Data Principles week 2 part 1

What are the distinctions between the model planning and model building phase?

• Model planning involves data collection, while model building involves data analysis.

• Model planning is about choosing the right tools, while model building is about implementing the model.

• Model planning focuses on the theoretical framework, while model building is the practical application.

• There are no distinctions; both phases are the same.

What are some key considerations in model building?

• Selecting the right dataset and tools for implementation.

• Ensuring the model is overfit to the training data.

• Ignoring the problem statement and focusing on the algorithm.

• Choosing a complex model for simple tasks.

What software tools (commercial, open source) are typically used at this phase?

• Word processors and spreadsheet software.

• Web browsers and email clients.

• Data analysis and machine learning libraries.

• Graphic design and video editing software.

What is Linear Regression model and in what situation is it appropriate?

• It is a classification model used for image recognition tasks.

• It is a regression model used for predicting numerical values based on independent variables.

• It is a clustering model used for grouping similar data points.

• It is a reinforcement learning model used for real-time decision making.

How does the Linear Regression model work for predictive modelling tasks?

• By finding the median value of the dependent variable.

• By clustering data points into different categories.

• By establishing a relationship between dependent and independent variables using a straight line.

• By using decision trees to make predictions.

How do we prepare our data prior to applying the Linear Regression model?

• By converting all data into text format.

• By ensuring data is clean, relevant, and properly formatted.

• By randomly shuffling the data to create diversity.

• By deleting all outliers without analysis.

What is one of the learning outcomes related to the processes within the Data Analytics Lifecycle?

• Evaluate suitable techniques and tools for specific data science tasks.

• Develop analytics plan for a given business case study.

• Describe the processes within the Data Analytics Lifecycle.

• Create a marketing strategy for data analytics.

Which learning outcome focuses on the analysis of business and organisational problems?

• Evaluate suitable techniques and tools for specific data science tasks.

• Analyse business and organisational problems and formulate them into data science tasks.

• Develop analytics plan for a given business case study.

• Summarize the history of data analytics.

What does the learning outcome related to evaluating techniques and tools pertain to?

• Developing a comprehensive understanding of data analytics history.

• Evaluating suitable techniques and tools for specific data science tasks.

• Describing the processes within the Data Analytics Lifecycle.

• Analyzing the effectiveness of data analytics in marketing.

What is the goal of the learning outcome that involves developing an analytics plan?

• To create a new data analytics software.

• To evaluate the impact of data analytics on global markets.

• To develop analytics plan for a given business case study.

• To describe the ethical considerations in data analytics.