#data_science

Engineers can surely benefit significantly by learning data science and AI due to increased job opportunities, solving complex problems, and also the ability to enhance their problem-solving skills and contribution to innovation.

We all know that data science and AI offer a powerful combination of skills that can be applied across a vast engineering disciplines to improve efficiency, and develop new solutions. In this blog we will discuss about data science for engineering students along with AI skills for engineers.

Below is a detailed look at why engineers should consider learning data science and AI:

The Job Market is Booming

Data science and AI are among the fastest-growing fields, and the demand for professionals with these skills is expected to continue to rise. As perthe study, the number of job postings for data scientists has increased by almost 75% over the past five years, and the demand for AI professionals is growing even faster. That is exactly why data science for engineering students is becoming important.

High Salaries

You’re an engineer, and opting for Data Science and AI will give you a higher salary option. There is no denying that Data Science and AI skills are driving salaries higher, making these careers some of the most lucrative ones in the tech industry. Engineers who take on Data Science and AI can earn salaries that are very high.

Solve Real-World Problems

Engineers can learn Data Science and AI to solve some of the world’s most pressing problems, which range from healthcare outcomes to reducing crime, and also improving the traffic flow in cities. By learning these very skills, you will have the chance to make a real impact on the world and people’s lives.

Cutting-Edge Technology

Are you an engineer, then you should be aware that Data Science and AI are one of the most cutting edge technologies of the time, and they are contantly changing the way we live and work. Data science for engineering students is becoming important. if they learn these skills, then they will be at the forefront of technological advancement.

Versatility

Today, we are well aware that data science and AI can be applied in a wide range of industries, from healthcare to finance, retail and beyond. This form of versatility makes the skills very valuable, especially for engineers.

In — Demand Skills

Data Science and AI skills for engineers are becoming more and more in demand. Whether you’re looking to start a new career or even advance in your current field, these skills will give you a competitive edge in the job market — especially if you’re an engineer.

Interdisciplinary

We know that Data science and AI are interdisciplinary fields, so, as an engineer, one can gain a deep understanding by developing these skills, from mathematics and statistics to programming and computer knowledge. By learning these skills, one will have the opportunity to develop a broad and diverse skill that can be applied in many different contexts.

Exciting Work

In Data science and AI the work is continuously changing, and so it’s a very exciting field. If you’re an engineer, then you will always be working on large scale data analysis project, or even developing cutting-edge AI systems. Here, one will never get bored.

Sentimental Analysis

Regression Analysis

Time Series Analysis

Cluster Analysis

Predictive Analysis

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Machine Language is extensively applied across industries and sectors. Various applications of machine learning are:

Prediction

ECommerce - display products based on customer preference based on past purchases

Automatic Language translation: translate text from one language to another.

Speech Recognition: translation of spoken words into text.

Image Recognition: recognize image with image segmentation techniques.

Financial sector : detect fraudelent transactions, optimization

Medical Diagnosis: detect diseases early

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Deep Learning is a subset of Machine wherein we are training neural network models with datasets varying with complexity. We can identify patterns from our inputs which can be used to make predictions. Also, we can re-use the trained data derived from the model in other similar type of application which is known as Transfer Learning (TL).

Popular Models for doing Image Classification

MobileNetV2

ResNet50 by Microsoft Research

InceptionV3 by Google Brain team

DenseNet121 by Facebook AI Research

Smaller the algorithm, the faster the prediction time.

Use Cases:

Automatic speech recognition Image recognition Drug discovery and toxicology Customer relationship management Virtual Assistants (like Alexa, Siri, and Google Assistant). Self Driving Cars. News Aggregation and Fraud News Detection. Natural Language Processing. Entertainment Healthcare

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Regression Analysis is a set of statistical processes, that are used for estimating the relationship between dependent variables and multiple independent variables. For eg, predicting the resale value of our house, our dependent variable is the price of our house ie the house price is the outcome you are trying to predict. The multiple indedendent variables could be the number of bedrooms in the house, sitting room, dinning room, kitchen, bathroom, car parking etc. We may have ten different houses with ten different type of bed rooms, kitchens, siitting rooms having different prices ranges depending on the size and area, place and location etc. Hence, we might require to create a model to take these information and predict the value of the house. Here, we are using Neural Networks for solving Regression Problems. We need to give the inputs and outputs to the machine learning algorithm, and the machine learning will use the algorithms at its disposal, and understanding the relationship between the inputs and outputs. In the case of inputs ie if you have 4 bedrooms in your home, the inputs have to be given in numerical encoding form such as [0 0 0 0 1 0] which are applicable for all cases. These data are sent through a neural network and the representational output is converted into a human readable form.

