Sohan Miryalkar

Cricket Data Analytics Using Python and PowerBI

This project aimed to leverage the power of Python for data manipulation and Power BI for data visualization to gain insights from cricket data. Here's a breakdown of the process:

• Data Acquisition:
  • Source Identification: You identified a source for cricket data. This could be public APIs provided by sports organizations, websites like ESPN Cricinfo, or downloaded datasets in CSV format.
  • Data Extraction: Using Python libraries like requests or BeautifulSoup (for web scraping), you retrieved the desired cricket data.
• Data Analysis with Python:
  • Using libraries like pandas, NumPy, or scikit-learn, you could have performed exploratory data analysis (EDA) to understand trends, patterns, and relationships within the cricket data. This might involve calculating descriptive statistics, identifying correlations between variables, and creating basic visualizations (e.g., histograms, scatter plots) directly in Python.
• Data Visualization with Power BI:
  • You imported the preprocessed data from Python into Power BI Desktop.
  • Data Modeling: You established relationships between different data tables within Power BI to create a cohesive data model for analysis.
  • Report Creation: You designed interactive reports using Power BI's functionalities. This could involve creating various visualizations like bar charts, line charts, pie charts, maps (if location data was included), and custom visuals to explore different aspects of the cricket data.
  • Insights and Storytelling You used the visualizations to identify interesting insights from the data (e.g., top performing players, most successful teams on specific grounds, impact of weather conditions on scoring rates) and presented them in a clear and concise manner through storytelling techniques.
• Project Deliverables:
  • This project likely resulted in a set of interactive Power BI reports showcasing the insights derived from the cricket data analysis.
  • Optionally, you might have included the Python code used for data preprocessing and any basic analysis you performed.