Entry Level Data Scientist Resume Brianna Kramer Entry Level Data Scientist (608) 555-1842 brianna.kramer@example.com linkedin.com/example/briannakramer Madison, WI 53703 STRENGTHS Analytical reasoning In capstone work, careful comparisons of models and assumptions helped turn large datasets into useful findings. Reproducible workflows Documented notebooks, data dictionaries, and lineage notes made project decisions easier for peers and faculty to review. Clear communication Technical presentations translated model tradeoffs and errors into language that mixed audiences could follow. Practical experimentation Research projects compared validation behavior, representative errors, and cluster meaning before drawing conclusions. Collaborative learning Peer review and faculty supervised work created space for questions, shared feedback, and steady improvement. SKILLS Python Machine Learning Artificial Intelligence Exploratory Data Analysis Data Preprocessing Data Visualization NLP Deep Learning Model Evaluation Statistical Analysis Problem Solving Written Communication Reading Comprehension Cloud Computing Data Collection LANGUAGES English Native 40 Spanish Intermediate 20 MY CAREER 1.2 Years Data Scientist Capstone Student, Analytics Workflow at Capstone Project (8 Months) Machine Learning Research Student, Text and Image Modeling at University Research Lab (3 Months) Applied Data Analytics Student, Public Dataset Study at Course Project (4 Months) SUMMARY Data science student building practical solutions through machine learning , artificial intelligence , exploratory data analysis , and clear visualization. Academic projects have covered structured and unstructured data, automated collection, preprocessing, regression, classification, clustering, natural language processing, and image classification. Python work with NumPy, Pandas, Scikit learn, Seaborn, Plotly, TensorFlow, Tableau, and Excel has supported repeatable analysis and thoughtful model comparison. Experience with Microsoft Azure and Amazon Web Services adds a useful cloud foundation. Careful documentation, data quality checks, validation review, and plain language presentations have helped mixed audiences follow technical decisions. Collaborative research and capstone work strengthened analytical reasoning, written communication, reading comprehension, and attention to detail. Ready for an entry level data science setting where curiosity, steady learning, and practical analysis can support sound decisions. EDUCATION Bachelor's Degree in Computer Science University of Wisconsin Madison GPA: 4.0 2027 Madison, WI Coursework: Machine Learning, Artificial Intelligence, Data Mining, Statistical Analysis, Database Systems TECHNICAL SKILLS Programming Languages: Python, Python scripting, Jupyter Notebooks Data Analysis Libraries: NumPy, Pandas, data preprocessing Machine Learning: Scikit learn, Linear Regression, Logistic Regression Classification Models: Decision Tree, Random Forest Classifier, Naive Bayes Advanced Models: Support Vector Machine, clustering, model evaluation Visualization Tools: Seaborn, Plotly, Tableau Business Spreadsheet Tools: Microsoft Excel, data visualization, chart design Deep Learning: TensorFlow, image classification, neural networks Natural Language Processing: NLP, text classification, unstructured data Cloud Platforms: Microsoft Azure, Amazon Web Services, cloud storage Data Engineering: automated collection, data lineage, schema checks Analytics Methods: EDA, exploratory analysis, error analysis SKILLS Python Machine Learning Artificial Intelligence Exploratory Data Analysis Data Preprocessing Data Visualization NLP Deep Learning Model Evaluation Statistical Analysis Problem Solving Written Communication Reading Comprehension Cloud Computing Data Collection EXPERIENCE Data Scientist Capstone Student, Analytics Workflow Capstone Project January 2026 - September 2026 Madison, WI Led a semester scale analytics workflow for a mixed format dataset, shaping data quality checks, reproducible modeling, validation review, visualization, and final communication for a capstone audience. Defined prediction goals for a 120,000 row dataset and documented quality checks, preprocessing, evaluation criteria Built reproducible Python pipelines with Pandas and NumPy for missing values, encoding, and standardization Compared regression, classification, and support vector models, recording validation results and practical limitations Used Seaborn and Plotly for EDA , then translated distributions and relationships into Tableau views Tested clustering methods and assessed whether segments added useful context beyond supervised modeling Presented model tradeoffs and error analysis to 14 peers with organized notebooks and data dictionaries Machine Learning Research Student, Text and Image Modeling University Research Lab September 2025 - December 2025 Madison, WI Supported faculty supervised research across text classification and image modeling, using documented experiments, cloud resources, evaluation review, and concise communication of findings. Investigated text classification across a 9,500 document corpus with repeatable preprocessing and baseline models Prepared unstructured text in Python and Pandas while preserving raw data lineage and feature generation notes Reviewed precision, recall, confusion matrices, and representative errors across Scikit learn classifiers Prototyped TensorFlow image classification and documented training settings, validation behavior, and misclassified examples Used Microsoft Azure resources for controlled experiments and reproduced a smaller workflow on Amazon Web Services Produced a Plotly report connecting model results with research questions and measured conclusions Applied Data Analytics Student, Public Dataset Study Course Project January 2025 - May 2025 Madison, WI Completed an end to end public dataset study, combining collection scripting, data checks, exploratory analysis, baseline modeling, visualization, and presentation for a course deliverable. Created Python collection scripts for a 35,000 record dataset, then assessed source quality and structure Built checks for duplicates, missing fields, unexpected values, and schema consistency before analysis Established regression and classification baselines with Scikit learn and compared assumptions before method selection Explored clustering stability and practical interpretation instead of treating algorithm output as a final conclusion Developed Tableau and Excel visuals for nontechnical readers alongside Seaborn figures for analytical review Delivered a documented repository and presentation that supported reproducibility, clear writing, and careful reasoning PROJECTS Mixed Format Predictive Analytics 2026 Built a documented predictive workflow for a large mixed format dataset, combining preprocessing, model comparison, clustering, EDA, Tableau storytelling, and peer review. Cloud Based Text and Image Study 2025 Connected NLP and TensorFlow image classification experiments with Azure and AWS practice, giving research questions a clear and measured analytical response. LEADERSHIP & AWARDS Dean's List, 2025 and 2026 Undergraduate Data Analytics Capstone Recognition, 2026 Academic Excellence Scholarship, 2025 CERTIFICATIONS Google Advanced Data Analytics Professional Certificate 2026 Microsoft Learn Azure Fundamentals Learning Path 2026 AWS Academy Cloud Foundations 2025 PROFESSIONAL AFFILIATIONS Data Science Student Association, Peer Project Reviewer, 2025 - 2026 Undergraduate Research Forum, Student Presenter, 2026 Campus Analytics Challenge, Team Participant, 2025 LANGUAGES English (Native) Spanish (Intermediate) ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. REFERENCES AVAILABLE ON REQUEST
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