Data Science Mental Health

MANIDEEP ANNARAPU

+1-940-***-**** ****************@**.***.*** Portfolio-website Dallas, TX.

EDUCATION

Master of Computer Science, (GPA: 4.0) Jan 2023 - May 2024 University of North Texas - Denton,Texas.

Coursework: Big Data and Data Science, Machine Learning, Fundamentals of Database, Analysis of Computer Algo- rithms, Software Engineering, Information Retrieval, Computer Architecture, Computer Networks, Data Analytics. Bachelor of Technology - Computer Science, Jun 2018 - Aug 2022 St. Martin’s Engineering college - Hyderabad, India TECHNICAL SKILLS

Languages & Libraries : Python, SQL, R, Java, TensorFlow, Pandas, MatplotLib, Scikit-Learn, Numpy, PyTorch. Data Engineering: Hive, Apache Spark, Scala, HDFS, ETL development, Data Management Tools : Tableau, Power BI, Microsoft Excel, SQL Server, Linux, Git Hub, CI/CD, AWS, JIRA EXPERIENCE

Teaching Assistant, University of North Texas, Denton, TX Aug 2023 - May 2024

• Assisted in the instruction of CSCE-4110 Data Structures and Algorithms, helping over 100 students improve their understanding of key concepts such as divide and conquer, problem-solving skills and dynamic programming. Summer AI Research, University of North Texas, Denton, TX May 2023 - Jul 2023

• Implemented dimensionality reduction techniques by reducing data dimensions by 50% while maintaining 95% of the original information by generating neural receptive fields and visualizing them from the sensory data. Data Scientist, Onlane Solutions Pvt Ltd, Hyderabad, India Jan 2021 - Dec 2022

• Worked as part of team to build predictive models to support various business functions, including fraud detection and customer segmentation. Utilized Python (scikit-learn, pandas, numpy) to develop and automate predictive modeling processes. Created and maintained scalable ML pipelines and wrote production-grade Python code. Intern, Value Labs, Hyderabad, India Mar 2022 - Jun 2022

• Automated testing processes using the TestNG framework and Selenium, reducing manual testing efforts by 70%. PROJECTS

BCG Data Science Job Simulation on Forage — AI, Python, ML models, Data Analysis Jun 2024

• Completed a customer churn analysis simulation, demonstrating advanced data analytics skills, identifying essential client data through requirements gathering and outlining a strategic approach. Conducted efficient data analysis and data transformation using Python. Employed data visualization techniques for trend interpretation.

• Completed the engineering and optimization of a random forest model, achieving an 85% accuracy rate in predicting customer churn. Completed a concise executive summary for the Associate Director. Prediction and Global Analysis of Mental Health — Python, CNN, TensorFlow, Jupyter, Feb 2024

• Developed a comprehensive predictive model for analyzing mental health indicators using deep learning neural networks CNNs achieving an accuracy of 92% by analyzing global trends in mental health.

• Integrated deep learning methodologies, providing insights into factors influencing mental health on a global scale with the usage of strong Python coding experience to preprocess and clean large datasets, ensuring data precision, efficiency, uniformity, and dependability for model training, resulting in a 50% reduction in data processing time. Forecasting customer buying products using big data and data science Nov 2023

• Worked on analyzing and presenting the customer buying habits to enhance customer experience by recommenda- tion. Employed efficient ETL pipelines, storing datasets in Hadoop HDFS and using Hive for analysis, demonstrating distributed and large-scale data processing capabilities, harvesting power of the big data to grow digital sales.

• Used Pyspark for analytics and integrated Spark with HDFS, achieving a 25% improvement in query performance. Applied Random Forest for classification, resulting in a 15% increase in model accuracy. Med-Bot — Python, Django, LLM, GenAI, GitHub, JSON Nov 2023

• Built a chatbot using Django and Python that utilizes natural language processing with pretrained BERT (LLM), achieving a response accuracy of 90%.

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