of ‘change’, because of how people have foreseen the circumstances and have adapted to it. 

For example, AI will not replace a doctor. But AI can replace a doctor without AI knowledge. 

We need to be up-to-date with the current trends, the technologies and the new and ever-changing requirements of the real world. The focus should be on learning to work with machines, not outwork them. 

Dipayan

Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.

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Activation Functions for Deep Neural Networks

The Universal Approximation Theorem Any predictive model is a mathematical function, y = f(x) that can map the features (x) to the target variable (y). The function, f(x) can be a linear function or it can be a fairly complex nonlinear function. The function, f(x) can help predict with high accuracy depending on the distribution of the data. In the case of neural networks, it would also depend on the type of network architecture that's employed. The Universal Approximation Theorem says that irrespective of what the f(x) is, a neural network model can be built that can approximately deliver the desired result. In order to build a proper neural network architecture, let us take a look at the activation functions. What are Activation Functions? Simply put, activation functions define the output of neurons given a certain set of inputs. Activation functions are mathematical functions that are added to neural network models to enable the models to learn complex patterns. An activation function takes in the output from the previous layer, passes it through the mathematical function (mostly non-linear functions) to convert it into some form, that can be considered as an input for the next computation layer. Activation functions determine the final accuracy of a network model while also contributing to the computational efficiency of building the model. Why do we need Activation Functions? In a neural network, if we add the hidden layers as the weighted sum of the inputs, this would translate into a linear function which is equivalent to a linear regression model. Image source: Neural Network ArchitectureIn the above diagram, we see the hidden layer is simply the weighted sum of the inputs from the input layer. For example, b1 = bw1 + a1w1 + a2w3 which is nothing but a linear function.Linear combination of linear functions is a linear function. So no matter whatever number of linear function we add, or increase the hidden linear layers, the output would still be linear.However in the real world, more often than not, we need to model data which is non-linear and way more complex. Adding non-linear functions allow these non-linear decision boundaries to be built into the model.Multi-layer neural network models can classify linearly inseparable classes. However, in order to do so, we need the network to be transformed to a nonlinear function. For this nonlinear transformation to happen, we would pass the weighted sum of the inputs through an activation function. These activation functions are nonlinear functions which are applied at the hidden layers. Each hidden layer can have different activation functions, though mostly all neurons in each layer will have the same activation function.Additionally, by applying non-linear activation function to the neurons it can also act as gate and selectively switch on or off a neuron.Types of Activation Functions? In this section we discuss the following: Linear Function Threshold Activation Function Bipolar Activation Function Logistic Sigmoid Function Bipolar Sigmoid Function Hyperbolic Tangent Function Rectified Linear Unit Function Swish Function (proposed by Google Brain - a deep learning artificial intelligence research team at Google) Linear Function: g(x) = xA linear function is similar to a straight line, y=mx. Irrespective of the number of hidden layers, if all the layers are linear in nature, then the final output is also simply a linear function of the input values. Hence we take a look at the other activation functions which are non-linear in nature and can help learn complex patterns. Note: This function is useful when we want to model a wide range in the regression network output.Threshold Activation Function: (sign(x) +1)/2In this case, if the input is above a certain value, the neuron is activated. It is to note that this function provides either a 1 or a 0 as the output. In effect, the step function divides the input space into two halves such that one side of the hyperplane represents class 0 and the other side of the hyperplane represents class 1. However, if we need to classify certain inputs into more than 2 categories, a Threshold-Activation function is not a suitable one. Because of its binary output nature, this function is also known as binary-step activation function.Threshold Activation FunctionDrawback:Can be used for binary classification only. It is not suited for multi class classification problems.This function does not support learning, i.e., when you fine tune the NN, you would not know if by changing the weights slightly the loss has reduced or changed at all.Bipolar Activation Function: This is similar to the threshold function that was explained above. However, this activation function will return an output of either -1 or +1 based on a threshold.Bipolar Activation