A Data Analyst is a professional who collects, processes, and performs statistical analyses of data. They help organizations make informed decisions by interpreting complex data sets and presenting insights clearly.

Data Analysts commonly use tools such as Excel, Tableau, R, Python, and SQL for data manipulation and visualization.

Structured data is organized and easily searchable, often found in databases (e.g., tables). Unstructured data, on the other hand, is not organized in a predefined manner (e.g., emails, videos, social media posts).

SQL (Structured Query Language) is essential for Data Analysts as it allows them to communicate with databases, retrieve, manipulate, and analyze data efficiently.

Handling missing data can be done through various methods such as imputation, where you replace missing values with statistical measures (mean, median) or deletion of rows/columns with missing values. The method chosen often depends on the context and amount of missing data.

Data normalization is the process of organizing data to reduce redundancy and improve data integrity. This often involves structuring a database in a way that dependencies are properly enforced.

A/B testing is a method of comparing two versions of a webpage or product to determine which one performs better. It helps in making data-driven decisions based on user interactions and preferences.

Data visualization helps in conveying complex data insights in a clear and intuitive manner. Using tools like Tableau or Power BI, analysts can create charts and graphs that simplify data interpretation for stakeholders.

Ensuring data quality involves validation checks, consistency checks, and regular audits. Implementing data governance practices helps maintain the integrity and accuracy of data.

A pivot table is a data summarization tool used in Excel and other data tools. It allows users to reorganize and summarize selected columns and rows to obtain a desired report view.

A primary key uniquely identifies each record in a database table, while a foreign key is a field in one table that uniquely identifies a row of another table, establishing a relationship between the two.

Correlation measures the relationship between two variables, indicating how changes in one variable are associated with changes in another. It ranges from -1 to +1.

A data warehouse is a centralized repository that stores current and historical data from various sources for analysis and reporting.

Common data cleaning techniques include removing duplicates, handling missing values, filtering out outliers, and standardizing formats to ensure data consistency.

Prioritizing tasks can be achieved by assessing project deadlines, stakeholder needs, and project impact. Using tools like Trello or Asana can help in managing and tracking progress effectively.

Statistical analysis provides techniques for collecting, reviewing, and interpreting data to identify trends and patterns. It underpins the decision-making process in data-driven organizations.

Big Data refers to large and complex data sets that traditional data processing applications cannot manage efficiently. It encompasses volume, velocity, and variety.

Data storytelling combines data analysis and narrative to communicate findings effectively. It helps in engaging stakeholders and making data insights relatable and actionable.

As a Data Analyst, I have worked with machine learning algorithms such as linear regression, decision trees, and clustering to identify patterns and predict future trends based on historical data.

I follow reputable data analytics blogs, attend webinars, and participate in online courses to stay updated with the latest tools, techniques, and best practices in the field.

In a previous role, I worked on a project where I had to integrate data from multiple sources with varying formats. I tackled the challenge by utilizing data cleaning techniques and ensuring data consistency across datasets.

Documentation is crucial as it provides a clear record of data sources, methodologies, and findings. It aids in reproducibility and helps new team members understand previous work.

My approach to EDA includes using visualizations to identify trends, checking for missing values, understanding data distributions, and performing summary statistics to gather initial insights.

I have experience using time series analysis techniques for forecasting, which involves analyzing historical data to predict future outcomes. Tools like R and Python have been instrumental in this process.

I am passionate about transforming data into actionable insights that drive business decisions. The ability to solve problems and contribute to an organization's success through data analysis excites me.