Data Analyst

Skills and Responsibilities:

Technical Proficiency:

• Python:
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow (for basic ML tasks).
• Tasks: Data cleaning, transformation, visualization, and modeling.
• R:
• Libraries: ggplot2, dplyr, tidyr, caret, Shiny (for interactive dashboards).
• Tasks: Statistical analysis, data visualization, and reporting.

Data Processing:

• Cleaning and preprocessing data for analysis.
• Handling large datasets efficiently.
• Identifying trends, correlations, and patterns using descriptive statistics.

Visualization & Reporting:

• Creating clear, impactful visualizations using Python (Matplotlib, Seaborn) or R (ggplot2).
• Building dashboards in Power BI, Tableau, or R Shiny.
• Communicating results to stakeholders through reports or presentations.

Statistical Analysis:

• Hypothesis testing, regression analysis, and ANOVA.
• Time-series forecasting.
• Predictive modeling.

Data Engineering:

• Extracting data from databases using SQL.
• Working with ETL pipelines.
• Ensuring data integrity and security.

Soft Skills:

• Problem-solving mindset.
• Strong communication to translate technical findings into actionable insights.
• Collaboration with cross-functional teams.