Forage Virtual Experience (VE) Programs are innovative online learning experiences designed to provide students with a taste of real-world work in various industries and professions. These programs are developed by Forage, a company specializing in creating immersive virtual experiences in partnership with leading organizations worldwide. The Forage VE Programs allow students to gain valuable insights into specific industries, companies, and roles through interactive modules and tasks. Participants can engage in simulated work tasks, solve real-world problems, and develop practical skills relevant to their field of interest. These programs are typically self-paced, enabling students to complete them at their convenience.
Overall, Forage VE Programs offer students a unique chance to explore different industries, gain practical skills, and make informed decisions about their career paths. These programs bridge the gap between academic learning and real-world work, providing a valuable stepping stone for students looking to enter the workforce or pursue further education in their chosen field.
The Deloitte Australia VE Program offers a range of virtual modules and interactive tasks that simulate real-world work scenarios. Participants have the chance to engage in projects and assignments related to areas such as cyber technology, forensic investigations, data analytics, platform engineering, and coding. These activities are designed to provide hands-on experience and practical skills development, giving students a taste of what it's like to work in these areas within a leading professional services firm. Completing the Deloitte Australia VE Program can be an impressive addition to a participant's resume, demonstrating their commitment to professional development and understanding of the industry. It can also open doors to further opportunities within Deloitte, such as internships or graduate roles.
The task involves completing a programming project on repl.it. The steps include creating an account, forking the project, exploring different data formats, and completing the solution in the main.py file. The project provides automatic testing, and the goal is to have successful tests without failures. The task requires implementing two functions and converting one data format to match the target format. Once completed, the solution's URL needs to be submitted.
The task involves analyzing telemetry data using Tableau software. To complete the task, download and install Tableau, import the provided daikibo-telemetry-data.json.zip file, and create calculated measures for unhealthy statuses. Create bar charts for downtime per factory and downtime per device type. Then, create a dashboard with these charts, set the first chart as a filter, and select the factory with the highest downtime. Take a screenshot of the dashboard and submit it as the task completed. A step-by-step Task Guide PDF is available for assistance.
The development proposal aims to create a private dashboard displaying the health status of nine machines in each of Daikibo's four factories, accessible only within the client's Intranet. The dashboard will synchronize authentication with the internal authentication server, allowing users to leverage their company-wide accounts. The proposal includes an overview, scope, estimate of man-hours for development, testing, and integration, project timeline, and assurance of continuous product support. Use an office package like MS Office or Libre Office to create the proposal document using the provided template.
In this task, as part of the Cyber Security team, your objective is to investigate a potential breach. You need to determine if the breach could have occurred directly from an external attacker without accessing Daikibo's VPN. Analyze the web_requests.log file during the specific period when the alleged attack occurred. Look for suspicious requests, particularly longer sequences of user requests following the order of Login, dashboard page resource requests, and API requests for machine statuses. Pay attention to automated requests occurring at precise intervals and record the user's ID associated with such requests. The log file structure consists of blocks representing unique IP addresses, their corresponding requests sorted by time, and static IP addresses from Daikibo's internal network.
The final task involves editing an Excel file called "Equality Table.xlsx" that contains three columns: Factory, Job Role, and Equality Score. The objective is to create a fourth column called "Equality class" to classify the equality scores into three types: Fair (within the range of +-10), Unfair (less than -10 and greater than +10), and Highly Discriminative (less than -20 and greater than +20). Examples of the classification are provided. After completing the task, upload the edited version of the file.
- Git Bash
- Python
- Jupyter Notebook
- VS Code
- Microsoft Word Document
- Tableau Community Edition
- Sublime Text 3
- Microsft Excel
- Google Chrome Web Browser
- Notepad
- Windows 10
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Processing employee compensation data and generating reports: The task involved handling employee compensation data and generating an Excel file, "Equality Table.xlsx," with relevant columns for analysis.
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Classifying equality scores: The task required creating a new column, "Equality class," to classify the equality scores into three types: Fair, Unfair, and Highly Discriminative. This classification was based on predefined ranges and criteria.
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Applying data classification logic: By examining the equality scores, we applied logical conditions to determine the appropriate classification for each score. This exercise helped us understand the process of categorizing data based on specific criteria.
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Enhancing data analysis skills: Through this task, we further developed our data analysis skills by working with real-world data and applying classification techniques. This experience allowed us to gain hands-on knowledge of manipulating data within an Excel file.
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Practicing attention to detail: The task emphasized the importance of careful attention to detail, ensuring accurate classification based on the provided criteria. It reinforced the need for precision in data analysis tasks.
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Documenting and submitting work: Completing the task involved ensuring accurate edits in the Excel file and submitting the updated version as per the given instructions. This reinforced the significance of clear documentation and proper file management in professional settings.
These key learnings contribute to enhancing data analysis capabilities, reinforcing our understanding of classification techniques, and reinforcing the importance of accuracy and attention to detail in handling data.
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