Sana Sana

Data Management and Optimization:

• Liaised with vendors to secure cost-effective solutions.
• Collaborated with cross-functional teams for project success.
• Partnered with users to understand and define system requirements.
• Consulted with users and management to formulate guiding principles.
• plays a crucial role in interpreting and analysing data to help organizations make informed decisions. The primary responsibilities of a data analyst typically
• Data Collection and Cleaning:
• Gathering data from various sources (internal databases, external APIs, spreadsheets, etc.).
• Cleaning and pre-processing data to ensure accuracy, consistency, and completeness.
• Handling missing or inconsistent data through techniques like imputation, removal, or transformation.
• Applying statistical techniques and methods to interpret data and identify patterns, trends, or anomalies.
• Using tools like Excel, SQL, Python (with libraries such as Pandas and NumPy).
• Performing exploratory data analysis (EDA) to understand the dataset and its key characteristics.
• Creating clear and insightful visualizations (graphs, charts, dashboards) to represent findings.
• Using tools like Power BI, or Python libraries (Matplotlib, Seaborn) to create visuals.
• Summarizing insights and presenting findings to stakeholders in an understandable format, such as reports or presentations.
• Translating data findings into actionable business insights and strategic recommendations.
• Collaborating with different departments (marketing, finance, operations) to help drive business decisions based on data.
• Maintaining and updating databases to ensure data remains relevant and easily accessible.
• Automating repetitive tasks using scripts and tools to improve efficiency.
• Technical Skills: SQL, Excel, Python, Data visualization tools ( Power BI).
• Statistical Knowledge: Understanding of statistical methods and how to apply them to real-world data.
• Communication Skills: Ability to explain complex data findings in simple terms for non-technical stakeholders.
• SQL for querying databases.
• Excel for data manipulation and analysis.
• Python (with libraries like Pandas, NumPy, Matplotlib, Seaborn).
• Power BI for creating dashboards and visualizations.

Data analysts are i

• Prepared proposals outlining requirements, potential solutions and required resources, timelines and costs to achieve objectives.

• Coordinate with different department
• Monitored project timelines to avoid delays.
• Coordinated project tasks, ensuring timely completion.
• Managed communication amongst project team members to foster collaboration.
• Worked closely with the Project Manager to define objectives, leading to clear guidelines.
• Responded promptly to client queries, boosting customer satisfaction levels.
• Assisted in developing detailed project plans, resulting in clearer direction.