1.Machine learning has revolutionized data analytics by enabling intelligent pattern recognition, prediction, and decision-making. Provide three specific examples of how machine learning is applied in data analytics, such as fraud detection, recommendation systems, and sentiment analysis. Analyse the impact of machine learning on improving business processes, customer experiences, and decision-making outcomes. [25] 2.Quantitative data methods are essential tools in business analysis for extracting meaningful insights from numerical data. Briefly explain the two primary types of quantitative data methods: descriptive statistics and inferential statistics. [10] 3.Discuss the purpose and application of each method in business decision-making. [15] 4.Explain the mechanism of supervised learning as a fundamental approach in machine learning. Emphasizing how algorithms map inputs to outputs. Provide an example of a supervised learning algorithm, such as logistic regression, and describe its application in a specific domain.[25] 5.Data analytics plays a pivotal role in various AI applications, enabling intelligent decision-making and problem-solving. Discuss three specific examples of how data analytics is utilized in AI applications, such as machine learning, natural language processing, and computer vision.[10] 6.Analyse the impact of data analytics on the performance and effectiveness of these AI applications.[15] 7.Outline the steps involved in developing and evaluating a predictive model for customer churn using logistic regression or a decision tree. Discuss the importance of data preparation, feature engineering, model training, and model evaluation in the process. Explain how you would evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.[25] 8.Data quality is a critical aspect of data analytics, ensuring the accuracy, reliability, and consistency of data for meaningful analysis. Explain four common data quality issues and discuss strategies for addressing them. [15] 9.Elaborate on the process of data transformation, highlighting the role it plays in preparing data for effective analysis. [10] 10.Describe three techniques used to validate the accuracy and completeness of data before analysis. [6] 11.Explain how you would gather and understand the specific needs of stakeholders when designing a data dashboard. [6] 12.Discuss the key characteristics of effective data visualizations that make them clear, informative, and impactful. [5]

Machine learning has been instrumental in data analytics in various ways. For instance, in fraud detection, machine learning algorithms can learn from historical fraud patterns and detect suspicious activities. In recommendation systems, machine learning can analyze user behavior and preferences to provide personalized recommendations. In sentiment analysis, machine learning can interpret and classify emotions in text data to understand customer sentiments. The impact of machine learning on business processes and decision-making outcomes is significant. It allows for more accurate predictions, better customer experiences, and more efficient business processes.

Descriptive statistics summarize and organize characteristics of a data set, such as mean, median, mode, and standard deviation. Inferential statistics, on the other hand, allow for conclusions or predictions to be made about a population based on a sample of data.

Descriptive statistics can help businesses understand the current state of affairs, such as average sales per month. Inferential statistics can help businesses make predictions about future trends, such as forecasting sales for the next quarter.

Supervised learning is a type of machine learning where an algorithm learns from labeled training data, and makes predictions based on that data. An example of a supervised learning algorithm is logistic regression. This algorithm can be used in a variety of domains, such as healthcare, where it can predict the likelihood of a patient having a disease based on certain symptoms.

Data analytics is used in AI applications in various ways. In machine learning, it helps in training models and making predictions. In natural language processing, it helps in understanding and generating human language. In computer vision, it helps in recognizing and interpreting images and videos.

Data analytics significantly improves the performance and effectiveness of AI applications. It allows for more accurate predictions, better understanding of data, and more efficient decision-making.

Developing a predictive model for customer churn involves several steps. First, data preparation involves cleaning the data and dealing with missing values. Feature engineering involves selecting the most relevant features for the model. Model training involves training the model on the data, and model evaluation involves assessing the model's performance. The model's performance can be evaluated using metrics such as accuracy, precision, recall, and F1-score.

Common data quality issues include inaccurate data, missing data, inconsistent data, and outdated data. These issues can be addressed by implementing data validation checks, using data cleaning tools, and regularly updating the data.

Data transformation involves converting data from one format or structure into another. This is crucial for preparing data for analysis, as it ensures that the data is in a suitable format for the analysis tools.

Techniques used to validate the accuracy and completeness of data before analysis include data profiling, data auditing, and data reconciliation.

To gather and understand the specific needs of stakeholders when designing a data dashboard, one could conduct interviews, surveys, or focus groups. It's also important to understand the business context and objectives.

Effective data visualizations should be clear, informative, and impactful. They should present data in a way that is easy to understand, provides valuable insights, and influences decision-making.