FunctionLogistic Sigmoid Function: One of the most frequently used activation functions is the Logistic Sigmoid Function. Its output ranges between 0 and 1 and is plotted as an ‘S’ shaped graph.Logistic Sigmoid FunctionThis is a nonlinear function and is characterised by a small change in x that would lead to large change in y. This activation function is generally used for binary classification where the expected output is 0 or 1. This activation function provides an output between 0 and 1 and a default threshold of 0.5 is considered to convert the continuous output to 0 or 1 for classifying the observationsAnother variation of the Logistic Sigmoid function is the Bipolar Sigmoid Function. This activation function is a rescaled version of the Logistic Sigmoid Function which provides an output in the range of -1 to +1.Bipolar Logistic FunctionDrawback: Slow convergence - Gradients only in the active region enable learning. When the neurons fire in the saturation region(the top and bottom part of the S curve), the gradients are very small or close to zero. Hence the training becomes slow and leads to slow convergence.Vanishing Gradient problem -  When the neurons fire in the saturation region, i.e., if the output of the previous layer is in the saturation region, the gradients will get close to zero not enable learning, i.e., even large changes in parameter(weights) leads to very small change in the output.Hyperbolic Tangent Function: This activation function is quite similar to the sigmoid function. Its output ranges between -1 to +1. So the output is zero centred, hence makes weight initialization easier.Hyperbolic Tangent FunctionDrawback:This too suffers from the vanishing gradient problem.Slightly more expensive to computeRectified Linear Activation Function: This activation function, also known as ReLU, outputs the input if it is positive, else will return zero. That is to say, if the input is zero or less, this function will return 0 or will return the input itself. This function mostly behaves like a linear function because of which the computational simplicity is achieved.This activation function has become quite popular and is often used because of its computational efficiency compared to sigmoid and the hyperbolic tangent function that helps the model converge faster.ReLU has a better convergence than sigmoid and tanh(x) functions, as there are no saturation regions in ReLU. If the input of the previous layer is positive, it simply passes it as is and if the input is negative, it simply clips it.Another critical point to note is that while the sigmoid & the hyperbolic tangent function tries to approximate a zero value, the Rectified Linear Activation Functions can return true zero.Rectified Linear Units Activation FunctionOne disadvantage of ReLU is that when the inputs are close to zero or negative, the gradient of the function becomes zero. This causes a problem for the algorithm while performing back-propagation and in turn the model cannot converge. If the dataset is such that the input for a particular neuron is a negative  number then during backward propagation, the gradient will always be zero. Since the gradient is zero the weights for those neurons will never be updated and there will be no learning. If the weights are not updated, we would get same negative numbers for those neurons. Thus, no matter what those neurons would be dead. This is commonly termed as the “Dying” ReLU problem. Hence when using ReLU, one should keep track of the fraction of dead neurons.There are a few variations of the ReLU activation function, such as, Noisy ReLU, Leaky ReLU, Parametric ReLU and Exponential Linear Units (ELU) Leaky ReLU which is a modified version of ReLU, helps solve the “Dying” ReLU problem. It helps perform back-propagation even when the inputs are negative. Leaky ReLU, unlike ReLU, defines a small linear component of x when x is a negative value. With this change in leaky ReLU, the gradient can be of non-zero value instead of zero thus avoiding dead neurons. However, this might also bring in a challenge with Leaky ReLU when it comes to predicting negative values.  Exponential Linear Unit (ELU) is another variant of ReLU, which unlike ReLU and leaky ReLU, uses a log curve instead of a straight line to define the negative values. Swish Activation Function: Swish is a new activation function that has been proposed by Google Brain. While ReLU returns zero for negative values, Swish doesn’t return a zero for negative inputs. Swish is a self-gating technique which implies that while normal gates require multiple scalar inputs, self-gating technique requires a single input only. Swish has certain properties - Unlike ReLU, Swish is a smooth and non-monotonic function which makes it more acceptable compared to ReLU. Swish is unbounded above and bounded below.  Swish is represented as x · σ(βx), where σ(z) = (1 + exp(−z))−1 is the sigmoid function and β is a constant or a trainable parameter.  Activation functions in deep learning and the vanishing gradient descent problem Gradient based methods are used by various algorithms to train the models. Neural networks algorithm uses stochastic gradient descent method to train the model. A neural network algorithm randomly assigns weights to the layers and once the output is predicted, it calculates the prediction errors. It uses these errors to estimate a gradient that can be used to update the weights in the network. This is done in order to reduce the prediction errors. The error gradient is updated backward from the output layer to the input layer.  