Wishing to kick start your career as a junior data scientist?
One of the most crucial tools that will help you realize your dream to land a job as a data scientist is having a data scientist resume that showcases your skills and knowledge.
Even if you don’t have significant experience as a professional, you can still make an impressive data scientist resume as a fresher by emphasizing your skills, certifications, internship experience, and projects.
Plus, with the booming demand and job opportunities for data scientists which are projected to grow by 36% by 2031, now is a good time to build a stellar resume and send out those applications.
In this blog, we will show you what a perfect entry-level data scientist resume looks like, along with pointers on why it works well so that you can use it as a reference to build your own.
Entry-level Data Scientist Resume Example
- Course Modules:
- Data Science Life Cycle |Common terminologies of Data Science| Basics of databases using SQL | Machine Learning
- Problem Formulation and Framework | Hypothesis Building and Framework | Excel Basics, Formulas and Functions
- GPA: 4.5
- Collecting and analyzing data using Tableau to identify user's purchase patterns for supporting business decisions
- Maintaining report dashboards to reduce data processing time and monitor key performance indicators
- Migrating server data from legacy system to a unified database to unify disparate data and reduce costs by 20%
- Analyzing survey data for ensuring data integrity to increase business efficiency, mitigate risks, and increase revenue
- Assisted the technical team of 10+ professionals to develop advanced analytics models for improving products & services
- Collaborated with senior data scientists to deploy machine learning models to evaluate POS tagging using data mining
- Researched and mined data to support database for streamlining the data visualization process
- Coordinated with 5 junior data scientists to plan and develop presentations for senior executives & external stakeholders
- GitHub Swagger UI Analysis | GitHub | Nov '21
- Predicted the programming language of a repository with 71% accuracy via NLP given the context of the Swagger UI file
- Employed an API to bring in the Swagger UI files and programming language of each repository
- Utilized techniques such as lemmatizing, tokenizing, removing non-ASCII characters, and dropping outliers
- Data Sentiment Analysis for Infosys | GitHub | Mar '21
- Estimated the ROI of the online marketing efforts of Infosys to help boost sales and increase revenue
- Utilized Natural Language Processing (NLP) to extract positive, negative, and neutral sentiments from textual data
- Executed sentiment analysis on textual data to monitor brand and product sentiment for understanding customer needs