Explain if the following statements are true or false in details with scholar references. 1. All data science investigations start with an existing dataset. 2. Data scientists do most of their work in Python and are unlikely to use other tools. 3. Most data scientists spend the majority of their time developing new models. 4. The use of historical data to make decisions about the future can reinforce historical biases. 1. Explain with references the differences between Information visualization and visual analytics. (2 paragraphs) Why would you want to use data visualization for (2 paragraphs) 3. Data used in visual design comes in a “messy” form and as a result the data must go through a data cleansing process. a. Explain what is a “messy” data. (2 paragraphs) b. Explain why a data may come in a “messy” data format. (2 paragraphs) c. Explain methods that maybe used to cleansed the data. (2 paragraphs) d. Data sets come in many different format. Many formats are prone to error because the data lack any information that indicates structure. i. Give examples of data sets that lack structure. (1 paragraphs) ii. Give examples of data sets that includes structure. (1 paragraphs)

1. All data science investigations start with an existing dataset.

False. While many data science investigations do start with an existing dataset, it is not always the case. In some scenarios, data scientists may collect their own data through surveys, experiments, or other means. Additionally, data scientists may also work with data that is continuously streaming in real-time, which does not necessarily start from an existing dataset. Therefore, data science investigations can start both with existing datasets as well as with new data collection processes.

2. Data scientists do most of their work in Python and are unlikely to use other tools.

False. While Python is a popular programming language in the field of data science and is often used for various data science tasks such as data cleaning, analysis, and modeling, data scientists do not exclusively use Python. There are several other programming languages and tools used in data science, including R, Julia, and SQL, among others. The choice of programming language and tools depends on the specific requirements and preferences of the data scientist and the nature of the data being analyzed.

3. Most data scientists spend the majority of their time developing new models.

False. While developing new models is an important aspect of data science, it is not necessarily the primary focus or time-consuming task for most data scientists. The data science process involves various stages, including data collection, data cleaning, exploratory data analysis, feature engineering, model selection, model evaluation, and deployment. Each of these stages requires substantial time and effort. Moreover, data scientists also spend a significant amount of time on data preprocessing and feature engineering, which are critical for building accurate and reliable models. Therefore, while developing new models is an important component of data science, it does not consume the majority of a data scientist’s time.

4. The use of historical data to make decisions about the future can reinforce historical biases.

True. The use of historical data to make decisions about the future can indeed reinforce historical biases. Historical data often reflects the societal, cultural, and systemic biases that were present during its collection. Therefore, when using historical data to build predictive models or make decisions, these biases can be perpetuated and result in biased predictions or biased decision-making. It is crucial for data scientists to be aware of these biases and take appropriate steps, such as carefully selecting and preprocessing the data, using unbiased sampling methods, employing fairness metrics, and regularly evaluating and updating the models, to mitigate their impact on the outcomes.

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