It is preferred to build a neural network model with a larger number of hidden layers. With more hidden layers, the neural network model can achieve enhanced capability to perform more accurately.  One problem with too many layers is that the gradient diminishes pretty fast as it moves from the output layer to the input layer, i.e. during the back propagation, in order to get the update for the weights, we multiply a lot many gradients and jacobians. If the largest singular value of these matrices is less than one, we will get very small number when we multiply these less than one numbers. If we get very small number, the gradients would diminish. When we update the weight with this gradient, the update is very low. By the time it reaches the other end backward, it is quite possible that the error might get too small to make any effect on the model performance improvement. Basically, this is a situation where some difficulty is faced while training a neural network model using gradient based methods.  This is known as the vanishing gradient descent problem. Gradient based methods might face this challenge when certain activation functions are used in the network.  In deep neural networks, various activations functions are used. However when training deep neural network models, the vanishing gradient descent problems can demonstrate unstable behavior.  Various workaround solutions have been proposed to solve this problem. The most commonly used activation function is the ReLU activation function that has proven to perform way better than any other previously existing activation functions like sigmoid or hyperbolic tangent. As mentioned above, Swish improves upon ReLU being a smooth and non-monotonic function. However, though the vanishing gradient descent problem is much less severe in Swish, it does not completely avoid the vanishing gradient descent problem. To tackle this problem, a new activation function has been proposed. “The activation function in the neural network is one of the important aspects which facilitates the deep training by introducing the nonlinearity into the learning process. However, because of zero-hard rectification, some of the existing activation functions such as ReLU and Swish miss to utilize the large negative input values and may suffer from the dying gradient problem. Thus, it is important to look for a better activation function which is free from such problems.... The proposed LiSHT activation function is an attempt to scale the non-linear Hyperbolic Tangent (Tanh) function by a linear function and tackle the dying gradient problem… A very promising performance improvement is observed on three different types of neural networks including Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network like Long-short term memory (LSTM).“   - Swalpa Kumar Roy, Suvojit Manna, et al, Jan 2019 In a paper published here, Swalpa Kumar Roy, Suvojit Manna, et al proposes a new non-parametric activation function - the Linearly Scaled Hyperbolic Tangent (LiSHT) - for Neural Networks that attempts to tackle the vanishing gradient descent problem. 
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Activation Functions for Deep Neural Networks

The Universal Approximation Theorem Any predictiv... Read More

A Peek Into the World of Data Science

Touted as the sexiest job in the 21st century, back in 2012 by Harvard Business Review, the data science world has since received a lot of attention across the entire world, cutting across industries and fields. Many people wonder what the fuss is all about. At the same time, others have been venturing into this field and have found their calling.  Eight years later, the chatter about data science and data scientists continues to garner headlines and conversations. Especially with the current pandemic, suddenly data science is on everyone’s mind. But what does data science encompass? With the current advent of technology, there are terabytes upon terabytes of data that organizations collect daily. From tracking the websites we visit - how long, how often - to what we purchase and where we go - our digital footprint is an immense source of data for a lot of businesses. Between our laptops, smartphones and our tablets - almost everything we do translates into some form of data.  On its own, this raw data will be of no use to anyone. Data science is the process that repackages the data to generate insights and answer business questions for the organization. Using domain understanding, programming and analytical skills coupled together with business sense and know-how, existing data is converted to provide actionable insights for an organization to drive business growth. The processed data is what is worth its weight in gold. By using data science, we can uncover existing insights and behavioural patterns or even predict future trends.  Here is where our highly-sought-after data scientists come in.  