In today’s competitive job market, stepping into a data science role can be challenging. The mantra “Start the job before you have it” resonates strongly, suggesting that prospective data scientists should adopt the mindset and responsibilities of the role even before securing a position.

Instead of merely submitting applications and awaiting responses, candidates can take proactive measures to showcase their initiative. Engaging in Kaggle competitions or completing online courses through platforms like Coursera and DataCamp can significantly enhance one’s skill set.

This article will focus on virtual internships, particularly those offered by Forage, which hosts company-sponsored programs at no cost. For individuals eager to break into data science, these opportunities provide invaluable experience and networking possibilities.

What is Forage?

Forage is a digital platform offering free virtual internships designed in collaboration with reputable companies across various industries. These programs aim to give participants real-world experience by simulating the tasks and responsibilities typical of data analysts.

Participants will engage in activities that reflect actual scenarios faced in the workplace. The self-paced nature of these programs typically requires around 5–6 hours to complete. Although submissions are not graded, participants receive model answers after submitting their attempts.

Upon finishing each program, a completion certificate is awarded, which can be added to resumes and LinkedIn profiles. Participants may also choose to share their data with Forage, allowing partnering companies to reach out regarding networking events and job opportunities.

For more details on Forage, check their FAQ page.

Now, let’s delve into the data science-related internships currently available on Forage.

Accenture North America — Data Analytics Virtual Experience

Background Your client, Social Buzz, a social media organization, seeks to keep trending content visible to users while maintaining user anonymity. As the company prepares for its IPO next year, it requires assistance in managing its rapid growth and analyzing vast amounts of unstructured data generated daily.

Task 1: Project Understanding You will receive a client brief that outlines the background, future plans, and project scope, along with a stakeholder chart detailing Accenture’s team structure and individual roles. The goal here is to grasp the business problem, understand Accenture's deliverables, and identify relevant tasks for a data analyst.

Task 2: Data Cleaning & Modelling Following the initial task, you will be given Excel spreadsheets containing user profiles, locations, sessions, content, and reactions. This task focuses on understanding how to clean, merge, and analyze these datasets, with resources provided for those who may be unfamiliar with these concepts.

Task 3: Data Visualization & Storytelling This task requires you to create engaging visual representations of the data and communicate findings effectively through a PowerPoint presentation designed to guide the client through their data and the business challenges faced.

Task 4: Present to the Client Finally, you will prepare and record a video presentation of your findings, ensuring to use industry-specific terminology while conveying your message with confidence.

Quantium — Data Analytics Virtual Experience Program Background Supermarkets frequently adjust their store layouts and product offerings to meet customer preferences and remain competitive. The Quantium analytics team plays a crucial role in assessing these changes and their effectiveness.

Task 1: Data Preparation and Customer Analytics You will analyze transaction and customer datasets to uncover purchasing behaviors and provide actionable insights to the category manager.

Task 2: Experimentation and Uplift Testing Here, you will identify benchmark stores to evaluate the impact of changes on customer sales during a trial period.

Task 3: Analytics and Commercial Application Utilizing insights gained, you will compile a report for the category manager, emphasizing key findings and recommendations.

General Electric — Digital Technology Data Analytics Program Background At GE Aviation, a comprehensive data lake integrates diverse datasets from across the aviation sector, facilitating data-driven insights.

Task 1: Data Engineering Your goal is to consolidate multiple datasets into a single cohesive table, using either Excel or Tableau.

Task 2: Data Visualization You will create various visualizations, including run charts and KPIs, to assist in quality control and decision-making processes.

KPMG Australia — Data Analytics Virtual Experience Program Background Sprocket Central Pty Ltd requires assistance in analyzing customer and transaction data to optimize its marketing strategies.

Task 1: Data Quality Assessment Draft an email to the client detailing data quality issues and proposed mitigation strategies.

Task 2: Data Insights Analyze a list of potential customers to recommend which segments should be targeted based on existing datasets.

Task 3: Data Insights and Presentation Develop an interactive dashboard to visually present your findings and recommendations to the client.

ANZ Australia — Data@ANZ Program Background This program utilizes a synthesized transaction dataset to draw meaningful insights into customer behaviors.

Task 1: Exploratory Data Analysis Load the dataset, assess data quality, and derive overall insights.

Task 2: Predictive Analytics Employ statistical software to identify customer salaries and their correlations with various attributes.

BCG — Data Science & Analytics Virtual Experience Program Background PowerCo, a utility company, seeks to understand and mitigate customer churn.

Task 1: Business Understanding and Hypothesis Testing Formulate a hypothesis regarding customer retention and outline the necessary analytical steps.

Task 2: Exploratory Data Analysis Analyze historical customer and pricing data to verify correlations with churn.

Task 3: Feature Engineering and Modelling Identify and construct features that could be predictive of customer churn, and evaluate a random forest classifier.

Task 4: Findings and Recommendations Compile a concise update for the client, highlighting essential metrics and potential impacts on the business.

This overview illustrates the potential of virtual internships in enhancing data science skills through real-world applications. Engaging in these programs can be a significant step toward achieving your career aspirations in the field.

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