The theme of this competition is "Statistical Research in the Era of Big Data and Artificial Intelligence". The participating teams wrote papers on their own topics around the theme.
1. Big data analysis and processing
Research ideas
- Data collection : First identify data sources, such as social media, corporate databases, or public datasets, and use crawler technology or APIs to collect data.
- Data preprocessing : including data cleaning (removing noise and outliers), data conversion (standardization, normalization), missing value processing, etc., to improve data quality.
- Data storage : Choose an appropriate database management system (such as Hadoop, Spark) to store large-scale data sets.
- Data analysis : Apply statistical methods and machine learning algorithms to analyze data and extract valuable information.
- Data visualization : use charts, graphs and other forms to visually display analysis results, such as using Tableau, Power BI and other tools.
2. Application of artificial intelligence in statistics
Research ideas
- Prediction model : Use statistical methods such as regression analysis and time series analysis, combined with machine learning prediction models (such as random forest, neural network), to predict data.
- Classification algorithm : Apply algorithms such as decision trees, support vector machines (SVM), and deep learning to classify data.
- Cluster analysis : Use algorithms such as K-means and hierarchical clustering to group data points to discover the underlying structure of the data.
3. Internet behavior analysis
Research ideas
- User behavior data collection : Obtain user online behavior data through website logs, click stream data, etc.
- User preference analysis : Use methods such as association rule mining and sequence pattern analysis to analyze user interests and behavioral habits.
- Social network analysis : Apply graph theory and network analysis methods to study the relationships between users and community structure, and discover opinion leaders or key nodes.
4. Financial data analysis
Research ideas
- Market trend prediction : Use historical transaction data to predict stock prices, exchange rates, etc. through time series analysis and machine learning models.
- Risk assessment : Use statistical models (such as VaR) and machine learning algorithms (such as neural networks) to conduct quantitative risk analysis and assessment.
- Investment strategy formulation : Combine a variety of analysis methods, such as factor analysis, portfolio optimization, etc., to formulate scientific investment strategies.
5. Public health and epidemiological research
Research ideas
- Data collection and integration : Collect data on disease incidence, transmission speed, and distribution of medical resources.
- Epidemic model construction : Use epidemiological models such as the SIR model to analyze the disease spread process.
- Policy effect evaluation : Use statistical analysis methods to evaluate the effects of public health interventions, such as lockdowns, vaccinations, etc.
6. Intelligent Manufacturing and Industry 4.0
Research ideas
- Production process optimization : Use data analysis and machine learning technology to analyze data in the production process, identify inefficient links, and propose improvement measures.
- Quality Control : Apply statistical process control (SPC) and machine learning algorithms (such as anomaly detection
Testing) to monitor product quality.
- Equipment maintenance prediction : By analyzing historical equipment operation data, predictive maintenance algorithms (such as regression analysis and neural networks) are used to predict equipment failures.
7. Environmental and climate change research
Research ideas
- Data collection : Integrate meteorological station data, satellite remote sensing data and other multi-source data.
- Climate change trend analysis : Apply time series analysis and other methods to study global or regional climate change trends.
- Identification of influencing factors : Analyze the driving factors of climate change through regression analysis, path analysis and other statistical methods.
8. Traffic flow and urban planning
Research ideas
- Traffic data analysis : Collect traffic flow, vehicle speed and other data, and apply time series analysis, spatial data analysis and other methods to study traffic flow changes.
- Traffic model establishment : Build a traffic flow model to analyze the impact of different factors (such as road design, traffic signals) on traffic flow.
- Urban planning suggestions : Combined with the traffic analysis results, suggestions for urban infrastructure improvement, traffic management strategies, etc. are proposed.
When preparing a thesis, each topic selection needs to comprehensively consider theoretical research and practical applications, pay attention to data collection and processing, and use appropriate statistical analysis and machine learning methods to ensure the scientificity and originality of the research. At the same time, the research objectives, methods, results and conclusions should be clearly defined, as well as the practical significance and application prospects of the research.