Programming & Automation:
Python (data cleaning, automation scripts, QA automation), SQL (advanced querying, validation logic, troubleshooting lead‑flow systems)
Data Analysis & Modeling:
Exploratory Data Analysis (EDA), statistical testing, KPI analysis, root‑cause analysis, anomaly detection, data quality assessment
Machine Learning Foundations:
Regression & classification basics, feature engineering, model evaluation, predictive insights for operational improvements
Deep Learning (Foundational):
Neural network fundamentals, ANN concepts, model tuning basics, overfitting/underfitting understanding
NLP (Applied):
Text preprocessing, tokenization, TF‑IDF, embeddings (Word2Vec/BERT‑style), classification, sentiment analysis for data‑review workflows
Generative AI:
Large Language Models (GPT‑based), prompt engineering, workflow acceleration using GenAI, requirement translation, summarization, QA support automation
Data Visualization & BI:
Power BI (DAX, drilldowns, performance dashboards), Tableau (interactive dashboards), Excel (Pivot Tables, Power Query, VBA macros)
Databases:
MS SQL Server, MySQL, Oracle, PostgreSQL, MongoDB
Tools & Platforms:
Jupyter Notebook, Git/GitHub, Jira, Eloqua (Marketing Automation)
Data Operations & Quality:
Data‑flow validation, end‑to‑end QA testing, Python‑SQL automation, ETL/ELT understanding, process optimization