The chances of partiality, in the process of data analysis, are extreme and it can vary from how a question is hypothesized and explored to how the data is sampled and organized. Bias can be introduced at any stage from defining and capturing the data set to run the analytics or AI or ML system. Hariharan Kolam, CEO, and founder of Findem, a people intelligence company stated in an interview, “Avoiding bias starts by recognizing that data bias exists, both in the data itself and in the people analyzing or using it,” Actually it is kind of impossible to be completely unbiased and biasedness is an existing element of human nature.

The Human Catalyst

Bias in data analysis can come from human sources because they use unrepresentative data sets, leading questions in surveys, and biased reporting and measurements. Often bias goes unnoticed until some decision is made based on the data, such as building a predictive model that turns out to be wrong. Although data scientists can never completely eliminate bias in data analysis, they can take countermeasures to look for it and mitigate issues in practice.

The Social Catalyst

Bias is also a moving target as societal definitions of fairness evolve. Reuters has reported an instance when the International Baccalaureate program had to cancel its annual exams for high school students in May due to COVID-19. Instead of using exams to grade students, the IB program used an algorithm to assign grades that were substantially lower than many students and their teachers expected.

Biasedness from Existing Data

Amazon’s previous recruiting tools showed preference toward men, who were more representative of their existing staff. The algorithms didn’t explicitly know or look at the gender of applicants, but they ended up being biased by other things they looked at that were indirectly linked to gender, such as sports, social activities, and adjectives used to describe accomplishments. In essence, the AI was picking up on these subtle differences and trying to find recruits that matched what they internally identified as successful.

Under-representing populations

Another big source of bias in data analysis can occur when certain populations are under-represented in the data. This kind of bias has had a tragic impact in medicine by failing to highlight important differences in heart disease symptoms between men and women, said Carlos Melendez, COO, and co-founder of Wovenware, a Puerto Rico-based nearshore services provider. Bias shows up in the form of gender, racial or economic status differences. It appears when data that trains algorithms do not account for the many factors that go into decision-making.

Cognitive biases

Cognitive bias leads to statistical bias, such as sampling or selection bias. Often analysis is conducted on available data or found in data that is stitched together instead of carefully constructed data sets. Both the original collection of the data and an analyst’s choice of what data to include or exclude creates sample bias. Selection bias occurs when the sample data that is gathered isn’t representative of the true future population of cases that the model will see. In times like this, it’s useful to move from static facts to event-based data sources that allow data to update over time to more accurately reflect the world we live in. This can include moving to dynamic dashboards and machine learning models that can be monitored and measured over time.

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The following are the most important deep learning algorithms that programmers should know about in 2021.

Deep learning algorithms train machines and it uses artificial neural networks to perform sophisticated computations on large amounts of data. It is a type of machine learning that works based on the structure-function of the human brain. While deep learning algorithms feature self-learning representations, they depend upon ANNs that mirror the way the brain computes information.

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Convolutional Neural Network

CNN’s, also known as ConvNets, consist of multiple layers and are mainly used for image processing and object detection. Yann LeCun developed the first CNN in 1988 when it was called LeNet. It was used for recognizing characters like ZIP codes and digits. CNN’s are widely used to identify satellite images, process medical images, forecast time series, and detect anomalies

Long Short Term Memory Networks

LSTMs are a type of Recurrent Neural Network (RNN) that can learn and memorize long-term dependencies. Recalling past information for long periods is the default behavior. LSTMs retain information over time. They are useful in time-series prediction because they remember previous inputs. LSTMs have a chain-like structure where four interacting layers communicate uniquely. Besides time-series predictions, LSTMs are typically used for speech recognition, music composition, and pharmaceutical development.

Recurrent Neural Networks

RNNs have connections that form directed cycles, which allow the outputs from the LSTM to be fed as inputs to the current phase. The output from the LSTM becomes an input to the current phase and can memorize previous inputs due to its internal memory. RNNs are commonly used for image captioning, time-series analysis, natural-language processing, handwriting recognition, and machine translation.

