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Category
Artificial Intelligence
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Course Price
Free
AI - Data Quality Analyst
AI Data Quality Analysts play a crucial role in the artificial intelligence lifecycle by ensuring the integrity, accuracy, and reliability of data used to train AI models. These professionals are responsible for inspecting, cleaning, and validating datasets to eliminate biases, inconsistencies, and errors that could compromise AI system performance. They implement quality control processes, establish data standards, and work closely with data scientists to optimize datasets for specific AI applications.
As AI continues to transform industries from healthcare to finance, the demand for skilled Data Quality Analysts has grown exponentially. These specialists bridge the gap between raw data collection and effective AI implementation, ensuring that AI systems make decisions based on high-quality information. Their expertise in data validation methodologies, statistical analysis, and domain knowledge helps organizations build trustworthy AI solutions that deliver accurate results and comply with ethical guidelines and regulatory requirements.
Course Details
AI Data Quality Analysts play a crucial role in the artificial intelligence lifecycle by ensuring the integrity, accuracy, and reliability of data used to train AI models. These professionals are responsible for inspecting, cleaning, and validating datasets to eliminate biases, inconsistencies, and errors that could compromise AI system performance. They implement quality control processes, establish data standards, and work closely with data scientists to optimize datasets for specific AI applications.
As AI continues to transform industries from healthcare to finance, the demand for skilled Data Quality Analysts has grown exponentially. These specialists bridge the gap between raw data collection and effective AI implementation, ensuring that AI systems make decisions based on high-quality information. Their expertise in data validation methodologies, statistical analysis, and domain knowledge helps organizations build trustworthy
Course Information
- Class Start: March 20, 2025
- Course Duration: 3 Months
- QP Code: SSC/Q0503
- Student Capacity: Max 30 Students
- Class Schedule: Monday - Saturday
- Class Time: 9.00 am - 4.00 pm
- Course Teachers: 01
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AI - Data Quality Analyst Curriculum
This is a model curriculum for the AI - Data Quality Analyst QP Code: SSC/Q8101, Version 2.0.
Module 1: Artificial Intelligence & Big Data Analytics - An Introduction
This module serves as a bridge, introducing the fundamentals of AI and Big Data Analytics.
Theory - Key Learning Outcomes:
- Explain the relevance of AI & Big Data Analytics for society and various industries.
- Define "general" and "narrow" AI and describe fields like image processing, computer vision, robotics, and NLP.
Practical - Key Learning Outcomes:
- Outline a career map for roles in AI & Big Data Analytics.
- Analyze the differences between key terms such as Supervised Learning, Unsupervised Learning, and Deep Learning.
Module 2: Basic Statistical Concepts
This bridge module covers basic statistical concepts and their application.
Theory - Key Learning Outcomes:
- Distinguish between various probability distributions (Normal, Poisson, Exponential, Bernoulli).
- Identify correlation between variables using graphical techniques like scatterplots.
Practical - Key Learning Outcomes:
- Apply descriptive statistics, including measures of central tendency (mean, median, mode).
- Apply different correlation and regression techniques.
- Use hypothesis testing to draw inferences and measure statistical significance.
Module 3: Statistical Tools and Usage
This bridge module assesses the use of statistical tools and packages.
Theory - Key Learning Outcomes:
- Explain the basics of using statistical software packages and IDEs like RStudio and Jupyter Notebooks.
Practical - Key Learning Outcomes:
- Apply basic functions and libraries present in statistical software packages and IDEs.
- Use statistical packages, frameworks, and libraries such as NumPy and Pandas for developing applications.
Module 4: Importing Data
This module focuses on the process of importing data.
Theory - Key Learning Outcomes:
- Identify data type, volume, and variables required for analysis.
- Discuss various data sources, the purpose of metadata, and data validation tools.
Practical - Key Learning Outcomes:
- Demonstrate capturing and importing data from internal and external sources.
- Organize and map metadata as required for analysis and perform data profiling.
Module 5: Pre-processing Data
This module covers the fundamentals and techniques of data pre-processing.
Theory - Key Learning Outcomes:
- Differentiate between unprocessed and processed data.
- Explain the impact of unprocessed data and describe anomalies like missing values, incorrect data types, and redundant data.
Practical - Key Learning Outcomes:
- Analyze unprocessed data to find anomalies.
- Apply different cleaning techniques, normalize datasets, and validate pre-processed data with appropriate tools.
Module 6: Manage and plan work requirements
This module focuses on planning and managing work requirements effectively.
Theory - Key Learning Outcomes:
- Discuss the role and responsibilities, prioritizing work, organizational policies, and the importance of completing work accurately.
Practical - Key Learning Outcomes:
- Analyze work needs and requirements and apply resource and time management principles.
- Demonstrate how to maintain an organized work area.
Module 7: Communication and collaboration with colleagues
This module teaches effective communication and collaboration skills.
Theory - Key Learning Outcomes:
- Explain principles of clear communication and the importance of listening and adhering to commitments.
- Identify challenges related to teamwork and the importance of sharing workloads.
Practical - Key Learning Outcomes:
- Demonstrate effective oral, written, and non-verbal communication skills and professional behavior.
- Demonstrate effective team mentorship.
Module 8: Workplace data management
This module describes how to manage data and information effectively in the workplace.
Theory - Key Learning Outcomes:
- Discuss data privacy, the importance of providing accurate and timely information, and various data types.
Practical - Key Learning Outcomes:
- Demonstrate rule-based analysis and data formatting.
- Identify data anomalies and apply confidentiality guidelines.
- Use CRM databases to record and extract information.
Module 9: Relationship management at the workplace
This module applies approaches for building and maintaining professional relationships.
Theory - Key Learning Outcomes:
- Describe ways to build new professional relationships and the importance of workplace ethics.
- Discuss the qualities of a supportive team player and strategies to build rapport.
Practical - Key Learning Outcomes:
- Apply different conflict management and resolution approaches.
- Demonstrate methods to build healthy relationships across business units.
Module 10: Client relationship management
This module defines and describes techniques for managing client relationships.
Theory - Key Learning Outcomes:
- Discuss how to handle client requirements and the importance of timely communication and responses.
- Explain the importance of client deliverables management and working on client feedback.
Practical - Key Learning Outcomes:
- Demonstrate methods for gathering client requirements and managing their expectations.
- Demonstrate effective communication and good working relationships with clients.
Module 11: Inclusive and environmentally sustainable workplaces
This module illustrates sustainable practices and promotes an inclusive workplace.
Theory - Key Learning Outcomes:
- Describe approaches for efficient energy use and waste management.
- Discuss the importance of diversity policies and how to identify and overcome stereotypes related to people with disabilities.
Practical - Key Learning Outcomes:
- Practice the segregation of recyclable, non-recyclable, and hazardous waste.
- Demonstrate methods for energy conservation and communication that aligns with gender inclusiveness and PwD sensitivity.