Sai Erra

Results-driven Data Analyst with experience at Milinium, specializing in developing impactful dashboards and visualizations using Tableau. Proficient in Python and SQL, I leverage analytical thinking to enhance business metrics tracking, driving informed decision-making and optimizing performance.

Prediction of Cardiovascular Disease

Objective: Engineered and deployed machine learning classification models to accurately predict cardiovascular disease risk using patient clinical data

Data Science Process: Conducted data preprocessing, feature engineering, and statistical analysis on health indicators, including blood pressure, cholesterol, glucose levels, BMI, and lifestyle factors.

Model development: evaluated and optimized multiple algorithms (e.g., logistic regression, random forest, gradient boosting) to maximize recall score for early disease detection

Impact: Delivered a data-driven decision support system empowering healthcare professionals to perform predictive analytics and enable proactive patient care

2. Exploratory data analysis on the FIFA player dataset

Objective: Conducted in-depth data cleaning, preprocessing, and exploratory data analysis (EDA) to uncover patterns in professional football player attributes and market values

Data Wrangling: Performed feature selection, data type conversions, string manipulations, and missing value imputation for numerical and categorical variables.

Analysis and visualization: utilized Seaborn and Matplotlib to generate distribution plots, pair plots, and correlation insights for player performance metrics (overall rating, value, wage, reputation, height, weight, release clause)

Impact: Delivered actionable insights into player valuation trends to support data-driven scouting and transfer decisions

3. COVID-19 data analysis with Python, used Pandas and Matplotlib to analyze global COVID-19 trends from open-source datasets, performed EDA, cleaned data, and visualized infection and death rates by country, created a summary report with actionable insights and graphs SQL dashboard for movie ratings (MySQL + Tableau), wrote complex SQL queries to aggregate movie ratings and user demographics, and visualized results in Tableau to showcase top-rated genres, user preferences, and review trends

● Machine Learning (Supervised & Unsupervised Learning) – Great Learning

● Statistics for Machine Learning – Great Learning

● Exploratory Data Analysis using Python – Great Learning

● Introduction to Python Programming – Great Learning

Database Management Systems – Great Learning

1. Post Graduate Programme in Data Science and Engineering| | Great Learning Academy

 Used Python libraries like NumPy, Pandas, Seaborn, and Matplotlib to learn data analysis methodologies and obtain practical expertise in gaining insights and making data-driven decisions from large datasets.

Gained experience in creating and refining SQL queries, executing join operations in MySQL, and managing data to improve the effectiveness of information retrieval from relational databases

Studied Power BI to produce efficient data visualizations, developing my design and dashboard-building skills to effectively convey data to visuals

2. Programming languages and data structures

Learned about input/output, variables, constants, and control structures like loops and conditionals in Python

Studied advanced programming ideas such as file operations, dynamic data management using linked lists and pointers, function design, and arrays

Applied algorithms for searching, such as binary search, and for sorting, such as merge sort, heap sort, bubble sort, etc

Solving HackerRank Python problems and gaining practical experience throughout the process, interests and hobbies include chess and speedcubing