Free Data Scientist Intern Resume

Objective

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. As a dedicated learner, I aim to acquire practical experience in extracting valuable insights from complex datasets, refining predictive models, and enhancing decision-making processes. Through this internship, I aspire to develop a strong foundation in data-driven problem solving while actively contributing to the growth and success of the organization.

Education

BSc Computer Science, MSc Computer Science

Skills

Skills relevant to Data Science Intern Resume

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

Projects

Sample Data Science Project Ideas

Project 1. Customer Churn Prediction for a Telecom Company

Project Idea: Develop a predictive model to identify customers at risk of churning (canceling their subscriptions) for a telecom company. Analyze customer data and usage patterns to predict which customers are likely to leave, enabling the company to take proactive retention measures.

Quantified Results:

Achieved a predictive accuracy of 85% in identifying churned customers.
Reduced customer churn rate by 15% over a six-month period after implementing retention strategies based on model predictions.

Skills Demonstrated:

Machine Learning:
Employ machine learning algorithms (e.g., logistic regression, random forests) for predictive modeling.

Data Preprocessing:
Clean, transform, and preprocess customer data.

Feature Engineering:
Create relevant features, such as customer lifetime value (CLV) and usage patterns.

Data Visualization:
Present insights and model results through data visualizations.

Model Evaluation:
Use metrics like accuracy, precision, recall, and ROC AUC to assess model performance.

Communication:
Clearly communicate findings and recommendations to stakeholders.

Project 2. Sentiment Analysis for Product Reviews

Project Idea: Perform sentiment analysis on product reviews from an e-commerce platform to gain insights into customer opinions and identify areas for product improvement. Categorize reviews as positive, neutral, or negative based on sentiment.

Quantified Results:

Achieved an accuracy of 88% in sentiment classification of product reviews.
Identified key product features that received the most positive and negative feedback.

Skills Demonstrated:

Natural Language Processing (NLP):
Utilize NLP techniques to preprocess text data and perform sentiment analysis.

Text Classification:
Implement machine learning models for text classification (e.g., Naive Bayes, LSTM).

Feature Extraction:
Extract relevant features from text data, such as word frequency or TF-IDF.

Data Visualization:
Visualize sentiment trends and key findings using word clouds or bar charts.

Data Cleaning:
Clean and preprocess unstructured text data.

Statistical Analysis:
Conduct statistical analysis to uncover correlations between sentiment and product attributes.

Report Writing:
Summarize findings and insights in a clear and actionable report.

These projects demonstrate your proficiency in data science, machine learning, data analysis, and communication.

They will showcase to your potential employers your ability to tackle real-world problems, make data-driven decisions, and provide actionable recommendations to improve business outcomes. When presenting these projects to potential employers, emphasize the impact your work had on the company or organization, such as improved customer retention or product enhancement.

Interests/Hobbies

Reading about AI / ML advancements, exploring new ML Algorithms and Large Language Models and reading about leadership.

Experience

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.

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

Additional Inputs

Top Free Data Science Podcasts

Learning Data Science does not have to cost you any money. There are lots of free resources such as podcasts with great content and absolutely free, being run by enthusiastic and kind-hearted data science professionals.

Data Skeptic
Web Address: https://dataskeptic.com/
Description: Data Skeptic is a podcast that explores various topics in data science and machine learning. It covers everything from introductory concepts to more advanced techniques.

Not So Standard Deviations
Web Address: http://nssdeviations.com/
Description: Hosted by Hilary Parker and Roger D. Peng, this podcast discusses data science, statistics, and the everyday challenges data scientists face.

Data Science at Home
Web Address: https://datascienceathome.podbean.com/
Description: Data Science at Home is dedicated to the topic of machine learning and artificial intelligence. The podcast explores various aspects of AI, including practical applications and ethical considerations.

Linear Digressions
Web Address: http://lineardigressions.com/
Description: Linear Digressions is a podcast that delves into the world of data science, machine learning, and artificial intelligence. It covers both technical and non-technical aspects of the field.

DataFramed
Web Address: https://www.datacamp.com/community/podcast
Description: DataFramed is the official podcast of DataCamp. It features interviews with data scientists, industry experts, and thought leaders discussing various data science topics.

Partially Derivative
Web Address: http://partiallyderivative.com/
Description: Partially Derivative is a podcast that blends data science with humor. It covers data-related news, trends, and interviews with data professionals.

Talking Machines
Web Address: https://www.thetalkingmachines.com/
Description: Talking Machines is a podcast that explores the world of machine learning. It includes interviews with researchers, discusses recent developments, and provides insights into the field.

O'Reilly Data Show
Web Address: https://www.oreilly.com/topics/oreilly-data-show-podcast
Description: The O'Reilly Data Show podcast features interviews with data science and machine learning experts. It covers emerging trends, technologies, and best practices.

Becoming a Data Scientist
Web Address: http://www.becomingadatascientist.com/category/podcast/
Description: Becoming a Data Scientist is a podcast by Renée Teate that explores the journeys of data scientists from various backgrounds. It provides insights into their career paths and experiences.

Data Science Imposters
Description: Data Science Imposters is a podcast that takes a humorous approach to discussing data science topics. It's a fun and informative resource for newcomers to the field.