Data Analyst Interview Question: Manipulating Large Datasets

During a previous project, I encountered a large dataset containing customer transaction records that needed analysis to identify purchasing patterns and trends. To derive meaningful insights, I followed a systematic approach using various tools and techniques.

1. Data Cleaning: The first step involved cleaning the dataset to remove duplicates, inconsistencies, and missing values. I used Python programming language along with libraries like Pandas for efficient data cleaning.
2. Data Exploration: Next, I performed exploratory data analysis to understand the distribution of variables and relationships within the dataset. Visualization tools such as Matplotlib and Seaborn were crucial in this stage.
3. Data Transformation: I transformed the data by aggregating transaction records, calculating key metrics like average purchase value and frequency, and creating new features for deeper analysis. SQL queries were used for data transformation and aggregation.
4. Data Analysis: With the cleaned and transformed data, I conducted statistical analysis and machine learning techniques to uncover patterns and trends. Tools like R and Jupyter Notebooks were instrumental in running statistical models and algorithms.
5. Insights Generation: Finally, I interpreted the analytical results to derive actionable insights for business stakeholders. Visualization techniques like dashboards using Tableau were used to present the findings effectively.

Through this process, I was able to extract valuable insights from the large dataset, enabling informed decision-making and strategic planning based on data-driven evidence.