Generative Adversarial Networks

GANs are generative deep learning algorithms that create new data instances that resemble the training data. GAN has two components: a generator, which learns to generate fake data, and a discriminator, which learns from that false information. The usage of GANs has increased over some time. They can be used to improve astronomical images and simulate gravitational lensing for dark-matter research. Video game developers use GANs to upscale low-resolution, 2D textures in old video games by recreating them in higher resolutions via image training.

Radial Basis Function Network

RBFNs are special types of feedforward neural networks that use radial basis functions as activation functions. They have an input layer, a hidden layer, and an output layer and are mostly used for classification, regression, and time-series prediction.

Multilayer Perceptions

MLPs are an excellent place to start learning about deep learning technology. MLPs belong to the class of feedforward neural networks with multiple layers of perceptrons that have activation functions. MLPs consist of an input layer and an output layer that is fully connected. They have the same number of input and output layers but may have multiple hidden layers and can be used to build speech recognition, image recognition, and machine-translation software.

Self Organizing Maps

Professor Teuvo Kohonen invented SOMs, which enable data visualization to reduce the dimensions of data through self-organizing artificial neural networks. Data visualization attempts to solve the problem that humans cannot easily visualize high-dimensional data. SOMs are created to help users understand this high-dimensional information.

Deep Belief Network

DBNs are generative models that consist of multiple layers of stochastic, latent variables. The latent variables have binary values and are often called hidden units. DBNs are a stack of Boltzmann Machines with connections between the layers, and each RBM layer communicates with both the previous and subsequent layers. Deep Belief Networks (DBNs) are used for image recognition, video recognition, and motion-capture data.

Restricted Boltzmann Machine

Developed by Geoffrey Hinton, RBMs are stochastic neural networks that can learn from a probability distribution over a set of inputs. This deep learning algorithm is used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. RBMs constitute the building blocks of DBNs.

Autoencoders

Autoencoders are a specific type of feedforward neural network in which the input and output are identical. Geoffrey Hinton designed autoencoders in the 1980s to solve unsupervised learning problems. They are trained neural networks that replicate the data from the input layer to the output layer. Autoencoders are used for purposes such as pharmaceutical discovery, popularity prediction, and image processing.

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HIGHLIGHTS

• By offering personalisation, customisation, and enabling the use of virtual assistants, Machine Learning has become increasingly relevant for students more than before.
• ML can’t stay behind in the race considering the various benefits it offers and the wide scope it has.
• Machine Learning helps to upscale the e-learning process by upgrading the content and tailoring it as per the present requirement.

The education industry is one of the most thriving sectors in India. While the pandemic brought with it various challenges, it also helped in digital adoption across all businesses. This trend was especially witnessed in the education sector. The teachers and students had to cope up with the changing times and had to move the learning sessions to the online medium.

To ensure that the educational firms and the ed-tech platforms extend the best of the knowledgeable sessions to their students, they need to be armed with state-of-the-art technology. The key technological advancement that has brought about a transformation in learning for the students is Machine Learning. Having this technology in place enables efficient and swift processing of big data thereby extending superior quality education to the students.

Importance of machine learning in educational firms and ed-tech platforms

An extension of Artificial Intelligence (AI), Machine Learning (ML) provides the ability to the IT systems to interpret data at their end and self-educate basis the experience gained. Its primary objective is to grab hold of the required data, analyze it and accordingly create a problem and solution algorithm.

The best bit about this technology is that it does not require the assistance of humans or external coding. Every aspect of the process is covered by the machines only. A prominent example of this is when IT professionals communicate directly with their clients and provide them necessary solutions.

While machine learning is widely used in IT and e-commerce firms, it has acquired an important place in the education sector as well. It has now become the need of the hour rather than being a luxury. The most common ways in which we integrate machine learning with education are via learning analytics and educational data mining.

Siri, the virtual assistant is another soon to become essential part of the e-learning process.

As reported by Technavio, the global market share of online courses based on machine learning technology is expected to grow at a CAGR higher than 16% by 2021. This growth will be driven primarily by the affordable large data volume storage and its efficient processing. It will further help in making the e-learning process interactive and result-driven.

1. Customisation and personalisation

Machine Learning algorithms analyse the mannerism by which the students explore the information they are provided with. In case, the students have grasped the topic well enough, they move ahead. But if not, then ML takes them back and makes them go through relevant points again to make them understand the concept better.