A data scientist is a multifaceted role in an organization. They have a wide range of knowledge as they need to marry a plethora of methods, processes and algorithms with computer science, statistics and mathematics to process the data in a format that answers the critical business questions meaningfully and with actionable insights for the organization. With these actionable data, the company can make plans that will be the most profitable to drive their business goals.  To churn out the insights and knowledge that everyone needs these days, data science has become more of a craft than a science despite its name. The data scientists need to be trained in mathematics yet have some creative and business sense to find the answers they are looking in the giant haystack of raw data. They are the ones responsible for helping to shape future business plans and goals.  It sounds like a mighty hefty job, doesn’t it? It is also why it is one of the most sought after jobs these days. The field is rapidly evolving, and keeping up with the latest developments takes a lot of dedication and time, in order to produce actionable data that the organizations can use.  The only constant through this realm of change is the data science project lifecycle. We will discuss briefly below on the critical areas of the project lifecycle. The natural tendency is to envision that it is a circular process immediately - but there will be a lot of working back and forth within some phases to ensure that the project runs smoothly.  Stage One: Business Understanding  As a child, were you one of those children that always asked why? Even when the adults would give you an answer, you followed up with a “why”? Those children will have probably grown up to be data scientists as it seems, their favourite question is: Why? By asking the why - they will get to know the problem that needs to be solved and the critical question will emerge. Once there is a clear understanding of the business problem and question, then the work can begin. Data scientists want to ensure that the insights that come from this question are supported by data and will allow the business to achieve the desired results. Therefore, the foundation stone to any data science project is in understanding the business.  Stage Two: Data Understanding  Once the problem and question have been confirmed, you need to start laying out the objectives of this project by determining the required variables to be predicted. You must know what you need from the data and what the data should address. You must collate all the information and data, which can be reasonably difficult. An agreement over the sources and the requirements of the data characteristics needs to be reached before moving forward.  Through this process, an efficient and insightful understanding is required of how the data can and will be used for the project. This operational management of the data is vital, as the data that is sourced at this stage will define the project and how effective the solutions will be in the end.  Stage Three: Data Preparation  It has been said quite often that a bulk of a data scientist’s time is spent in preparing the data for use. In this report from CrowdFlower in 2016, the percentage of time spent on cleaning and organizing data is pegged at 60%. That is more than half their day!  Since data comes in various forms, and from a multitude of sources, there will be no standardization or consistency throughout the data. Raw data needs to be managed and prepared - with all the incomplete values and attributes fixed, and all deconflicting values in the data eliminated. This process requires human intervention as you must be able to discern which data values are required to reach your end goal. If the data is not prepared according to the business understanding, the final result might not be suitable to address the issue.  Stage Four: Modeling Once the tedious process of preparation is over, it is time to get the results that will be required for this project lifecycle. There are various types of techniques that can be used, ranging from decision-tree building to neural network generation. You must decide which would be the best technique based on the question that needs to be answered. If required, multiple modeling techniques can be used; where each task must be performed individually. Generally, modeling techniques are applied more than once (per process), and there will be more than one technique used per project.  With each technique, parameters must be set based on specific criteria. You, as the data scientist, must apply your knowledge to judge the success of the modeling and rank the models used based on the results; according to pre-set criteria. Stage Five: Evaluation Once the results are churned out and extracted, we then need to refer back to the business query that we talked about in Stage One and decide if it answers the question raised; and if the model and data meet the objectives that the data science project has set out to address. The evaluation also can unveil other results that are not related to the business question but are good points for future direction or challenges that the organization might face. These results should be tabled for discussion and used for new data science projects. Final Stage: Deployment  This is almost the finishing line!  Now with the evaluated results, the team would need to sit down and have an in-depth discussion on what the data shows and what the business needs to do based on the data. The project team should come up with a suitable plan for deployment to address the issue. The deployment will still need to be monitored and assessed along the way to ensure that the project will be a successful one; backed by data.  