A Gratia Data Analyst is responsible for gathering and analyzing various forms of data to establish a factual basis for decision-making. Their objective is to identify patterns or trends that can be utilized for informed decision making and projections. Utilizing programs such as Python, Tableau, and software such as Airtable, Gratia Data Analysts can efficiently extract, manipulate, and analyze data, turning that information into visually appealing and interactive dashboards and reports that effectively communicate insights. By collecting, analyzing, and interpreting data, they empower organizations to make strategic choices, optimize processes, and gain a competitive advantage.
<aside> <img src="/icons/arrow-right-basic_gray.svg" alt="/icons/arrow-right-basic_gray.svg" width="40px" /> Data Analyst Responsibilities
A research assistant web-based product that empowers analysts to discover and ramp up new investment ideas in record time approached Gratia for help in preparing a training dataset for a financial AI model. The model would be used by senior executives to quickly browse select information about publicly listed companies by just typing in a few keywords.
The training dataset prepared by the Gratia Analyst was able to identify the key information in the data with a high degree of accuracy, due to the Analyst's comprehensive understanding of business processes, objectives, and data requirements.
The Gratia Data Analyst first used a variety of tools to gather a large dataset of financial information from various sources, including news articles, financial reports, and social media posts. The dataset also included a few other types of financial documents, such as 10-K reports, 10-Q reports, and annual reports. The data was then cleaned and processed to remove any errors or inconsistencies. The analyst then used a specialized data labeling tool to label the data with the key information that the model would need to learn, such as the company name, stock price, financial performance, and news headlines.
The labeled data was then used to train the AI model on a powerful computer cluster. The training process took several weeks, but the model was able to learn to identify the key information in the data with a high degree of accuracy. Once the model was trained, it was tested on a set of unseen data. The model was able to correctly identify the key information in the unseen data with a high degree of accuracy. The success metrics for the project included identification of the key information in the data with an accuracy of 95%, data processing to generate results in under 1 minute, and the model’s ability to handle large datasets.
<aside> <img src="/icons/arrow-right-basic_gray.svg" alt="/icons/arrow-right-basic_gray.svg" width="40px" /> To learn more about how a Data Analyst can benefit your organization, schedule a call with our team here.
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