Data Analysis and Manipulation:
● Proficiency in tools like Microsoft Excel (including advanced functions like pivot tables, VLOOKUP, and macros).
● Strong skills in querying and managing data using SQL. Data Visualization:
● Creating dashboards and visualizations using tools like Tableau, Power BI, or Looker.
● Using Python (Matplotlib, Seaborn) or R (ggplot2) for customized visualizations. Programming Languages: ● Python (libraries like Pandas, NumPy) or R for statistical analysis and automation. Database Management: ● Experience working with relational databases (MySQL, PostgreSQL, SQL Server) and NoSQL databases (MongoDB). Statistical Software: ● Familiarity with tools like SAS, SPSS, or Stata for advanced statistical modeling. ● Descriptive Statistics: Understanding measures like mean, median, standard deviation, and variance.
● Inferential Statistics: Conducting hypothesis testing, confidence intervals, and regression analysis.
● Probability Theory: Applying probability concepts in predictive modeling and risk assessment. Attention to Detail: Ensuring accuracy in data analysis and reporting. Collaboration: Working with cross-functional teams (e.g., IT, marketing, finance) to gather data and share insights. Time Management: Prioritizing tasks and meeting deadlines for data projects. Data Storytelling: Presenting data findings in a clear and compelling way for non-technical stakeholders. Report Writing: Creating detailed and concise reports summarizing analysis and insights. Presentation Skills: Effectively communicating results and recommendations through visuals and narratives