The assessment would normally restart the project lifecycle; bringing you full circle.  Data is everywhere  In this day and age, we are surrounded by a multitude of data science applications as it crosses all industries. We will focus on these five industries, where data science is making waves. Banking & Finance  Financial institutions were the earliest adopters of data analytics, and they are all about data! From using data for fraud or anomaly detection in their banking transactions to risk analytics and algorithmic trading - one will find data plays a key role in all levels of a financial institution.  Risk analytics is one of the key areas where data science is used; as financial institutions depend on it to make strategic decisions for the financial health of the business. They need to assess each risk to manage and optimize their cost.  Logistics & Transportation  The world of logistics is a complex one. In a production line, raw materials sometimes come from all over the world to create a single product. A delay of any of the parts will affect the production line, and the output of stock will be affected drastically. If logistical delays can be predicted, the company can adjust quickly to another alternative to ensure that there will be no gap in the supply chain, ensuring that the production line will function at optimum efficiency.  Healthcare  2020 has been an interesting one. It has been a battle of a lifetime for many of us. Months have passed, and yet the virus still rages on to wreak havoc on lives and economies. Many countries have turned to data science applications to help with their fight against COVID-19. With so much data generated daily, people and governments need to know various things such as:  Epidemiological clusters so people can be quarantined to stop the spread of the virus tracking of symptoms over thousands of patients to understand how the virus transmits and mutates to find vaccines and  solutions to mitigate transmission. Manufacturing  In this field, millions can be on the line each day as there are so many moving parts that can cause delays, production issues, etc. Data science is primarily used to boost production rates, reduce cost (workforce or energy), predict maintenance and reduce risks on the production floor.  This allows the manufacturer to make plans to ensure that the production line is always operating at the optimum level, providing the best output at any given time.  Retail (Brick & Mortar, Online)  Have you ever wondered why some products in a shop are placed next to each other or how discounts on items work? All those are based on data science.  The retailers track people’s shopping routes, purchases and basket matching to work out details like where products should be placed; or what should go on sale and when to drive up the sales for an item. And that is just for the instore purchases.  Online data tracks what you are buying and suggests what you might want to buy next based on past purchase histories; or even tells you what you might want to add to your cart. That’s how your online supermarket suggests you buy bread if you have a jar of peanut butter already in your cart.  As a data scientist, you must always remember that the power in the data. You need to understand how the data can be used to find the desired results for your organization. The right questions must be asked, and it has become more of an art than a science. Image Source: Data science Life Cycle
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A Peek Into the World of Data Science

Touted as the sexiest job in the 21st century, ba... Read More

Top Job Roles With Their Salary Data in the World of Data Science for 2020–2021

Data Science requires the expertise of professionals who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. Data science is woven into the fabric of our daily lives in myriad ways that we may not even be aware of; starting from the online purchases we make, our social media feeds, the music we listen to or even the movie recommendations that we are shown online.  For several years in a row, the job of a data scientist has been hailed as the “hottest job of the 21st century”. Data scientists are among the highest paid resources in the IT industry. According to Glassdoor, the average data scientist’s salary is $113,436. With the growth of data, the demand for data science job roles in companies has been rising at an accelerated pace.  How Data Science is a powerful career choice The landscape of a data science job is promising and full of opportunities spanning different industries. The nature of the job allows an individual to take on flexible remote jobs and also to be self-employed.  The field of data science has grown exponentially in a very short time, as companies have come to realize the importance of gathering huge volumes of data from websites, devices, social media platforms and other sources, and using them for business benefits. Once the data is made available, data scientists use their analytical skills, evaluate data and extract valuable information that allows organizations to enhance their innovations. A data scientist is responsible for collecting, cleansing, modifying and analyzing data into meaningful insights. In the first phase of their career, a data scientist generally works as a statistician or data analyst. Over many years of experience, they evolve to be data scientists.  