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M32614 Business Analytics Assignment Answer UK

M32614 Business Analytics Assignment Answer UK

M32614 Business Analytics course explore the fundamental principles and techniques of business analytics, which is the practice of using data, statistical and quantitative analysis, and predictive modeling to drive business decisions and improve performance.

Throughout this course, we will cover various topics including data collection and cleaning, exploratory data analysis, regression analysis, time series forecasting, optimization, and decision-making under uncertainty. We will also discuss the ethical considerations and challenges associated with data analytics.

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Diploma Assignment Help UK is an online platform that provides academic assistance to students pursuing various diploma courses in the UK. As a student of M32614 Business Analytics course, you may be required to submit assignments on various topics related to business analytics. These assignments may have strict deadlines, and failing to submit them on time can negatively impact your academic performance.

In this segment, we will describe some assignment activities. These are:

There are several technology trends that are currently shaping modern business, including:

  1. Cloud Computing: Cloud computing enables businesses to access IT resources and services over the internet, rather than having to maintain expensive and complex on-premises IT infrastructure. This allows businesses to scale up or down as needed, and to pay only for what they use.
  2. Artificial Intelligence and Machine Learning: These technologies allow businesses to automate tasks and processes, improve decision-making, and gain insights from vast amounts of data. AI and machine learning can be used in a variety of ways, such as customer service chatbots, fraud detection, and predictive analytics.
  3. Internet of Things (IoT): IoT refers to the network of physical devices that are connected to the internet, such as smart home appliances, wearable devices, and industrial equipment. Businesses can use IoT to collect data, monitor performance, and automate processes.
  4. Big Data Analytics: Big data analytics involves processing and analyzing large volumes of data to uncover insights and make better decisions. This can include analyzing customer behavior, market trends, and operational data to identify opportunities for improvement and growth.
  5. Cybersecurity: As businesses increasingly rely on technology to operate, cybersecurity has become more important than ever. Companies need to protect their data and networks from cyber threats, such as hacking and data breaches.

These technology trends are shaping modern business by enabling greater efficiency, productivity, and innovation. Businesses that are able to adapt and leverage these technologies are better positioned for success in today’s fast-paced digital economy.

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Assignment Activity 2: Explain data privacy and ethics in Big Data age.

Data privacy and ethics are crucial concerns in the age of Big Data, which refers to the increasing amount of digital information generated, collected, and analyzed by individuals, organizations, and governments. In this context, data privacy refers to the right of individuals to control their personal information and to determine how it is used and shared, while data ethics refers to the moral principles and values that guide the collection, storage, processing, and use of data.

One of the main challenges of data privacy in the age of Big Data is the sheer volume and complexity of data. With the proliferation of digital devices and the internet of things (IoT), there is a massive amount of data being generated and collected about individuals, including their online activity, location, health status, and more. This data can be used for various purposes, such as targeted advertising, fraud detection, and improving healthcare outcomes, but it can also be used for nefarious purposes, such as identity theft and surveillance.

To address these concerns, there are various data privacy regulations and frameworks, such as the General Data Protection Regulation (GDPR) in the European Union, which aims to protect the personal data of EU citizens and residents, and the California Consumer Privacy Act (CCPA) in the United States, which grants California residents certain rights over their personal information. These regulations typically require organizations to obtain consent from individuals before collecting and using their data, provide transparency about how the data is being used, and allow individuals to access, modify, or delete their data.

In addition to data privacy, data ethics is also a critical concern in the age of Big Data. The ethical considerations around data collection and use can be complex and multifaceted, and can involve issues such as data bias, algorithmic transparency, and data ownership. For example, biased data sets can lead to biased algorithms and decision-making, which can perpetuate discrimination and inequality. To mitigate these risks, organizations must ensure that their data collection and processing practices are transparent, accountable, and fair, and that they are respectful of individuals’ rights and freedoms.

Assignment Activity 3: Apply data extraction techniques to shape dataset for analytics.

Data extraction is a crucial step in preparing data for analytics. Here are some techniques that can be used to shape a dataset for analytics:

  1. Data Cleaning: The first step in data extraction is to clean the data. This involves removing any duplicate records, handling missing values, correcting data formatting errors, and removing irrelevant data. Data cleaning ensures that the dataset is accurate and consistent.
  2. Data Transformation: Data transformation involves converting the data into a format that is suitable for analysis. This includes converting data types, reformatting columns, and creating new variables that can be used in analysis.
  3. Data Aggregation: Data aggregation involves summarizing the data to create new variables that can be used in analysis. This includes calculating sums, averages, counts, and other statistical measures.
  4. Data Sampling: Data sampling involves selecting a subset of data from a larger dataset to use in analysis. This can help reduce the size of the dataset and make it easier to work with.
  5. Data Integration: Data integration involves combining data from multiple sources to create a single dataset that can be used in analysis. This can include merging datasets, joining tables, or appending data.
  6. Data Filtering: Data filtering involves selecting only the relevant data from a larger dataset. This can be done using criteria such as date ranges, specific values, or other conditions.
  7. Data Normalization: Data normalization involves transforming the data to a common scale or format. This can be useful when comparing data from different sources or when analyzing data over time.

By applying these techniques to shape a dataset for analytics, you can ensure that the data is accurate, consistent, and in a format that is suitable for analysis.

Assignment Activity 4: Apply data visualisation techniques for data analysis and information communication.

Data visualization is a powerful tool for data analysis and information communication, as it allows us to represent complex data sets in a visual format that is easy to understand and interpret. Here are some techniques for data visualization:

  1. Bar Charts: Bar charts are a great way to show categorical data. They are easy to read and interpret, and can be used to show frequency or proportions of different categories.
  2. Line Charts: Line charts are ideal for showing trends over time. They are often used to show how a particular variable changes over time.
  3. Scatter Plots: Scatter plots are useful for visualizing the relationship between two continuous variables. They can help identify patterns and outliers in the data.
  4. Heatmaps: Heatmaps are a great way to show the distribution of data over a geographic area or to highlight differences between categories.
  5. Bubble Charts: Bubble charts are similar to scatter plots, but use the size of the data points to represent a third variable. They can be used to show relationships between three variables.
  6. Box Plots: Box plots are useful for visualizing the distribution of data. They can be used to show the median, quartiles, and outliers in a data set.
  7. Histograms: Histograms are useful for visualizing the distribution of a single variable. They can be used to show the frequency of different values in a data set.

When creating data visualizations, it is important to consider the audience and the purpose of the visualization. The visualizations should be clear, easy to read, and effectively communicate the key insights from the data. Color, labels, and annotations can also be used to highlight important information and make the visualizations more engaging.

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