They also keep track of the learning process of the students at an individual level. ML gives an edge to e-learning over classroom teaching by ensuring that the students learn effectively with the varied formats of content in an interactive, engaging, and techie manner.

2. Upgradation of content

Machine Learning helps to upscale the e-learning process by upgrading the content and tailoring it as per the present requirement. They analyse the quality of the content of the various courses offered and compare it with them. Then, they accordingly make changes in their courses to provide state of the art education curriculum to the students. This helps them give an edge over the players in the market as they pro-actively alter their courses to extend the best of the educational experiences to their students.

3. Ensures higher ROI

When ML is applied, students get personalised services. Additionally, Machine Learning provides real-time benefits to the students thereby leading to their increased adoption in the education system. The process of predictive analysis further helps keep track of the progress of every student and also enables their quick grading.

4. Provides instant solutions

When incorporated, Machine Learning enables the platforms to provide instant solutions to the queries of the students. This benefit of ML makes it increasingly accepted by the educational and ed-tech platforms.

5. Helps save time by taking up admin-related jobs

ML helps save the time of students by automating the admin-related jobs. This makes the students devote their time wholeheartedly to their studies and assignments and they won’t waste their time on the admin-related jobs.

Summing Up

By offering personalisation, customisation, and enabling the use of virtual assistants, Machine Learning has become increasingly relevant for students more than before. While it cannot replace the teachers, it is used as a system to support and assist the traditional teaching methods. Since, digitisation is present and proliferating, with it, the technological advancements will amplify too.

ML can’t stay behind in the race considering the various benefits it offers and the wide scope it has. It has indeed revolutionised the education sector and will lead to further upscaling of the industry thereby being a major contributor to its growth.

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Do you want to minimize risk and have your business succeed in increasingly uncertain times? If so, then studying data science could be your next best step.

Before you take any decision to study, it is vital to read as widely as possible around the subject and all the possibilities available. There are numerous and varied resources out there and as many buzzwords as there are bees. So, get as much information as you can and read it.

Here are a few introductory tips as to why data science may be the perfect study choice for entrepreneurs and wantrepreneurs such as yourself in 2021 and beyond.

Data Science Will Help You Understand Your Business

For improved enterprise management and the protection of profitability, you need to understand your business. What are the historical risks? What are the key features of the business over time? How can you avoid these in the future? In order to answer these questions, you will need to have a wealth of information at your disposal and be able to manipulate this data to create possible business planning scenarios. That process is encapsulated in data science. It’s a complex process made easy with the data science degree Merrimack college has to offer. Taking a qualification like this will enable you to learn crucial skills that can help solve real problems that your business could come to face.

Keep Your Business at the Cutting Edge

Current uncertainties in the market have increased all business risk, and it is now more pertinent than ever to use everything at your disposal to keep your enterprise profitable. A growing mainstream/ big business trend is the use of data science to improve risk management. In this instance, if data science is good for mainstream business, just maybe it’s good for us. Furthermore, studying data science will provide for one of those crucial entrepreneurial skills, allowing you to express your willingness to learn.

One of the Fastest Growing Professions

A qualification in data science currently offers you the security that many entrepreneurs can only dream of. As once you understand the importance of big data and data science for your business, not only will you improve risk management for your enterprise, but you have at your fingertips the skills for a top professional job or consultancy option.

You can be a data scientist, research scientist, or senior research analyst, just to mention a few of the numerous professional jobs a data science degree or course can get you.

Understanding Data Science Can Reduce Your Exposure to Business Risk

Using as much historical data as can be collected and having a keen understanding of current trends, coupled with data science, allows the building of sensible business scenarios that serve to plan around risk. A good understanding of data science will allow you to mitigate the risk, dealing with it using advanced planning and adjustments made to the operations.

What Now?

If you’re serious about data science as your way to improve your career or business, then your next step is to strategize as to the many possible scenarios that big data mining may provide your business. It seems a lengthy process but can be simplified with the right knowledge base. Studying data science is thus the most sensible route. To gain a genuine understanding of the algorithms used to collect and process big data in a scientific way to improve the manner in which a business deals with risk, a professional data science qualification may be for you.

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