The ambit of data has been increasing rapidly which has urged companies to actively recruit data scientists to harness and leverage insights from the huge quantities of valuable data available, enabling efficiency in processes and operations and driving sales and growth.  In the future, data may also emerge as the turning point of the world economy. So, pursuing a career in data science would be very useful for a computer enthusiast, not only because it pays well but also since it is the new trend in IT. According to the Bureau of Labor Statistics (BLS), jobs for computer and information research scientists, as well as data scientists are expected to grow by 15 percent by the year 2028. Who is a Data Scientist & What Do They Do? Data Scientists are people with integral analytical data expertise together with complex problem-solving skills, besides the curiosity to explore a wide range of emerging issues.  They are considered to be the best of both the sectors – IT and business, which makes them extremely skilled individuals whose job roles straddle the worlds of computer science, statistics, and trend analysis. Because of this surging demand for data identification and analysis in various tech fields like AI, Machine Learning, and Data Science, the salary of a data scientist is one of the highest in the world. Requisite skills for a data scientist Before we see the different types of jobs in the data analytics field, we must be aware of the prerequisite skills that make up the foundation of a data scientist: Understanding of data – As the name suggests, Data Science is all about data. You need to understand the language of data and the most important question you must ask yourself is whether you love working with data and crunching numbers. And if your answer is “yes”, then you’re on the right track. Understanding of algorithms or logic – Algorithms are a set of instructions that are given to a computer to perform a particular task. All Machine Learning models are based on algorithms, so it is quite an essential prerequisite for a would-be data scientist to understand the logic behind it.  Understanding of programming – To be an expert in data science, you do not need to be an expert coder. However, you should have the foundational programming knowledge which includes variables, constants, data types, conditional statements, IO functions, client/server, Database, API, hosting, etc. If you feel comfortable working with these and you have your coding skills sorted, then you’re good to go. Understanding of Statistics – Statistics is one of the most significant areas in the field of Data Science. You should be well aware of terminologies such as mean, median, mode, standard deviation, distribution, probability, Bayes’ theorem, and different Statistical tests like hypothesis testing, chi-square, ANOVA, etc. Understanding of Business domain:  If you do not have an in-depth working knowledge of the business domain, it will not really prove to be an obstacle in your journey of being a data scientist. However, if you have the primitive understanding of the specific business area you are working for, it will be an added advantage that can take you ahead. Apart from all the above factors, you need to have good communication skills which will help the entire team to get on the same page and work well together.Data Science Job Roles  Data science experts are in demand in almost every job sector, and are not confined to the IT industry alone.  Let us look at some major job roles, associated responsibilities , and the salary range: 1. Data ScientistsA Data Scientist’s job is as exciting as it is rewarding. With the help of Machine Learning, they handle raw data and analyze it with various algorithms such as regression, clustering, classification, and so on. They are able to arrive at insights that are essential for predicting and addressing complex business problems.  Responsibilities of Data Scientists The responsibilities of Data Scientists are outlined below: Collecting huge amounts of organized and unorganized data and converting them into useful insights. Using analytical skills like text analytics, machine learning, and deep learning to identify potential solutions which will help in the growth of organizations. Following a data-driven approach to solve complex problems.  Enhancing data accuracy and efficiency by cleansing and validating data. Using data visualization to communicate significant observations to the organization’s stakeholders. Data Scientists Salary Range According to Glassdoor, the average Data Scientist salary is $113,436 per annum. The median salary of an entry-level professional can be around $95,000 per annum. However, early level data scientists with 1 to 4 years' experience can get around $128,750 per annum while the median salary for those with more experience ranging around 5 to 9 years  can rise to an average of $165,000 per annum. 