ISP610 Business Data Analytics Assignment Sample UiTM Malaysia

ISP610 Business Data Analytics Assignment – The purpose of this assignment is to understand how organizations can use data analytics to improve their business strategies. It also discusses various tools and techniques available for performing these functions, as well as an overview of what it means when a company has implemented successful operations using those methods.

The information provided here should not only cover the basics of each topic but go into greater detail so that readers who lack technical knowledge will still be able to read through everything easily without any confusion or difficulty understanding new terms/terminologies used throughout this paper.

Organizations use data analytics to improve their business strategies in a variety of ways. For example, they may use it to understand customer behaviour or preferences, optimize marketing campaigns, or streamline operations. Additionally, data analytics can help organizations reduce costs and increase revenues by identifying efficiencies and opportunities for improvement.

Several different tools and techniques can be used for data analytics, including statistical analysis, data mining, predictive modelling, and machine learning. Each of these methods has its strengths and weaknesses, and the best approach for an organization will depend on the specific goals and objectives that they are trying to achieve.

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Assignment Brief 1: Display practical skills in handling multiple operations on the different forms of data.

There’s no one answer to this question, as the best way to display your skills in handling multiple operations on different forms of data will vary depending on the situation. However, here are a few tips that may help you in this regard:

First, try to organize your data in a way that makes sense for the task at hand. For example, if you’re working with financial data, organizing it by transaction type or date range can make it easier to manipulate.

Second, when performed on larger data sets, multiple operations can take quite a bit of time to complete. If possible, break up your operations into smaller chunks so that you can check your work as you go. This can save you a lot of headaches in the long run.

Finally, always be mindful of the potential for errors when working with data. Checking your work regularly can help prevent costly mistakes down the line.

Assignment Brief 2: Evaluate appropriate text analytical techniques in business problems.

Text analytical techniques can be extremely valuable in business settings, as they can provide insights into customer sentiment, competitor activity, and more. Some of the most commonly used text analytical techniques include sentiment analysis, keyword extraction, and topic modelling.

Sentiment analysis is the process of identifying positive or negative sentiments in a piece of text. This can help understand how customers feel about a company or product, as well as for tracking changes in customer sentiment over time. Keyword extraction is the process of identifying relevant keywords or key phrases from a piece of text.

This information can be useful for understanding what topics are being discussed about a company or product, as well as for monitoring competitor activity. Topic modelling is a technique that identifies the underlying themes or topics in a corpus of text. This can help understand the overall content of a large text dataset, as well as for identifying potential areas of interest for further analysis.

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Assignment Brief 3: Demonstrate lifelong learning skills related to business data analytics applications.

Text analytical techniques can be extremely valuable in business settings, as they can provide insights into customer sentiment, competitor activity, and more. Some of the most commonly used text analytical techniques include sentiment analysis, keyword extraction, and topic modelling.

Sentiment analysis is the process of identifying positive or negative sentiments in a piece of text. This can help understand how customers feel about a company or product, as well as for tracking changes in customer sentiment over time. Keyword extraction is the process of identifying relevant keywords or key phrases from a piece of text.

This information can be useful for understanding what topics are being discussed about a company or product, as well as for monitoring competitor activity. Topic modelling is a technique that identifies the underlying themes or topics in a corpus of text. This can help understand the overall content of a large text dataset, as well as for identifying potential areas of interest for further analysis.

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