Anatomy of Neural Networks:

Input Layer : Data goes into Input Layer.

Hidden Layer(s) : Learns pattern in data.(We can have multiple layers here depending on complexity). We can have very deep neural networks such as resnet152 having 152 different layers which is a common computer vision algorithm. Hence, we can have how many layers as we want.

Output Layer: Learned Representation or prediction probabilities.

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In machine learning, there are three parts to a modelling process viz.

Choosing and training a model (Training Data) (major chunk on 70%)

Validating (or tuning) the model. (about 15%)

Testing the model (Model Comparision) (about 15%)

You need to determine what kind of algorithm to use for various kind of problems. This is because some algorithms works better than others on certain kinds of data. For instance, if you are working with Structured Data, then decision trees algorithms like random forests, K-Nearest Neighbors (KNN) etc seems good. if you are working with Unstructured Data, then Deep Learning, Neural Networks, NLP etc seems good.

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We are using machines to predict results based on incoming data. Basically, we have three types of ML: a) Supervised Learning b) Unsupervised Learning c) Reinforcement Learning

a) In Supervised Learning (having data with labels), the data we received already has categories. eg csv or excel files, with rows and columns having well-labeled data. In supervised learning scenario, we generally use a classification or regression model. In classification model, we can decide whether a fruit is an apple or mango based on input labels. In regression model, based on inputs, we can predict the outcome. eg., we can select candidates in interview based on inputs labels like qualification, skills, experience etc provided in a resume.

b) In Unsupervised Learning, sometimes we get data that doesn’t have labels or isn’t classified, allowing the model to act without any guidance, which is called unsupervised learning. Here the task of the model is to group together unsorted data according to similarities, patterns, difference etc without any prior training. We can discover inherent groupings in the data, such as grouping customers on the basis of historic purchasing behavior. We can use a clustered Learning model and and Association Rule Learning model. In clustering model, we are grouping customer by their purchasing behavior whereas Association Rule Learning model, we make an association such that we make an inference such that a customer who purchases Product A is most likely to purchase Product B as well and group them together.

c)Reinforced Learning is applicable for skill acquisition or Real time Learning eg learning a game by playing it hundreds of times till it achieves the skill level. it learns the art through trial and error.

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Machines in general are very quick in performing tasks. We can control these machines ie., we give instructions and make them do tasks for us which is what programming is all about. But Lower level tasks can best be left to computers while higher level tasks can be done with humans. For eg., if we tell a computer to identify a cat from other animals like dogs, can it be programmed ? Technically, we can say it has fur, has whiskers, makes a 'mwewwwwwwwww' noise etc. Immediately the computer comes back and asks us "What is mwew….or what is whisker or fur ? Therefore it becomes really harder to explain to the machines what to do.

But, there is a new idea of machine learning that has many applications with it. Examples of machine learning are self driving cars, robots, vision processing, language processing, recommendation engines, translation services, etc. The goal of machine learning is to make machines act more and more like humans. In other words, it is the way of getting computers to act without being explicitly programmed

Artificial Intelligenge (AI) simply means intelligence exhibited by machines, and Machine Learning is the subset of AI. We have something called Narrow AI, means it is really good at doing a particular task very well, better than humans, BUT, unlike humans who are good at handling multiple tasks, which is called General AI. it will take the computers many more years to reach that level. For eg detecting and diagonising human diseases eg heart disease or predicting if the patient will suffer from diabetics a few years from now. We might have heared robots performing complicated surgeries. Another example is a Chess computer named DeepBlue, that defeated Gary Kasprov, the world champion a few decades ago. These computers can do single task really well.

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