UTKARSH ANAND

Analytical and detail-oriented Data Analyst with comprehensive training across SQL, Python, Excel, and Power BI, supported by multiple data-driven academic and industry projects. Skilled in transforming raw datasets into actionable insights through data cleaning, visualization, and predictive modeling. Proficient in statistical analysis, DAX measure creation, and dashboard development for clear, executive-level decision support.

Certified in Data Science, Machine Learning, NLP, and Business Intelligence, with hands-on experience applying these skills to real-world datasets — including retail sales optimization (KPMG-style Excel project), churn prediction, price forecasting, and sentiment classification. Demonstrates strong communication, ethics, and a commitment to precision, consistently delivering well-structured, reproducible analyses that drive measurable impact.

Introduction to Data Analytics

Passionate Data Analyst with expertise in SQL, Python, Excel, and Power BI, experienced in data analysis, business intelligence, and visualization. Adept at transforming raw data into actionable insights, with internship experience at Tata Steel in data-driven decision-making and project management. Certified in Data Analytics, SQL, and Machine Learning, with strong skills in problem-solving, teamwork, and delivering measurable results.

DSA Upskilling — Independent

Data Analytics Study Sprint

GenAI & Prompt Engineering Practice — Independent

Cybersecurity Club, KIIT University

Deep Learning on CIFAR-10 (August 2025 - September 2025)

• Designed a lightweight CNN (~316k params) with dropout; achieved ~66% test accuracy as a vision baseline.
• Compared optimizers (Adam/RMSprop/SGD) and visualized learning curves to guide training choices.
• Organized results (tables/plots) for quick reproducibility.

IMDb Sentiment Analysis (NLP) (August 2025 - August 2025)

• Created TF-IDF text features and trained Logistic Regression achieving ~88% accuracy to classify movie reviews.
• Surfaced top positive/negative terms to inform campaign messaging and content moderation.
• Delivered a batch-scoring notebook for fast, repeatable inference.

News Article Classification (10-Class NLP) (August 2025 - August 2025)

• Developed headline+text pipeline with TF-IDF and linear models; benchmarked alternatives (best ~64% accuracy).
• Mapped confusions (Politics ↔ World) and proposed transformer upgrade path to lift editorial triage accuracy.
• Implemented 5-fold CV and reports to stabilize evaluation across categories.

Customer Churn Prediction(Machine Learning) (July 2025 - August 2025)

• Built churn model with Logistic Regression (AUC ≈ 0.83) after encoding and scaling a Telco dataset to flag at-risk customers.
• Designed threshold/metric tuning (ROC/PR) to balance recall vs. precision for cost-effective retention.
• Identified key churn drivers (contract/payment/charges) to prioritize targeted offers.

Airbnb Price Prediction (Machine Learning) (July 2025 - July 2025)

• Engineered features and trained a linear model on log-price (R² ≈ 0.60, MAE(log) ≈ 0.32) to estimate fair listing prices across cities.
• Analyzed coefficients to explain drivers (accommodates, room/property type, bedrooms), enabling transparent pricing guidance.
• Packaged a clean notebook + script for repeatable scoring, streamlining experimentation.

EV Data Analysis & Recommender (May 2025 - June 2025)

• Filtered EVs by budget/range and generated a recommender (e.g., Tesla Model 3 LR), giving buyers a data-driven shortlist.
• Validated performance differences using a Welch t-test; charted battery-capacity vs range to support purchase trade-offs.
• Presented concise visuals to speed stakeholder decisions.

Walmart Sales SQL Analysis (April 2025 - May 2025)

• Authored advanced SQL (CTEs, window functions) to analyze growth, weekday seasonality, anomalies, and payment mix.
• Discovered Friday peaks and e-wallet preference, informing staffing and promo timing.
• Created reusable views/queries to accelerate future analytics.

Airline Operations Dashboard (March 2025 - April 2025)

• Modeled flights/tickets and built a Power BI dashboard with DAX KPIs and slicers to monitor on-time, delay, and cancellation trends.
• Implemented Row-Level Security for role-based access, enabling secure sharing across teams.
• Highlighted destination/airline patterns to optimize schedules and services.

Customer Analytics & Business Growth (Segmentation, Seasonality & CLV) (February 2025 - March 2025)

• Developed customer segmentation (Affluent/HNW/Mass) from income×spend, industry, and demographics to target metros and younger users.
• Created seasonality & product-mix analysis to time promos, balance premium margins vs high-frequency SKUs, and align inventory.
• Designed CLV tiers to prioritize high-value cohorts for white-glove retention, loyalty rewards, and personalized campaigns.
• Developed new-customer onboarding insights highlighting urban/digital channels to raise first-to-repeat conversion.
• Created regional revenue mapping to focus expansion in high-potential metros and de-risk rollouts via phased pilots.
• Revamped executive reporting into concise recommendations and KPIs, accelerating decisions on marketing, bundles, and stock.

JP Morgan–Style Legal Document Classification (January 2025 - January 2025)

• Developed an end-to-end CRISP-DM pipeline to classify legal contracts (loan, CDS, custody), reducing review time and errors.
• Created OCR + text-cleaning to convert PDFs/scans/Word into machine-readable text; normalized structure and fixed OCR noise.
• Designed a clause taxonomy (repayment, default, interest) and labeled data; split train/validation/test for unbiased evaluation.
• Built a hybrid approach: rules + ML (TF-IDF/LogReg, Random Forest, BERT) to capture templates and generalize to varied language.
• Implemented metrics (Accuracy, Precision, Recall, F1) and real-world validation to guard against overfitting.
• Evaluated k-means clustering to discover new clause families and prioritize labeling backlogs.
• Deployed an integration plan with the document-management system, feedback loop, and drift monitoring; proposed clause highlighting and risk flags to speed legal triage.

Data Analysis & Visualization — Tata Steel (September 2025 - October 2025)

• Developed automated Excel/Power BI dashboards connected to large log datasets, reducing manual reporting effort and giving managers daily KPI visibility.
• Created Python/SQL data-cleaning pipeline (validation, type fixes, de-dup) to improve data accuracy and consistency across reports.
• Translated patterns into clear recommendations (bottlenecks, error spikes), supporting workflow improvementsand better decision-making.

Certified in Data Science with hands-on projects on Airbnb pricing, customer churn, IMDb sentiment analysis, and Power BI dashboards. Also created educational training videos to explain my work in detail and can share links to these on request.

Certified in Cloud Architecture Simulation Project, Cybersecurity Analysis Project, and Cybersecurity Skills Certification from IBM, Coursera.

Certified in Prompt Engineering (Vanderbilt); Generative AI—Introduction & Applications (IBM); Operating Systems—Overview, Administration & Security (IBM); Machine Learning for All (University of London).