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Fresh Graduate Internship Resume

Data Scientist Intern


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Motivated and analytical Data Science enthusiast seeking an internship position to apply and expand my knowledge of data analysis, machine learning, and statistical modelling. Eager to contribute to real-world projects, leverage advanced data manipulation techniques, and collaborate with a team of experienced professionals.


BSc – Computer Science
2015 - 2018
Sri Venkateshwara College


• Statistical Analysis • Machine Learning • Data Cleansing and Pre-processing • Data Visualization • Problem-solving • Communication • Natural Language Processing


Customer Segmentation and Marketing Strategy


Analyze customer data from a retail or e-commerce company to segment customers based on their purchasing behavior, demographics, and preferences.

• Data cleaning and pre-processing.
• Apply clustering algorithms (e.g., K-means) to segment customers.
• Develop customer personas based on the segments.
• Develop a predictive maintenance model using time series analysis and machine learning.
• Propose targeted marketing strategies for each customer segment.

Improved marketing campaigns with higher conversion rates and personalized recommendations for customers, leading to increased sales.


Reading magazines on AI/ML, reading latest advancements in AI/ML, leadership podcasts and following stock market news.


Data Science Intern
Outworx Solutions, Pune | May 2023 - August 2023

• Assisted in data collection, cleaning, and pre-processing.
• Conducted EDA to uncover data insights and patterns.
• Collaborated on machine learning projects for predictive analytics.
• Contributed to feature engineering and model optimization.
• Used Python, pandas, scikit-learn, and Matplotlib for analysis.
• Improved customer churn prediction model by 10%.
• Enhanced a recommendation system, increasing engagement by 15%.
• Reduced forecasting errors by 20% in time series analysis.


Python, Pandas, scikit-learn, Matplotlib, SQL, Git

Additional Inputs

As a fresh college graduate looking to learn data science, there are many free resources available to help you get started and build your skills. Here are some of the best ones:

Online Courses and Tutorials:

Coursera: Offers free courses from top universities and institutions. You can audit many courses for free.

edX: Provides free courses from universities worldwide, including Harvard and MIT.

Kaggle: Offers free courses and tutorials on data science and machine learning, along with datasets and competitions to practice.

DataCamp: Provides free introductory data science courses and interactive coding challenges.

YouTube Channels:

Channels like Data School, Corey Schafer, and StatQuest offer free tutorials on data science concepts and programming languages like Python.

Many classic data science books are available for free online, such as "Python for Data Science Handbook" by Jake VanderPlas or "Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

Blogs and Medium Articles:

There are many data science bloggers and authors who share their knowledge and insights for free on platforms like Medium. Some popular data science blogs include Towards Data Science and Simply Statistics.

MOOCs (Massive Open Online Courses):

Websites like Stanford Online and MIT OpenCourseWare offer free access to course materials from top universities, including data science-related courses.

GitHub Repositories:

Explore GitHub repositories that provide code examples and projects related to data science. You can find datasets and code to analyze them.

Data Visualization Tools:

Tools like Tableau Public and Power BI offer free versions for data visualization practice.

Data Science Communities:

Join online communities and forums like Stack Overflow and Reddit's r/datascience to ask questions and learn from experienced data scientists.

Practice Projects:

Start your own data science projects or contribute to open-source projects. GitHub is a great place to find open-source data science projects to collaborate on.


Listen to data science podcasts like "Data Skeptic" and "Not So Standard Deviations" for insights and discussions on data science topics.

LinkedIn Learning:

Some LinkedIn Learning courses are available for free through your university or workplace. Check if you have access to them.

Free Coding Platforms:

Platforms like Jupyter Notebook, Google Colab, and RStudio provide free environments for coding and experimenting with data science tools.

Remember that consistent practice and hands-on experience are key to becoming proficient in data science.

Try to work on real-world projects and build a portfolio to showcase your skills to potential employers. Additionally, consider networking with professionals in the field through LinkedIn and attending local data science meetups or virtual conferences to stay updated on industry trends and job opportunities.