I'm a graduate student in the M.S. in Data, Insights & Analytics (MSDIA) program at UW–Madison, building end-to-end analytics solutions that turn messy, real-world data into business decisions. My work blends Python, SQL, cloud computing, machine learning, process automation, and data visualization across healthcare, finance, marketing, manufacturing, transportation, real estate, and HR.
I approach problems like a consultant: clarify the business question, explore the data, model it, and communicate findings through dashboards and executive-ready presentations.
Actively seeking data analyst, data engineer, data science, and BI roles.
📍 Madison, WI · 📧 taranschlichtmann@gmail.com
| Category | Tools |
|---|---|
| Languages | Python, SQL |
| Cloud & Big Data | AWS (S3, EMR, Redshift, SageMaker), Apache Spark |
| Data Warehousing | Snowflake, Redshift, star-schema & dimensional modeling |
| Analytics & ML | Pandas, NumPy, Scikit-Learn (regression, K-Means clustering), statistical hypothesis testing |
| AI / LLM | Snowflake Cortex AI, Databricks AI functions, LLM-powered sentiment analysis & translation, AI agents (rule-based & LLM) |
| Process Mining | PM4Py, Directly-Follows Graphs, Heuristic Miner |
| Automation | Zapier, workflow integration |
| Visualization & Apps | Tableau, Looker Studio, Streamlit, Google Sheets |
| Other | ETL pipelines, ERD design, data storytelling, reproducible workflows |
University of Wisconsin–Madison — M.S. in Data, Insights & Analytics (MSDIA)
A consulting-style series solving end-to-end business problems for Urban Hamster, a fictional apparel retailer — from stakeholder interviews through SQL analysis, machine learning, and executive presentations:
| Project | Description |
|---|---|
| Case Study 1 — Customer Service & Returns | Benchmarked a 10% return rate against the 19.3% industry average and quantified $1.09M in revenue lost to returns; recommended return-window and prepaid-label changes. (Snowflake SQL · Tableau · Python) |
| Case Study 2 — Marketing & Segmentation | Built K-Means customer segments (VIP, growth, light buyers) and applied LLM functions to translate and analyze multilingual reviews. (Snowflake · Scikit-Learn · Databricks AI) |
| Case Study 3 — Finance & Pricing | Analyzed 90.7% YoY sales growth and pricing-policy compliance across 29,120 products, delivered via a Streamlit-in-Snowflake pricing app. (Snowflake SQL · Python · Streamlit) |
| Case Study 4 — Web Operations | Web operations analytics examining site traffic, performance, and operational efficiency to surface optimization opportunities. (Python) |
| Project | Description |
|---|---|
| Redshift Customer Data Warehouse | Star schema warehouse built in AWS Redshift with dimension/fact tables and analytical queries |
| EMR Taxi Data Analysis | Large-scale taxi trip analysis using Apache Spark on AWS EMR |
| SageMaker House Value Prediction | ML regression model trained and deployed on AWS SageMaker to predict housing prices |
| S3 + SageMaker AI | End-to-end ML pipeline using S3 for data storage and SageMaker for model training |
| Redshift Final Project | Capstone Redshift project covering schema design, data loading, and complex SQL analytics |
| Project | Description |
|---|---|
| Healthcare Analytics | BMI, readmission risk, and patient outcome analysis using Pandas and Matplotlib |
| Healthcare Ad Spend Analysis | Predicts sales from TV, radio, and newspaper ad spend using linear regression |
| Real Estate Valuation | Regression analysis of property value drivers including age, location, and amenities |
| Garment Industry Analysis | Manufacturing productivity analysis with feature engineering and statistical modeling |
| Scooter Rental Analysis | Demand forecasting for scooter-sharing using linear regression and usage pattern analysis |
| Taxi Cab Analysis | Profitability assessment of cab vs. rideshare using trip data and statistical tests |
| Printer Warranty Probability | Warranty claim probability modeling using Poisson and Binomial distributions |
A complete progression from fundamentals to advanced analytics:
| Project | Description |
|---|---|
| First SQL Statements | Foundational queries against the TPCDS_SF10TCL database |
| Basic SQL | SELECT, filter, sort, and aggregate queries on TPCH_SF1 and HR databases |
| Joins & CASE | Advanced joins and conditional logic on the CENSUS database |
