• Clean and transform data using Power BI.
• Visualize and analyze data using Power BI.
• Understand statistical data mining concepts and techniques.
• Apply data mining techniques including classification, clustering, association rule mining, and anomaly detection.
• Integrate machine learning models into data analysis.
• Analyze patterns and relationships in large datasets.
• Apply data mining techniques to real-world business scenarios.
• Understand ethical practices and data privacy in data mining.

1.0 Introduction to Statistical Data Mining and Processing

• Overview of data mining and its significance in modern industries.
• Fundamental concepts and terminologies.
• Differences between data mining and data processing.

2.0 Statistics in Business Intelligence

• Data cleaning and pre-processing techniques using Power BI Software.
• Handling missing data, outliers and noise using Power BI software.
• Data transformation and normalization using Power BI software.

3.0 Exploratory Data Analysis (EDA) for Big Data using Power BI Software

• Descriptive statistics (mean, median, mode, variance and standard deviation)
• Data visualization techniques (histograms, box plots, scatter plots and heatmaps).
• Identifying patterns and relationships in the data.

4.0 Data Mining Techniques

• Classification (decision trees, random forests, support vector machines and k-nearest neighbors)
• Clustering (k-means, hierarchical clustering and DBSCAN).
• Association rule mining (Apriori and FP-Growth algorithms).
• Anomaly detection techniques.

5.0 Tools and Software for Data Mining

• Introduction to popular data mining tools (R, Python (with libraries like pandas, NumPy, SciPy, scikit-learn) and SAS).
• Hands-on sessions and practical examples using these tools.

6.0 Case Studies and Real-World Applications

• Analyzing case studies from various industries (e.g., finance, Maritime Transport, marketing).
• Practical applications of statistical data mining and processing.
• Lessons learned and best practices.

5 Days

Course Completion Certificate by Network Binary

• Data cleaning and preprocessing using Power BI.
• Handling missing data, outliers, and noise.
• Data transformation and normalization.
• Exploratory data analysis using Power BI.
• Creating descriptive statistical analyses.
• Creating histograms, box plots, scatter plots, and heatmaps.
• Identifying patterns and relationships in datasets.
• Applying classification techniques.
• Applying clustering techniques.
• Performing association rule mining.
• Performing anomaly detection.
• Working with practical examples using R.
• Working with Python data-mining libraries including pandas, NumPy, SciPy, and scikit-learn.
• Applying statistical data-mining techniques to real-world case studies.

• Business intelligence
• Data analysis
• Statistical data mining
• Power BI
• Data visualization
• Machine-learning-assisted analysis

No prerequisites