mail:- info@naukripay.com

data analyst collects, cleans, and interprets raw data to uncover trends, patterns, and insights, which are then used to inform data-driven decision-making and solve business problems. Key responsibilities include data mining, data visualization through reports and dashboards, statistical analysis, and collaborating with various teams to understand needs and guide strategy. Essential skills for this role involve technical proficiency with tools like SQL and Excel, strong analytical thinking, and effective communication to present complex findings to both technical and non-technical audiences.

Key Responsibilities

Data Collection & Cleaning:

Gather data from diverse sources, verify its accuracy, and clean it by removing duplicates and errors to ensure high quality.

Data Analysis & Interpretation:

Apply statistical techniques and analytical methods to identify trends, patterns, and insights within datasets.

Reporting & Visualization:

Create clear reports, dashboards, and visualizations (like charts and tables) to make complex data easily understandable for stakeholders.

Problem-Solving:

Provide data-driven insights that help businesses improve efficiency, pinpoint underperforming areas, and make informed strategic choices.

Collaboration:

Work with technical and non-technical teams to understand business questions, determine key metrics, and present findings effectively.

Essential Skills

Technical Proficiency:

Skills in programming languages (like SQL), spreadsheet software (like Excel), and data visualization tools (like Tableau).

Analytical Thinking:

The ability to process large datasets, analyze them critically, and draw meaningful conclusions.

Communication:

Strong skills in both written and verbal communication to explain technical findings and their implications to diverse audiences.

Business Acumen:

Understanding of the business context to connect data insights with strategic goals and make relevant recommendations.

Common Tools Used

SQL: For querying and managing databases.

Excel: For data cleaning, organization, and basic analysis.

Tableau/BI Tools: For creating interactive dashboards and visualizations.

Python/R: For more advanced statistical analysis and data manipulation

Data analysist Male( Jalandhar)