รายละเอียดงาน1. Data Collection and Cleansing: Collect data from various sources, which may be databases, log files, questionnaires, etc. Before analysis, the data needs to be cleaned and preprocessed to remove invalid data and fill in missing values.
2. Data Analysis and Modeling: Data is analyzed and modeled to discover the patterns, associations and trends behind the data. This might include descriptive statistics, regression analysis, clustering, classification, etc.
3. Reporting and Visualization: Turn analysis results into easy-to-understand reports and visualizations to help non-technical people understand the meaning of the data and make decisions. Commonly used tools include data visualization software such as Power BI.
3. Business Insights: Connect data analysis results with business issues, provide insights into business operations, market trends, customer behavior, etc., and provide support for business decisions.
4. Forecasting and Recommendations: Based on historical data and analysis results, forecasting models are developed to provide forecasts for future trends. In addition, specific suggestions are put forward based on the analysis results to help enterprises optimize processes and improve efficiency.
5. Data Quality Maintenance: Monitor data quality to ensure data accuracy, completeness and consistency. Identify and resolve data quality issues in a timely manner.
6. Collaboration and Communication: Communicate effectively with different members inside and outside the team to understand their needs and communicate the results of the analysis to relevant personnel.
7. Technology updates and learning: Keep an eye on the latest technologies and trends in the field of data analysis, and keep learning and upgrading your skills to maintain competitiveness in the ever-evolving field.