FIN559 Big Data, Cloud Computing And Machine Learning Assignment Sample SUSS Singapore

The impact of big data, cloud computing and machine learning is finally here. FIN559 provides an overview on how these technologies are changing the way we live our lives from banking to shopping – even fitness apps! It all starts with providing students a foundational understanding about processing framework that deals with overwhelming volumes in data sets as well other approaches like artificial intelligence or deep learning for those looking forward into future tech trends. The course then goes deeper into examining core elements related specifically towards clouds including types depending upon what kind needs fulfilling (i..e storage) Risk Assessment Security concerns and the impacts of legal and compliance regulations are also discussed in this course. After which, students get to see how these technologies are practically used in industry via case studies from different domains like retail and e-commerce.

The course provides a framework for selecting the optimal combination of cloud technology to meet business requirements. Furthermore, it introduces machine learning covering both supervised and unsupervised algorithms with bias variance trade-off in mind when choosing models; this is followed by neural networks & convolutional neural networks which finally lead us into artificial intelligence applications through case studies on their own domains.

FIN559 provides an overview on how big data, cloud computing, and machine learning are changing the way we live our lives from banking to shopping – even fitness apps! It all starts with providing students a foundational understanding about a processing framework that deals with overwhelming volumes in data sets. The course then goes deeper into examining core elements related specifically towards clouds including types depending upon what kind needs fulfilling (i.e. storage) Risk Assessment Security concerns and the impacts of legal and compliance regulations are also discussed in this course. After which, students get to see how these technologies are practically used in industry via case studies from different domains like retail and e-commerce.

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Assignment Brief 1:Appraise big-data computing framework, the challenges in big-data computing issues and the approaches to resolve them

The challenges in big-data computing can be broadly classified into three categories–issues with data acquisition, management and analysis. 

Data acquisition refers to the process of capturing data from various sources for further processing. Due to the ever-growing volume and variety of data, this has become a major challenge for big-data systems. There are a number of approaches to address this issue, such as using data acquisition tools that can scale up with the size of the data set, parallelizing the data acquisition process across multiple nodes and employing streaming techniques to capture data in real-time.

Data management is another critical challenge for big-data systems. With so much data being generated every day, it is important to have a system that can efficiently store and manage it. There are a number of approaches to address this issue, such as using NoSQL databases, which are designed for handling large amounts of data, and employing data compression techniques to reduce the amount of storage required.

The last challenge is big-data analysis. With so much data being generated, it is becoming increasingly difficult to extract useful information from it. There are a number of approaches to address this issue, such as using machine learning techniques, which can automatically learn from data, and employing parallel processing techniques to speed up the analysis process.

Assignment Brief 2:Assess different types of clouds, core elements and risk management plans of cloud computing

Cloud computing is a process of outsourcing where businesses can outsource their computational requirements to a remote third party provider in order to save time and money. By using the cloud, companies are able to improve their disaster recovery plan as well as reduce capital expenses.

There are three types of clouds: public, private, and hybrid. Public clouds are those that are run by third-party providers and are open to anyone who wants to use them. Private clouds are operated by a single business or organization and are used exclusively by them. Hybrid clouds combine elements of both public and private clouds.

When it comes to risk management, businesses need to ask themselves a few questions: What data will be stored in the cloud? How will that data be accessed? What are the possible security breaches that could occur? And what is the likelihood of those breaches happening?

Data stored in the cloud is susceptible to the same risks as any other data stored electronically. However, there are some additional risks associated with cloud computing, such as data loss and leakage, unauthorized access, and Denial of Service attacks.

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Assignment Brief 3:Evaluate IaaS, PaaS and SaaS technology

There are many different types of cloud computing services, but the three most common ones are IaaS, PaaS, and SaaS.

IaaS (Infrastructure as a Service) is the simplest cloud computing service. With IaaS, the provider delivers a virtualized compute resource such as a server, storage area network, or network interface card. Customers can then run their own software on top of this virtualized infrastructure. 

PaaS (Platform as a Service) is more complex than IaaS. With PaaS, the provider delivers not only the virtualized compute resources, but also the platform on which to run your software. This might include a runtime environment, middleware, and libraries. PaaS providers make it easy for you to develop and deploy your applications without worrying about the underlying infrastructure.

SaaS (Software as a Service) is the most complex cloud computing service. With SaaS, the provider delivers a complete application suite that you can use right away. You don’t need to install or configure any software; you just need a web browser. SaaS applications are usually subscription-based, so you pay a monthly or annual fee to use them.