2. Data Engineers  A Data Engineer is the one who is responsible for building a specific software infrastructure for data scientists to work. They need to have in-depth knowledge of technologies like Hadoop and Big Data such as MapReduce, Hive, and SQL. Half of the work of Data Engineers is Data Wrangling and it is advantageous if they have a software engineering background. Responsibilities of Data Engineers  The responsibilities of Data Engineers are described below: Collecting data from different sources and then consolidating and cleansing it. Developing essential software for extracting, transforming, and loading data using SQL, AWS, and Big Data. Building data pipelines using machine learning algorithms and statistical techniques. Developing innovative ways to enhance data efficiency and quality. Developing, testing and maintaining data architecture. Required Skills for Data Engineers  There are certain skill sets that data engineers need to have: Strong skills in analytics to manage and work with massive unorganized datasets. Powerful programming skills in trending languages like Python, Java, C++, Ruby, etc. Strong knowledge of database software like SQL and experience in relational databases. Managerial and organizational skills along with fluency in various databases.  Data Engineers’ Salary Range According to Glassdoor, the average salary of a Data Engineer is $102,864 in the USA. Reputed companies like Amazon, Airbnb, Spotify, Netflix, IBM value and pay high salaries to data engineers. Entry-level data and mid-range data engineers get an average salary between $110,000 and $137,770 per annum. However, with experience, a data engineer can get up to $155,000 in a year. 3. Data Analyst As the name suggests, the job of a Data Analyst is to analyze data. A data analyst collects, processes, and executes statistical data analyses which help business users to develop meaningful insights. This process requires creating systems using programming languages like Python, R or SAS. Companies ranging from IT, healthcare, automobile, finance, insurance employ Data Analysts to run their businesses efficiently.  Responsibilities of Data Analysts  The responsibilities of Data Analysts are described below: Identifying correlations and gathering valuable patterns through data mining and analyzing data. Working with customer-centric algorithms and modifying them to suit individual customer demands. Solving certain business problems by mapping data from numerous sources and tracing them. Creating customized models for customer-centric market strategies, customer tastes, and preferences. Conducting consumer data research and analytics by deploying statistical analysis. Data Analyst Salary Range According to Glassdoor, the national average salary of a Data Analyst is $62,453 in the United States. The salaries of an entry-level data analyst start at  $34,5000 per year or $2875 per month.  Glassdoor states that a junior data analyst earns around $70,000 per year and experienced senior data analysts can expect to be paid around $107,000 per year which is roughly $8916 per month. Key Reasons to Become a Data Scientist Becoming a Data Scientist is a dream for many data enthusiasts. There are some basic reasons for this: 1. Highly in-demand field The job of Data Science is hailed as one of the most sought after jobs for 2020 and according to an estimate, it is predicted that this field would generate around 11.5 million jobs by the year 2026. The demand for expertise in data science is increasing while the supply is too low.  This shortage of qualified data scientists has escalated their demand in the market. A survey by the MIT Sloan Management Review indicates that 43 percent of companies report that a major challenge to their growth has been a lack of data analytic skills. 2. Highly Paid & Diverse Roles Since data analytics form the central part of decision-making, companies are willing to hire larger numbers of data scientists who can help them to make the right decisions that will boost business growth. Since it is a less saturated area with a mid-level supply of talents, various opportunities have emerged that require diverse skill sets. According to Glassdoor, in the year 2016,  data science was the highest-paid field across industries. 3. Evolving workplace environments With the arrival of technologies like Artificial Intelligence and Robotics which fall under the umbrella of data science, a vast majority of manual tasks have been replaced with automation.  Machine Learning has made it possible to train machines to perform repetitive tasks , freeing up humans to focus on critical problems that need their attention. Many new and exciting technologies have emerged within this field such as Blockchain, Edge Computing, Serverless Computing, and others.  4. Improving product standards The rigorous use of Machine Learning algorithms for regression, classification recommendation problems like decision trees, random forest, neural networks, naive Bayes etc has boosted the customer experiences that companies desire to have. One of the best examples of such development is the E-commerce sites that use intelligent Recommendation Systems to refer products and provide customer-centric insights depending upon their past purchases. Data Scientists serve as a trusted adviser to such companies by identifying the preferred target audience and handling marketing strategies. 