Business intelligence and data mining with Power BI Course Overview

Target Audience for Business intelligence and data mining with Power BI

1. Introduction:

The Business Intelligence and Data Mining with Power BI course focuses on statistical data analysis, machine learning, and data visualization to equip professionals with advanced data-driven decision-making skills.

2. Job Roles and Audience:

• Data Analysts
• Business Intelligence Analysts
• Data Scientists
• Machine Learning Engineers
• IT Managers
• Database Administrators
• Data Engineers
• Market Researchers
• Financial Analysts
• Operations Managers
• Business Consultants
• Software Developers
• Project Managers
• Healthcare Analysts
• Academic Researchers
• Maritime Transport Analysts
• Marketing Professionals
• Compliance Officers
• Risk Analysts
• Government Data Officers

Learning Objectives - What you will Learn in this Business intelligence and data mining with Power BI?

Course Introduction

This 7-day course on Business Intelligence and Data Mining with Power BI offers comprehensive training in statistical data mining, data processing, and machine learning integrations. Participants will gain hands-on experience with Power BI and other data mining tools to effectively analyze and interpret large datasets.

Learning Objectives and Outcomes

• Understand the Fundamentals of Data Mining and Data Processing

  • Overview and significance of data mining in modern industries.
  • Differences between data mining and data processing techniques.

• Master Data Cleaning and Pre-Processing Techniques

  • Techniques for handling missing data, outliers, and noise using Power BI.
  • Data transformation and normalization processes.

• Perform Exploratory Data Analysis (EDA)

  • Application of descriptive statistics: mean, median, mode, variance, and standard deviation.
  • Data visualization techniques: histograms, box plots, scatter plots, and heatmaps.
  • Identifying patterns and relationships within data.

• Apply Various Data Mining Techniques

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