Assignment Brief 4:Compare supervised learning against unsupervised learning, and neural networks against convolutional neural networks

Supervised learning is a type of machine learning algorithm that relies on labeled data to learn representations of data. Unsupervised learning is a type of machine learning algorithm that relies on unlabeled data to learn representations of data. Neural networks are a type of machine learning algorithm that rely on both labeled and unlabeled data to learn representations of data.

Convolutional neural networks are a subset of neural networks that are specifically designed for image recognition tasks. They are able to learn more accurate representations because they are able to take into account the spatial structure of images (e.g., the relative positions of pixels in an image). This is important because the features that are important for recognizing an image may not be evident when looking at the individual pixels, but may be more apparent when looking at the larger structure of the image.

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Assignment Brief 5:Construct smart solution based on big-data computing

There are many ways to use big-data computing to construct smart solutions. For example, you can use big-data to analyze customer behavior in order to better understand what products or services they are likely to be interested in. You can also use big-data to predict how people are likely to respond to certain marketing campaigns, and you can use it to evaluate the performance of different marketing strategies. Additionally, big-data can be used for fraud detection and to identify potential security threats. By using big-data in these and other ways, businesses can become much more efficient and effective in their operations.

In addition to the examples mentioned above, big-data can also be used for a variety of other purposes. For instance, it can be used to improve the accuracy of weather forecasts, to map the spread of diseases, and to track the movements of people and animals. Additionally, big-data can be used to monitor financial markets and to study the behavior of social media users. As you can see, there are a myriad of ways in which big-data can be used to improve business operations and to create smart solutions. If your business is not currently using big-data, it may be time to consider doing so. It could potentially help you save time and money, and it could give you a competitive edge over your rivals.

Assignment Brief 6:Formulate calculation for practicing cloud economics

To calculate the economics of practicing cloud computing, you need to consider the cost of the cloud services, the amount of storage and bandwidth used, and the number of users. You also need to factor in any additional costs associated with running a cloud-based business, such as increased electricity usage.

Calculating the cost of using cloud services is relatively straightforward. You simply multiply the number of hours used by the hourly rate charged by the cloud provider. For example, if you use a cloud service for 100 hours in a month, your cost would be $100 (100 hrs x $1/hr).

Calculating the amount of storage and bandwidth used is a bit more complicated. You need to take into account the size of the files being stored, as well as the number of users accessing them. For example, if you have 10 GB of data stored on a cloud service, and 100 users accessing it, your total storage and bandwidth usage would be 1 TB (10 GB x 100 users).

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Assignment Brief 7:Implement cloud computing using open source software

There are a number of ways to implement cloud computing using open source software. One popular option is the OpenStack platform, which provides a set of tools for creating and managing cloud environments. Another possibility is to use the Eucalyptus platform, which offers similar functionality. Keep in mind that there are many other options available as well, so be sure to do your research before settling on a particular solution.

In terms of specific tools, you’ll need to choose a hypervisor for your system. This is the software that will allow you to create and manage virtual machines. Popular options include Xen, KVM, and VMware vSphere. You’ll also need to select a storage solution, such as Ceph or GlusterFS. And finally, you’ll need to choose an orchestration tool to manage your entire system. Options include Puppet, Chef, and Ansible.

When implementing cloud computing using open source software, it’s important to remember that there is no one-size-fits-all solution. You’ll need to tailor the system to meet your specific needs and requirements. Be sure to take the time to thoroughly evaluate all of your options before making a decision.

Assignment Brief 8:Design and develop machine learning applications

There are many ways to design and develop machine learning applications. One common approach is to use a framework such as TensorFlow, Keras, or PyTorch. These frameworks provide high-level APIs that make it easy to build and train models. Another approach is to use a lower-level library such as NumPy or Scikit-learn. These libraries provide more flexibility but require more development effort. Whichever approach you choose, it is important to have a good understanding of machine learning algorithms and how they work. This will help you design better models and avoid common mistakes. Finally, it is always helpful to test your models on real data to make sure they are performing as expected.

Regardless of the approach you choose, there are a few key steps that are always required:

1. Choose a model.

There are many different types of machine learning models, and it can be tricky to decide which one to use. There is no one-size-fits-all answer, so it’s important to understand the trade-offs between different models.

2. Train the model.

Once you’ve decided on a model, you need to train it on data. This is typically done using a training dataset, which is a set of examples that the model can learn from.

3. Evaluate the model.

After training the model, it’s important to evaluate its performance. This can be done in a number of ways, such as by comparing its predictions to the ground truth or by measuring its accuracy on a validation dataset.

4. Deploy the model.

Once you’re happy with the model’s performance, you need to deploy it so that it can be used in production. This may involve converting it to a format that can be run on a server or embedding it in an application.

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