DATA MINING AND ANALYSIS FOR AUDITORS

NAME OF THE COURSE
DATA MINING AND ANALYSIS FOR AUDITORS
 
CERTIFICATION
DATA MINING AND ANALYSIS FOR AUDITORS
 
COURSE OVERVIEW
Various projects often require a significant investment of time and resources, therefore stakeholders need to carefully look into their contractual responsibilities to ensure minimum disruption to their ventures. Key parties need to identify the potential risks and explore mitigation solutions prior to the start of their projects. As part of continuity planning, key parties are required to work through effective risk management policies in managing contracts.
 
TRAINING DURATION
Total Training Hours : 22 Hours
Training Duration      : 1 Week
Total Training  Days  : 4-5 Working Days
 
TRAINING SCHEDULE
Weekdays  
Regular Sessions : 4 – 6 Hrs Per day (9am to 2pm or 3.00pm to 9.00 pm)
Food & refreshments Included
Weekends (Friday & Saturday)
Fast Track Sessions: 8 Hours per day (9am to 5pm)
Food & refreshments Included
 
CERTIFICATION
Globally recognized certificate from “Kings Global Career Academy”
 
TEST
No
 
LEARNING AIDS
Yes
 
COURSE MATERIAL
Hard & Soft Copies of Study Material
 
LANGUAGE OF INSTRUCTION
English
 
INSTRUCTOR HELPLINE
Yes
1. Email
2. Social Media (For Emergency requirements)
 
REGISTRATION REQUIREMENTS
1. Passport Copy
2. Curriculum Vitae
3. Passport size photographs
4. Course Fee
 
MODE OF PAYMENT
Cash / Cheque / Credit Card / Bank Transfer.
 
ELIGIBILITY CRITERIA
Open all Audit officers
 
COURSE BENEFITS
It is helpful to predict future trends
It signifies customer habits
Helps in decision making
Increase company revenue5. It depends upon the market-based analysis
Quick fraud detection
 
COURSE CONTENTS
Day – 1
  • Definition of Data Mining
  • Potential Financial Benefits of Using Data Mining Techniques
  • Rules for Data Extraction
  • Initiating Data Mining Methodology
  • Data Mining Using Statistical Modeling
  • Standard Data Analysis
  • What is Data Mining?
  • What Kinds of Data Can Be Mined?
  • Data Objects and Attribute Types
  • Data Visualisation
  • Measuring Data Similarity and Dissimilarity
  • Data Cleaning and Data Integration
  • Data Reduction
  • Data Transformation and Data Discretization
  • Basic Concepts of Data Warehousing
  • Data Cube and OLAP
 
Day – 2
Design, Usage, and Implementation of Data Warehouse
  • Flowcharting the process.
  • Choosing and extracting the data.
  • Understanding the population.
  • Understanding the fields with descriptive statistics.
  • Exploratory data analysis.
  • Choice of analytic methods and alternative approaches.
  • Confirmatory data analysis and finding outliers.
  • Evaluating results evaluation and integrating with traditional findings.
  • Automatic Data Review 
  • Selective activation of Audit Process
  • Detailed Data Examination
  • Evaluation of Control Processes
 
Day – 3
Pattern Mining in Multilevel and Multidimensional Space
  • Constraint-Based Frequent Pattern Mining
  • Mining High-Dimensional Data and Colossal Patterns
  • Mining Compressed or Approximate Patterns
  • Pattern Exploration and Application
  • Text Mining
  • Introduction to Audit Analytics:
  • Special Topics in Audit Analytics:
  • Extracting value from data
  • Data analytics for auditing
  • Statistical methods and techniques for data analysis
  • Data-driven audit – case studies & field experiences
  • IT methods and techniques for data analysis
  • Data mining and analytics – implications for the audit profession
 
Day – 4
IMPLEMENT DATA ANALYTICS FOR PROJECTS
  • Project planning
  • Data extraction
  • Data analyses
  • Data visualization
  • What is Cluster Analysis?
  • Partitioning and Hierarchical Methods
  • Density-Based and Grid-Based Methods
  • Advanced Cluster Analysis
  • Probabilistic Model-Based Clustering
  • Clustering High-Dimensional and Graph Data
  • Clustering with Constraints
  • Outlier Analysis
  • Outlier Detection Methods
  • Statistical and Proximity-Based Approaches
  • Clustering-Based and Classification-Based Approaches
  • Outlier Detection in High-Dimensional Data
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