The objective of this project is to perform data integration and transformation for marketing campaigns using Python and SQL. By leveraging the power of these tools, we aim to streamline the process of aggregating and consolidating data from various sources to create a unified dataset for analysis and campaign optimization.

The scope of the project includes the following key tasks:

Data Source Identification: Identify the relevant data sources for the marketing campaigns, such as customer databases, campaign response logs, website analytics, and social media platforms.

Data Extraction: Extract data from the identified sources using SQL queries, APIs, or appropriate connectors.

Data Cleaning and Preprocessing: Cleanse the extracted data to handle missing values, remove duplicates, and standardize formats. Transform the data as required to ensure compatibility and consistency.

Data Integration: Combine the cleaned and preprocessed data from different sources into a centralized dataset, aligning it based on common attributes or unique identifiers.

To achieve the objectives of the project, we will utilize the capabilities of Python and SQL. Python libraries like Pandas and NumPy will be employed for data cleaning, preprocessing, and transformation tasks. SQL will be used to extract data from relational databases, perform data manipulations, and execute complex queries for data integration

Throughout the project, regular communication and collaboration with the client will be maintained to ensure alignment with their requirements and expectations. Progress updates will be shared at agreed intervals, and any deviations or challenges encountered will be promptly communicated and addressed. The final deliverables will include a comprehensive dataset that integrates and transforms the marketing campaign data, along with documentation outlining the methodology and steps undertaken.