Parimala Anjanappa
Portfolio
Hi, I’m Pari! 👋
Data Analyst | Data Engineer | Data Science | ML | AI
About Me
I’m an IT professional with experience in Data engineering, Data analytics, Forecating, system integration, and cloud-based solutions. I have a Master of Science degree in Applied Data Science from Indiana University, and I am passionate about data analytics, AI and machine learning. I’m actively seeking opportunities to work on impactful projects as a Data Analyst, Data Engineer, AI/ ML roles.
Technical Skills 💻
Data Analysis & Engineering
- Languages: SQL, Python, R, Java, C, SAS, MATLAB, HTML, CSS
- Databases: SQL, NoSQL, IBM DB, Oracle DB, MS access, Redis, Dynamo DB, Mongo DB, Spark
- Data Visualization: Tableau, Power BI, Plotly, Excel, Qlik
Project Management & Collaboration
- Tools:Jira, Confluence, VS Code, Postman, Linux (Basics), IBM Maximo, SAP Cloud ERP, GitHub Actions
- Methodologies: Agile, Scrum, Waterfall
Certifications
- Career Essentials in Data Analysis by Microsoft
- Microsoft Azure Data Fundamentals
- Coursera IBM ETL and Data Pipelines with Shell, Airflow and Kafka
- Atlassian Agile Project Management Professional
- Power BI for Data Analysts by Microsoft Press
- CSCMP Supply Chain Foundations: Inventory Management Professional Certificate
- The Data Science of Healthcare Medicine and Public Health
- Access Essential Training Office 365Microsoft
- Data Analytics with Microsoft Fabric
- AWS cloud 101 from AWS
- HackerRank SQL, R
- Azure AI services _ Microsoft Learn
- Azure Spark Databricks Essential Training
Projects 🚀
1. Drug Efficacy Prediction Model
- Tools: Data Preparation, ML: random forest, XGboost, Scikitlearn
- Description: Build a machine learning model to predict which chemical compounds can fight HIV effectively, helping researchers focus on the most promising candidates and skip compounds that likely won’t work. Eg: You have 40,000 chemical compounds to test. Instead of testing all in the lab (expensive and slow!), you use AI to predict which might work.
2. MULTI-CLASS GENRE CLASSIFICATION using R for Spotify
- Tools: Modeling - Decision Tree, Random Forest, and XGBoost, Correlation Analysis
- Description: Objective1 : is it possible to classify songs into genres with just audio features \n Objective2 : what can these audio features tell us about the distinctions between genre (Naive Bayes, Decision Tree, KNN)
3. Consumer Sentiment Analysis
- Tools: Python, NLP, scikit-learn, NLTK
- Description: Built sentiment classification models (Naive Bayes, Decision Tree, KNN) on customer feedback with accuracies of 78.66%–79.83%. Preprocessed text data using CountVectorizer, label encoding, and NLTK techniques. Created word clouds, sentiment distribution plots, and confusion matrices to visualize insights.
4. Real Estate Sales Prediction Web Application
- Tools: Python, Machine Learning, Streamlit
- Description: Developed a web app using the Texas housing dataset (txhousing) to forecast sales trends. Enabled user-driven predictions for city-specific and global trends. Integrated EDA, model selection, and real-time output using a Streamlit-based UI.
5. ETL and Data Pipelines with Shell, Airflow and Kafka
- Tools: Shell, Airflow and Kafka
- Description: Extracted and integrated data from SQL queries, APIs, and web scraping using both ETL and ELT approaches. Designed scalable pipelines to feed a centralized data warehouse, improving data accuracy by 25%. Completed hands-on labs using Kafka and Airflow.
6. Walmart Sales Forecasting
- Tools: Python, scikit-learn, Deep Learning
- Description: Predicted weekly sales for 45 Walmart stores using store-level sales data, holiday indicators, and economic factors. Performed data cleaning, feature engineering, and model tuning using Gradient Boosting, Decision Trees, and Deep Neural Networks, improving forecasting for inventory and promotion planning.
7. Crop Row Detection Using U-Net
- Tools: TensorFlow, Deep Learning, Image Segmentation
- Description: Designed a U-Net based segmentation model to detect crop rows from aerial field images. Optimized performance using IoU (Intersection over Union) and improved image preprocessing and annotation strategies.
8. Midwest Demographic & Socioeconomic Analysis
- Tools: R, Data Visualization, R shiny, Statistics
- Description: Explored county-level Midwest U.S. census data to identify patterns in income, education, and population distribution. Presented findings through statistical summaries and visualizations to uncover regional disparities.
Experience 💼
Data Assistant | Indiana University | Indianapolis | Apr 2023 – Jun 2025
- Tools: SQL, DBMS, DLSG softwares, Freeflow, Metadata, Batch Processing, ETL, Data warehouses, Delta lakehouse, Schema design.
Data Engineer/Data Analyst | Netcube Technologies | Bangalore, India | Jan 2019 – Feb 2022
- Tools: SQL, Azure, Apache Airflow, GitHub, Restful APIs, Flask, ETL/ELT, CI/CD pipelines, SQL, NoSQL, Data warehouses, Delta lakehouse, SAP ERP, Tableau, Power BI.
Associate Software Engineer | Tech Mahindra | Bangalore, India | Aug 2016 – Oct 2018
- Tools: Oracle DB, HP ALM, Python, Automation testing scripts, Data warehouses, Litmus Magix, Selenium.
Education 🎓
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Master of Science in Applied Data Science Indiana University Indianapolis Jan 2023 – May 2024 Dean’s Scholarship Recipient - Coursework: Data Analytics using Python and R, Data Visualization, Deep Learning, Cloud Computing, DBMS, Statistics
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Bachelor of Engineering Mechanical, Aeronautics Mangalore Institute of Technology and Engineering, VTU, India
Let’s Connect! 🌐
I’m open to collaborating on interesting projects or discussing new opportunities. Feel free to reach out!
- 📧 Email: parimala.js27@gmail.com
- 🔗 LinkedIn: LinkedIn Profile
- 🔗 Portfolio: Portfolio
- 🔗 Github: Github
- 🔗 Rpubs: link
- 🔗🔗 Medium