Al-Data Engineer (SSC/Q8106, V1.0 Level-7, SSC-NASSCOM)

An AI Data Engineer course is designed to equip you with the knowledge and skills required to work with data in the context of artificial intelligence and machine learning projects. As an AI Data Engineer, your role involves collecting, organizing, processing, and preparing data for AI models and algorithms. Here are some key points to consider when looking for an AI Data Engineer course:

1. Data Engineering Fundamentals: The course should cover the fundamental concepts of data engineering, including data collection, data cleaning, data transformation, and data integration. It should provide a solid understanding of databases, data structures, and data manipulation techniques.

2. AI and Machine Learning Basics: A good AI Data Engineer course should also cover the basics of AI and machine learning. This includes understanding different types of machine learning algorithms, model training, evaluation, and deployment. Knowledge of AI and machine learning concepts will help you effectively handle data for AI projects.

3. Data Processing Tools and Technologies: Look for a course that introduces popular data processing tools and technologies used in AI and data engineering. This may include technologies such as Apache Hadoop, Apache Spark, SQL, NoSQL databases, data pipelines, and ETL (Extract, Transform, Load) processes.

4. Distributed Computing: As AI and machine learning projects often involve large datasets, understanding distributed computing is crucial. The course should cover distributed data processing frameworks like Apache Hadoop or Apache Spark, which enable processing data in parallel across multiple nodes or clusters.

5. Data Storage and Management: An AI Data Engineer needs to have knowledge of different data storage and management systems. This includes relational databases like MySQL or PostgreSQL, as well as non-relational databases like MongoDB or Cassandra. Understanding how to choose the right database system for different use cases is important.

6. Data Integration and ETL Processes: The course should cover data integration techniques and ETL (Extract, Transform, Load) processes. This involves combining data from multiple sources, transforming it into a consistent format, and loading it into a target system. Familiarity with ETL tools like Apache NiFi or Talend is valuable.

7. Data Quality and Data Governance: Understanding data quality and data governance principles is essential. The course should cover techniques for ensuring data quality, data cleansing, data validation, and data governance best practices. This helps in maintaining reliable and accurate data for AI projects.

8. Cloud Platforms and Services: Many AI projects are hosted on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Look for a course that introduces cloud-based data storage, data processing, and AI services offered by these platforms.

9. Big Data Analytics: Familiarity with big data analytics concepts is valuable. This includes understanding data exploration, data visualization, and data analysis techniques for extracting insights from large datasets. Tools like Apache Hadoop, Apache Spark, or data analysis libraries like Pandas can be covered in the course.

10. Real-World Projects and Case Studies: Practical hands-on experience is crucial for an AI Data Engineer. Look for a course that provides opportunities to work on real-world projects or case studies. This allows you to apply the concepts you learn and gain practical experience in data engineering for AI.

11. Certification: Check if the course offers a certification upon completion. While certifications may not be mandatory, they can add credibility to your profile and demonstrate your expertise in AI data engineering. Consider courses that provide recognized certifications from reputable organizations or institutions.

12. Industry Relevance: Ensure that the course content is up to date and relevant to the current industry practices. AI and data engineering are rapidly evolving fields, so the course should cover the latest technologies, tools, and trends.

Remember, becoming proficient in AI data engineering requires continuous learning and practical experience. Besides taking a course, consider working on personal projects, participating in data

Course Outline :

IT-ITeS Sector – An Introduction

Future Skills – An Introduction

Artficial Intelligence & Big Data Analytics – An

Introduction

Basics of Architecture

Global Regulations and Standards

Data Tools and Usage

Data Ingestion and Synchronization

Data Transformation and Processing

Manage Your Work to Meet Requirements

Work Effectively with Colleagues

Provide Data / Informaton in Standard Formats

Develope Knowledge, Skills, and Competence

Build and Maintain Relationships at the Workplace

• Course Fees – INR 30,000 (One Time) or INR 10,000 + INR 10,000 + INR 10,000 (Instalments)

• Course Duraton -6 Months

• Eligibility Criteria – Bachelor’s Degree in Engineering /Technology / Statistics / Mathematics / Computer Science

• Job Role – Artificial Intelligence and Big Data Analytics