5. Helping the world In today’s world, almost everything revolves around data. Data Scientists extract hidden information from massive lumps of data which helps in decision making across industries ranging from finance and healthcare to manufacturing, pharma and engineering . Organizations are equipped with data driven insights that boost productivity and enhance growth, even as they optimize resources and mitigate potential risks. Data Science catalyzes innovation and research, bringing positive changes across the world we live in. Factors Affecting a Data Scientist’s Salary The salaries of Data Scientists can depend upon several factors. Let us study them one by one and understand their significance: Data Scientist Salary by Location The number of job opportunities and the national data scientist salary for data innovators is the highest in Switzerland in the year 2020, followed by the Netherlands and United Kingdom. However, since Silicon Valley in the United States is the hub of new technological innovations, it is considered to generate the most jobs for startups in the world, followed by Bangalore in India. A data scientist’s salary in Silicon Valley or Bangalore is likely to be higher than in other countries. Below are the highest paying countries for data scientist roles along with their average annual data science salary: Switzerland$115,475Netherlands$68,880Germany$64,024United Kingdom$59,781Spain$30,050Italy$37,785Data Scientist Salary by ExperienceA career in the field of data science is very appealing to young IT professionals. Starting salaries are very lucrative, and there is incremental growth in salary  with  experience. Salaries of a data scientist depend on the expertise, as well as the years of experience: Entry-level data scientist salary – The median entry-level salary for a data scientist is around $95,000 per year which is quite high. Mid-level data scientist salary –   The median salary for a mid-level data scientist having experience of around 1 - 4 years is $128,750 per year. If the data scientist is in a managerial position, the average salary rises upto $185,000 per year. Experienced data scientist salary –  The median salary for an experienced data scientist having experience of around 5 - 9 years is $128,750 per year whereas the median salary of an experienced manager is much higher; around $250,000 per year. Data Scientist Salary by Skills There are some core competencies that will help you to shine in your career as a Data Scientist, and if you want to get the edge over your peers you should consider polishing up these skills: Python is the most crucial and coveted skill which data scientists must be familiar with, followed by R. The average salary in the US for  Python programmers is $120,365 per annum. If you are well versed in both Data Science and Big Data, instead of just one among them, your salary is likely to increase by at least 25 percent . The users of innovative technology like the Statistical Analytical System get a salary of around $77,842. On the other hand, users of software analysis software like SPSS have a pay scale of  $61,452 per year. Machine Learning Engineers on the average earn around $111,855 per year. However, with more experience in Machine Learning along with knowledge in Python, you can earn around $146,085 per annum. A Data Scientist with domain knowledge of Artificial Intelligence can earn an annual salary between $100,000 to $150,000. Extra skills in programming and innovative technologies have always been a value-add that can enhance your employability. Pick skills that are in-demand to see your career graph soar. Data Scientist Salary by Companies Some of the highest paying companies in the field of Data Science are tech giants like Facebook, Amazon, Apple, and service companies like McGuireWoods, Netflix or Airbnb.  Below is a list of top companies with the highest paying salaries: McGuireWoods$165,114Amazon$164,114Airbnb$154,879Netflix$147,617 Apple$144,490Twitter$144,341Walmart$144,198Facebook$143,189eBay$143,005Salaries of Other Related Roles Various other job roles associated with Data Science are also equally exciting and rewarding. Let us look at some of them and their salaries: Machine Learning Engineer$114,826Machine Learning Scientist$114,121Applications Architect$113,757Enterprise Architect$110,663Data Architect$108,278Infrastructure Architect$107,309Business Intelligence Developer$81,514Statistician$76,884Conclusion Let us look at what we have learned in this article so far: What is Data Science? The job of a Data Scientist Pre-requisite skills for a Data Scientist Different job roles Key reasons for becoming a Data Scientist Salary depending upon different factors Salary of other related roles The field of Data Science is ripe in terms of opportunities for Data Scientists, Data Engineers, and Data Analysts. The figures mentioned in this article are not set in stone and may vary depending upon the skills you possess, experience you have and various other factors. With more experience and skills, your salary is bound to increase by a certain percentage every year. Data science is a field that will revolutionize the world in the coming years and you can have a share of this very lucrative pie with the right education qualifications, skills, experience and training.  
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Top Job Roles With Their Salary Data in the World ...

Data Science requires the expertise of professio... Read More