Data Analyst
Statistical Analysis and Hypothesis Testing:Mastered a range of statistical tests including t-tests, ANOVA, and chi-squared tests using Python.Conducted power analysis to determine sample sizes for experiments, ensuring robust results.Data Cleaning and Pre-processing:Became proficient in handling missing data, outliers, and inconsistencies, ensuring data integrity.Utilized tools like Pandas in Python for data transformation and manipulation.Exploratory Data Analysis (EDA):Conducted detailed data exploration to identify trends, patterns, and anomalies in datasets.Used visualization libraries like Matplotlib and Seaborn to create insightful plots and graphs.Predictive Modeling and Machine Learning:Trained and evaluated various machine learning models including linear regression, decision trees, and clustering algorithms.Applied cross-validation techniques and optimized models using grid search and random search methods.Advanced SQL Queries:Executed complex SQL queries to extract, manipulate, and analyze data from relational databases.Practiced optimizing queries for performance and worked with large datasets in a database environment.Data Visualization and Reporting:Leveraged tools like Tableau to create interactive dashboards and reports.Learned to effectively communicate insights to both technical and non-technical stakeholders.Big Data Tools and Frameworks:Gained hands-on experience with Hadoop and Spark to process and analyze large datasets.
