Data Analyst
CurrentData Collection and Extraction: Collect data from multiple sources such as databases, APIs, or spreadsheets. Ensure data is clean, accurate, and reliable by identifying inconsistencies or errors. Use SQL, Python, or other tools to pull data from systems like databases or CRMs. Data Cleaning and Preprocessing: Clean and format raw data to ensure it is ready for analysis. Handle missing values, outliers, and duplicates to ensure integrity. Data Analysis and Exploration: Perform exploratory data analysis (EDA) to identify trends, correlations, and insights. Use statistical methods such as regression, hypothesis testing, and machine learning models to analyze data. Identify key performance indicators (KPIs) for business metrics, Data Visualization and Reporting: Create visualizations (graphs, charts, dashboards) to communicate data insights clearly and concisely. Utilize tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn) to create interactive reports. Generate ad-hoc reports and presentations for business stakeholders, translating complex data into actionable insights.