| Subqueries & CTEs | Nested queries and common table expressions for layered analysis |
| Window Functions | Ranking, running totals, and partitioned aggregations |
| Optimization & Transformation | Query performance tuning and data transformation techniques |
| Final SQL Project | Capstone project synthesizing all SQL skills on a real-world dataset |
| ERD Modeling | Entity-relationship diagrams and schema design using LucidChart |
Applied data warehousing across diverse real-world domains:
| Project | Description |
|---|---|
| Module 1 — Streaming Services | Snowflake warehouse setup and SQL analytics on streaming service subscription data |
| Module 2 — Fitness Gym | Warehouse design and analytics on gym check-in and membership data |
| Module 3 — Electronics Sales | Snowflake analytics on electronics sales and warranty data |
| Module 4 — Advanced Warehousing | Advanced Snowflake data warehouse design and SQL analytics |
| Module 5 — Food Trucks | Food truck sales analytics using window functions, running totals, and Looker Studio dashboard |
| Module 6 — EV Charging | Snowflake analytics on EV charging network data with Cortex AI sentiment analysis |
End-to-end RPA course covering process analysis, scripted automation, AI agents, and workflow integration:
| Project | Description |
|---|---|
| Process Mapping | Swim lane process map and stakeholder analysis of a SaaS onboarding workflow to identify churn drivers and competitive gaps |
| Scripted Automation | Python automation scoring and ranking U.S. counties as clinical trial site candidates using 2020 Census demographic data |
| Workflow Integration | Zapier workflow that automatically extracts, summarizes, and logs mortgage call transcripts using AI, Google Drive, and Google Sheets |
| Process Mining | Insurance claims process mining using Python and PM4Py to discover workflow patterns, detect anomalies, and visualize the claim lifecycle |
| Coding Agents | NimbusPay fraud detection system using rule-based transaction risk scoring and AI-assisted code generation |
| General Agents | AI general-agent workflow for commercial real estate quarterly reviews, analyzing leases, inspections, and tenant communications |
| Final Project | Bolt Socks order-to-cash consulting project: swim lane process map, PM4Py process mining on SAP event data, and Zapier invoice automation |
Core Python programming skills built from the ground up — from control flow to collaborative, production-style workflows:
| Project | Description |
|---|---|
| Week 1 — Control Flow | Conditional logic, loops, and control-flow fundamentals in Python |
| Week 2 — Functions & Modularity | Writing reusable functions and structuring code into modular, maintainable components |
| Week 3 — Errors & Exception Handling | Handling errors gracefully with try/except, raising exceptions, and building robust, fault-tolerant code |
| Week 4 — Data Tools | Working with core Python data tools and libraries for structured data manipulation |
| Week 5 — Data Cleaning & Analysis | Cleaning, transforming, and analyzing datasets with Pandas to prepare data for insight |
| Week 6 — Data Management & Web Data | Managing data and retrieving web-based data through APIs and file formats such as JSON |
| Week 7 — Collaborative & Professional Python | Professional workflows: version control, code collaboration, and writing clean, maintainable Python |
| Final Project | Capstone project synthesizing the full course — data ingestion, cleaning, analysis, and reusable Python code |
| Project | Description |
|---|---|
| Tableau Traffic Analysis | Interactive Tableau dashboard analyzing traffic patterns and incident trends by time and location |
| Tableau Public Dashboard | Published Tableau Public visualization showcasing KPI design and data storytelling techniques |
| Looker Studio Dashboard | Google Looker Studio report with KPI cards, trend charts, and drill-down filters |
| HR Analytics (Google Sheets) | Workforce analytics dashboard built in Google Sheets with pivot tables, charts, and conditional formatting |
| Course |
|---|
| Prescriptive Modeling and Optimization |
| Predictive Modeling |
| Experiments & Causal Methods |
| Pitfalls, Ethics, Communication, and Leadership in Analytics |
| Text Mining and Generation for Analytics |
📧 Email: taranschlichtmann@gmail.com
I'm actively looking for data analytics, data engineering, data science, and BI roles. Open to collaboration and new opportunities.