Big Data Analytics Assignment Help

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CS5608 Big Data Analytics Assignment

Assessment Title - Design, implementation and evaluation of a complete data analytics solution

Comprehensive Assignment Support for Y/617/3035 Advanced Data Analytics in the OTHM Level 6 Diploma in Information Technology, Helping You Master Complex Data Concepts and Excel Academically.

LEARNING OUTCOMES -

Learning Outcome 1 - Implement appropriate analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data.

Answer: To extract value from real-time, heterogeneous data, a combination of analytic methods and techniques is essential. Data cleaning and pre-processing are crucial to ensure data quality and consistency. Feature engineering techniques can create new, informative features from existing data. Machine learning algorithms like linear regression, decision trees, and neural networks can be employed for predictive modeling and pattern recognition. For time-series data, specialized techniques like ARIMA and LSTM can be utilized. Real-time data processing frameworks like Apache Flink or Kafka can handle high-velocity data streams and enable timely analysis. By carefully selecting and applying these methods, organizations can gain valuable insights and make data-driven decisions in real-time.

Learning Outcome 2 - Critically reflect on analytic methods/techniques/algorithms, their ability to deliver accurate predictions of various kinds and the value and limitations of prediction.

Answer: Analytic methods, techniques, and algorithms can provide accurate predictions of various kinds, but their effectiveness depends on several factors. Data quality, quantity, and diversity play a crucial role. Overfitting, a common issue, occurs when models become too complex and perform poorly on unseen data. Regularization techniques can help mitigate this. The choice of algorithm must align with the nature of the problem and the desired outcome. For instance, regression models are suitable for continuous predictions, while classification models are better for categorical outcomes. While predictions can be valuable for decision-making, their limitations should be acknowledged. Uncertainty and bias can affect accuracy, and predictions may not always be reliable in dynamic environments. Therefore, it's essential to critically evaluate the limitations of predictive models and use them in conjunction with expert judgment and other sources of information.

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A. MAIN OBJECTIVE OF THE ASSESSMENT -

The aim of this assessment is to design, implement and evaluate a complete data analytics solution with the R software environment for statistical computing and graphics. You will have the opportunity to use the analytical features offered by R (through built-in functions and CRAN packages) to perform basic and advanced analytical routines, to explore the machine learning methods presented during the lectures and to use these methods for data transformation, integration, analysis and knowledge discovery. You are expected to perform all the data analysis in R and to include the scripts you have used in your report. Examples of scripts for each of the required data analysis task have been provided and commented during the lab sessions.

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B. DESCRIPTION OF THE ASSESSMENT -

You will have to use multiple data files (at least two different data sets) from existing open data sources (examples of data sources were provided in the lecture during Week 17). The task is to integrate these data files, perform a joint analysis on them and create a narrative about the data you have analysed driven by a research question of your choice. The research question should be relevant to the domain of knowledge of the data. You will have to select the most appropriate data analysis strategy to answer the research question, but you should include at least two of the methods presented in the lectures. The use of methods not presented during the module will not contribute to the grade, also if the method is relevant for the analysis.

The analysis you provide should be written in a report of no more than 12 pages (11pt font minimum, the only content allowed beyond the 12^th page is an appendix section including the R scripts).

You are invited to report the use of at least two machine learning methods (either for different tasks required to answer the research question or in a comparative fashion). The use of methods with different goals (e.g. regression, classification and clustering) is encouraged. You should include clear justification for each choice of method and data analysis technique. Data analysis results should be presented with tables and graphs.

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The assignment will be marked according to the following criteria:

i) Identifying a data analytics problem and formulating a relevant research question and plan.

ii) Preparing and integrating multiple data sets that are suitable to answer the research question.

iii) Implementing and executing a complete and coherent data analysis in R.

iv) Critically reflecting on the results of the data analysis (accuracy, limitations and interpretation).

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C. FORMAT OF THE ASSESSMENT -

You should submit your report as a single .pdf file. Your report should include exactly the following sections:

A. Data description and research question

B. Data preparation and cleaning

C. Exploratory data analysis

D. Machine learning prediction

E. Performance evaluation

F. Discussion of the findings.

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